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1.
  • Ranisch, Robert, et al. (author)
  • Ethics of digital contact tracing apps for the Covid-19 pandemic response
  • 2020
  • Reports (other academic/artistic)abstract
    • There is a growing interest in contact tracing apps (CT apps) for pandemic man- agement. These apps raise significant moral concerns. It is therefore crucial to consider ethical requirements before and while implementing such apps. Public trust is of major importance for population uptake of contact tracing apps. Hasty, ill-prepared or badly communicated implementations of CT apps will likely under- mine public trust, and as such, risk impeding general effectiveness. In response to these demands, to meet ethical requirements and find a basis for justified trust, this background introduces an ethical framework for a responsible design and implementation of CT apps. However, even prudently chosen measures of digital contact tracing carry moral costs, which makes it necessary address different trade-offs. This background paper aims to inform developers, researchers and decision-makers be- fore and throughout the process of implementing contact tracing apps.
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2.
  • Petersson, Jesper, 1974, et al. (author)
  • Off the record: The invisibility work of doctors in a patient-accessible electronic health record information service.
  • 2021
  • In: Sociology of health & illness. - : Wiley. - 1467-9566 .- 0141-9889. ; 43:5, s. 1270-1285
  • Journal article (peer-reviewed)abstract
    • In this article, we draw on Michael Lipsky's work on street-level bureaucrats and discretion to analyse a real case setting comprising an interview study of 30 Swedish doctors regarding their experiences of changes in clinical work following patients being given access to medical records information online. We introduce the notion of invisibility work to capture how doctors exercise discretion to preserve the invisibility of their work, in contrast to the well-established notion of invisible work, which denotes work made invisible by parties other than those performing it. We discuss three main forms of invisibility work in relation to records: omitting information, cryptic writing and parallel note writing. We argue that invisibility work is a way for doctors to resolve professional tensions arising from the political decision to provide patients with online access to record information. Although invisibility work is understood by doctors as a solution to government-initiated visibility, we highlight how it can create difficulties for doctors concerning accountability towards patients, peers and authorities.
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3.
  • Johansson, Martin L, et al. (author)
  • Non-invasive sampling procedure revealing the molecular events at different abutments of bone-anchored hearing systems–A prospective clinical pilot study
  • 2022
  • In: Frontiers in Neuroscience. - : Frontiers Media SA. - 1662-4548 .- 1662-453X. ; 16
  • Journal article (peer-reviewed)abstract
    • Purpose: To investigate the molecular activities in different compartments around the bone-anchored hearing system (BAHS) with either electropolished or machined abutments and to correlate these activities with clinical and microbiological findings. Materials and methods: Twelve patients received machined or electropolished abutments after implant installation of BAHS. Peri-abutment fluid and tissue were collected from baseline to 12 months. Gene expression of cytokines and factors related to tissue healing and inflammation, regeneration and remodelling, as well as bacterial recognition were determined using quantitative-polymerase chain reaction (qPCR). The clinical status was evaluated using the Holgers scoring system, and bacterial colonisation was investigated by culturing. Results: The gene expression of inflammatory cytokines (IL-8, IL-1β, and IL-10) and bacteria-related Toll-like receptors (2 and 4) was higher in the peri-abutment fluid than at baseline and in the peri-abutment tissue at 3 and 12 months. Conversely, the expression of genes related to tissue regeneration (Coll1a1 and FOXO1) was higher in the tissue samples than in the peri-abutment fluid at 3 and 12 months. Electropolished abutments triggered higher expression of inflammatory cytokines (IL-8 and IL-1β) (in peri-abutment fluid) and regeneration factor FOXO1 (in peri-abutment tissue) than machined abutments. Several cytokine genes in the peri-abutment fluid correlated positively with the detection of aerobes, anaerobes and Staphylococcus species, as well as with high Holger scores. Conclusion: This study provides unprecedented molecular information on the biological processes of BAHS. Despite being apparently healed, the peri-abutment fluid harbours prolonged inflammatory activity in conjunction with the presence of different bacterial species. An electropolished abutment surface appears to be associated with stronger proinflammatory activity than that with a machined surface. The analysis of the peri-abutment fluid deserves further verification as a non-invasive sampling and diagnostic procedure of BAHS.
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4.
  • Ge, Chenjie, 1991, et al. (author)
  • Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
  • 2020
  • In: IEEE Access. - 2169-3536 .- 2169-3536. ; 8:1, s. 22560-22570
  • Journal article (peer-reviewed)abstract
    • This paper addresses issues of brain tumor subtype classification using Magnetic Resonance Images (MRIs) from different scanner modalities like T1 weighted, T1 weighted with contrast-enhanced, T2 weighted and FLAIR images. Currently most available glioma datasets are relatively moderate in size, and often accompanied with incomplete MRIs in different modalities. To tackle the commonly encountered problems of insufficiently large brain tumor datasets and incomplete modality of image for deep learning, we propose to add augmented brain MR images to enlarge the training dataset by employing a pairwise Generative Adversarial Network (GAN) model. The pairwise GAN is able to generate synthetic MRIs across different modalities. To achieve the patient-level diagnostic result, we propose a post-processing strategy to combine the slice-level glioma subtype classification results by majority voting. A two-stage course-to-fine training strategy is proposed to learn the glioma feature using GAN-augmented MRIs followed by real MRIs. To evaluate the effectiveness of the proposed scheme, experiments have been conducted on a brain tumor dataset for classifying glioma molecular subtypes: isocitrate dehydrogenase 1 (IDH1) mutation and IDH1 wild-type. Our results on the dataset have shown good performance (with test accuracy 88.82%). Comparisons with several state-of-the-art methods are also included.
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5.
  • Robinson, Yohan, 1977, et al. (author)
  • AI och framtidens försvarsmedicin
  • 2020
  • Reports (other academic/artistic)abstract
    • Medicinskt legitimerad personal är, och kommer med stor sannolikhet fortsattatt vara, en knapp resurs inom Försvarsmaktens sjukvårdsorganisation. I denna rapport ges en översikt över pågående och planerade ansatser baserade påartificiell intelligens (AI) inom akutsjukvård med särskild tonvikt på omhändertagandet av traumapatienter, där lösningarna skulle kunna bidra till att Försvarsmakten kan bibehålla sin sjukvårdskapacitet i kritiska lägen. Rapporten är ett resultat av samarbetet mellan FM, FOI, FMV, FHS och KI, och vänder sig i första hand till Försvarsmaktens strategiska ledning.Användningen av AI-teknik i framtida beslutsstöd kan skapa nya möjligheter till avlastning av personal och resurseffektivisering. Tekniken ger möjligheter att i realtid samla in, bearbeta och analysera stora mängder blandadinformation om förbands hälsoläge och fysiska stridsvärde. Bedömning av skadade kan t.ex. göras av triagedrönare och den efterföljande evakueringen kanunderlättas av intelligenta autonoma plattformar. Införandet av AI-system ställer dock vårdgivaren inför svåra etiska och medikolegala överväganden.Försvarsmedicin har en central roll i Försvarsmaktens krigföringsförmåga och för samhällets uthållighet. För att nyttja hela AI-teknikens framfart till Försvarsmaktens nytta måste dess innebörd och konsekvens för försvarsmedicinen förstås. Därför rekommenderar denna studie att Försvarsmaktens framtida satsningar inom AI och autonomi inkluderar den försvarsmedicinska teknikutveckling som är beskriven i denna rapport.
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6.
  • Abbaspour, S., et al. (author)
  • Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements
  • 2021
  • In: Sensors. - : MDPI AG. - 1424-8220. ; 21:16
  • Journal article (peer-reviewed)abstract
    • Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.
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7.
  • Hellstrand Tang, Ulla, et al. (author)
  • Exploring the Role of Complexity in Health Care Technology Bottom-Up Innovations : Multiple-Case Study Using the Nonadoption, Abandonment, Scale-Up, Spread, and Sustainability Complexity Assessment Tool
  • 2024
  • In: JMIR Human Factors. - : JMIR Publications. - 2292-9495. ; 11:1
  • Journal article (peer-reviewed)abstract
    • BACKGROUND: New digital technology presents new challenges to health care on multiple levels. There are calls for further research that considers the complex factors related to digital innovations in complex health care settings to bridge the gap when moving from linear, logistic research to embracing and testing the concept of complexity. The nonadoption, abandonment, scale-up, spread, and sustainability (NASSS) framework was developed to help study complexity in digital innovations.OBJECTIVE: This study aims to investigate the role of complexity in the development and deployment of innovations by retrospectively assessing challenges to 4 digital health care innovations initiated from the bottom up.METHODS: A multicase retrospective, deductive, and explorative analysis using the NASSS complexity assessment tool LONG was conducted. In total, 4 bottom-up innovations developed in Region Västra Götaland in Sweden were explored and compared to identify unique and shared complexity-related challenges.RESULTS: The analysis resulted in joint insights and individual learning. Overall, the complexity was mostly found outside the actual innovation; more specifically, it related to the organization's readiness to integrate new innovations, how to manage and maintain innovations, and how to finance them. The NASSS framework sheds light on various perspectives that can either facilitate or hinder the adoption, scale-up, and spread of technological innovations. In the domain of condition or diagnosis, a well-informed understanding of the complexity related to the condition or illness (diabetes, cancer, bipolar disorders, and schizophrenia disorders) is of great importance for the innovation. The value proposition needs to be clearly described early to enable an understanding of costs and outcomes. The questions in the NASSS complexity assessment tool LONG were sometimes difficult to comprehend, not only from a language perspective but also due to a lack of understanding of the surrounding organization's system and its setting.CONCLUSIONS: Even when bottom-up innovations arise within the same support organization, the complexity can vary based on the developmental phase and the unique characteristics of each project. Identifying, defining, and understanding complexity may not solve the issues but substantially improves the prospects for successful deployment. Successful innovation within complex organizations necessitates an adaptive leadership and structures to surmount cultural resistance and organizational impediments. A rigid, linear, and stepwise approach risks disregarding interconnected variables and dependencies, leading to suboptimal outcomes. Success lies in embracing the complexity with its uncertainty, nurturing creativity, and adopting a nonlinear methodology that accommodates the iterative nature of innovation processes within complex organizations.
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8.
  • Pfeiffer, Christoph, 1989, et al. (author)
  • On-scalp MEG sensor localization using magnetic dipole-like coils: A method for highly accurate co-registration
  • 2020
  • In: Neuroimage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 212
  • Journal article (peer-reviewed)abstract
    • Source modelling in magnetoencephalography (MEG) requires precise co-registration of the sensor array and the anatomical structure of the measured individual's head. In conventional MEG, the positions and orientations of the sensors relative to each other are fixed and known beforehand, requiring only localization of the head relative to the sensor array. Since the sensors in on-scalp MEG are positioned on the scalp, locations of the individual sensors depend on the subject's head shape and size. The positions and orientations of on-scalp sensors must therefore be measured a every recording. This can be achieved by inverting conventional head localization, localizing the sensors relative to the head - rather than the other way around. In this study we present a practical method for localizing sensors using magnetic dipole-like coils attached to the subject's head. We implement and evaluate the method in a set of on-scalp MEG recordings using a 7-channel on-scalp MEG system based on high critical temperature superconducting quantum interference devices (high-T-c SQUIDs). The method allows individually localizing the sensor positions, orientations, and responsivities with high accuracy using only a short averaging time (<= 2 mm, < 3 degrees and < 3%, respectively, with 1-s averaging), enabling continuous sensor localization. Calibrating and jointly localizing the sensor array can further improve the accuracy of position and orientation (< 1 mm and < 1 degrees, respectively, with 1-s coil recordings). We demonstrate source localization of on-scalp recorded somatosensory evoked activity based on coregistration with our method. Equivalent current dipole fits of the evoked responses corresponded well (within 4.2 mm) with those based on a commercial, whole-head MEG system.
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10.
  • Insulander Björk, Klara, 1982, et al. (author)
  • Experimental determination of concentration factors of Mn, Zn and I in the phytoplankton species Phaeodactylum Tricornutum
  • 2023
  • In: Journal of Environmental Radioactivity. - : Elsevier BV. - 0265-931X .- 1879-1700. ; 261
  • Journal article (peer-reviewed)abstract
    • Anthropogenic radionuclides released into the environment cause a radiation dose to wildlife and humans which must be quantified, both to assess the effect of normal releases, and to predict the consequences of a larger, unplanned release. To estimate the spread of the radioactive elements, the ecosystem around release points is modelled, and element uptake is usually quantified by concentration factors (CF), which relates the concentration of an element in an organism to the concentration of the same element in a medium under equilibrium conditions. In this work, we experimentally determine some phytoplankton CF that are needed for improved modelling of the marine ecosystems around nuclear facilities and release points. CFs that require better determination have been identified through literature search. Sensitivity studies, using the currently used ecosystem modelling software PREDO, show that for most studied groups, the dose committed by the respective radionuclides is almost proportional to the corresponding phytoplankton CFs. In the present work, CFs are determined through laboratory experiments with cultured phytoplankton and radionuclides of the concerned elements, assessing the element uptake by the phytoplankton through detection of the emitted radiation. The three CF assessed in this work were those for manganese, zinc and iodine in phytoplankton. Conservative estimates of these CF based on the present data are 40 000 L/kg for manganese, 50 000 L/kg for zinc and 180 L/kg for iodine with the phytoplankton masses referring to their dry weight.
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11.
  • Hagberg, Eva, et al. (author)
  • Semi-supervised learning with natural language processing for right ventricle classification in echocardiography—a scalable approach
  • 2022
  • In: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 143
  • Journal article (peer-reviewed)abstract
    • We created a deep learning model, trained on text classified by natural language processing (NLP), to assess right ventricular (RV) size and function from echocardiographic images. We included 12,684 examinations with corresponding written reports for text classification. After manual annotation of 1489 reports, we trained an NLP model to classify the remaining 10,651 reports. A view classifier was developed to select the 4-chamber or RV-focused view from an echocardiographic examination (n = 539). The final models were two image classification models trained on the predicted labels from the combined manual annotation and NLP models and the corresponding echocardiographic view to assess RV function (training set n = 11,008) and size (training set n = 9951. The text classifier identified impaired RV function with 99% sensitivity and 98% specificity and RV enlargement with 98% sensitivity and 98% specificity. The view classification model identified the 4-chamber view with 92% accuracy and the RV-focused view with 73% accuracy. The image classification models identified impaired RV function with 93% sensitivity and 72% specificity and an enlarged RV with 80% sensitivity and 85% specificity; agreement with the written reports was substantial (both κ = 0.65). Our findings show that models for automatic image assessment can be trained to classify RV size and function by using model-annotated data from written echocardiography reports. This pipeline for auto-annotation of the echocardiographic images, using a NLP model with medical reports as input, can be used to train an image-assessment model without manual annotation of images and enables fast and inexpensive expansion of the training dataset when needed. © 2022
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12.
  • Ortiz Catalan, Max Jair, 1982, et al. (author)
  • Chronic Use of a Sensitized Bionic Hand Does Not Remap the Sense of Touch
  • 2020
  • In: Cell Reports. - : Elsevier BV. - 2211-1247. ; 33:12
  • Journal article (peer-reviewed)abstract
    • Electrical stimulation of tactile nerve fibers can be used to restore touch through a bionic hand. Ortiz-Catalan et al. show that a mismatch between the location of the sensor on the bionic hand and the tactile experience is not resolved after long-term prosthesis use.
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13.
  • Fan, Xuelong, et al. (author)
  • Effects of sensor types and angular velocity computational methods in field measurements of occupational upper arm and trunk postures and movements
  • 2021
  • In: Sensors. - : MDPI AG. - 1424-8220. ; 21:16
  • Journal article (peer-reviewed)abstract
    • Accelerometer-based inclinometers have dominated kinematic measurements in previous field studies, while the use of inertial measurement units that additionally include gyroscopes is rapidly increasing. Recent laboratory studies suggest that these two sensor types and the two commonly used angular velocity computational methods may produce substantially different results. The aim of this study was, therefore, to evaluate the effects of sensor types and angular velocity computational methods on the measures of work postures and movements in a real occupational setting. Half-workday recordings of arm and trunk postures, and movements from 38 warehouse workers were compared using two sensor types: accelerometers versus accelerometers with gyroscopes—and using two angular velocity computational methods, i.e., inclination velocity versus generalized velocity. The results showed an overall small difference (<2° and value independent) for posture percentiles between the two sensor types, but substantial differences in movement percentiles both between the sensor types and between the angular computational methods. For example, the group mean of the 50th percentiles were for accelerometers: 71°/s (generalized velocity) and 33°/s (inclination velocity)—and for accelerometers with gyroscopes: 31°/s (generalized velocity) and 16°/s (inclination velocity). The significant effects of sensor types and angular computational methods on angular velocity measures in field work are important in inter-study comparisons and in comparisons to recommended threshold limit values.
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14.
  • Ali, Muhaddisa Barat, 1986, et al. (author)
  • A novel federated deep learning scheme for glioma and its subtype classification
  • 2023
  • In: Frontiers in Neuroscience. - 1662-4548 .- 1662-453X. ; 17
  • Journal article (peer-reviewed)abstract
    • Background: Deep learning (DL) has shown promising results in molecular-based classification of glioma subtypes from MR images. DL requires a large number of training data for achieving good generalization performance. Since brain tumor datasets are usually small in size, combination of such datasets from different hospitals are needed. Data privacy issue from hospitals often poses a constraint on such a practice. Federated learning (FL) has gained much attention lately as it trains a central DL model without requiring data sharing from different hospitals. Method: We propose a novel 3D FL scheme for glioma and its molecular subtype classification. In the scheme, a slice-based DL classifier, EtFedDyn, is exploited which is an extension of FedDyn, with the key differences on using focal loss cost function to tackle severe class imbalances in the datasets, and on multi-stream network to exploit MRIs in different modalities. By combining EtFedDyn with domain mapping as the pre-processing and 3D scan-based post-processing, the proposed scheme makes 3D brain scan-based classification on datasets from different dataset owners. To examine whether the FL scheme could replace the central learning (CL) one, we then compare the classification performance between the proposed FL and the corresponding CL schemes. Furthermore, detailed empirical-based analysis were also conducted to exam the effect of using domain mapping, 3D scan-based post-processing, different cost functions and different FL schemes. Results: Experiments were done on two case studies: classification of glioma subtypes (IDH mutation and wild-type on TCGA and US datasets in case A) and glioma grades (high/low grade glioma HGG and LGG on MICCAI dataset in case B). The proposed FL scheme has obtained good performance on the test sets (85.46%, 75.56%) for IDH subtypes and (89.28%, 90.72%) for glioma LGG/HGG all averaged on five runs. Comparing with the corresponding CL scheme, the drop in test accuracy from the proposed FL scheme is small (−1.17%, −0.83%), indicating its good potential to replace the CL scheme. Furthermore, the empirically tests have shown that an increased classification test accuracy by applying: domain mapping (0.4%, 1.85%) in case A; focal loss function (1.66%, 3.25%) in case A and (1.19%, 1.85%) in case B; 3D post-processing (2.11%, 2.23%) in case A and (1.81%, 2.39%) in case B and EtFedDyn over FedAvg classifier (1.05%, 1.55%) in case A and (1.23%, 1.81%) in case B with fast convergence, which all contributed to the improvement of overall performance in the proposed FL scheme. Conclusion: The proposed FL scheme is shown to be effective in predicting glioma and its subtypes by using MR images from test sets, with great potential of replacing the conventional CL approaches for training deep networks. This could help hospitals to maintain their data privacy, while using a federated trained classifier with nearly similar performance as that from a centrally trained one. Further detailed experiments have shown that different parts in the proposed 3D FL scheme, such as domain mapping (make datasets more uniform) and post-processing (scan-based classification), are essential.
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15.
  • Andersen, L. M., et al. (author)
  • On-scalp MEG SQUIDs are sensitive to early somatosensory activity unseen by conventional MEG
  • 2020
  • In: NeuroImage. - : Elsevier BV. - 1053-8119 .- 1095-9572. ; 221
  • Journal article (peer-reviewed)abstract
    • Magnetoencephalography (MEG) has a unique capacity to resolve the spatio-temporal development of brain activity from non-invasive measurements. Conventional MEG, however, relies on sensors that sample from a distance (20–40 mm) to the head due to thermal insulation requirements (the MEG sensors function at 4 K in a helmet). A gain in signal strength and spatial resolution may be achieved if sensors are moved closer to the head. Here, we report a study comparing measurements from a seven-channel on-scalp SQUID MEG system to those from a conventional (in-helmet) SQUID MEG system. We compared the spatio-temporal resolution between on-scalp and conventional MEG by comparing the discrimination accuracy for neural activity patterns resulting from stimulating five different phalanges of the right hand. Because of proximity and sensor density differences between on-scalp and conventional MEG, we hypothesized that on-scalp MEG would allow for a more high-resolved assessment of these activity patterns, and therefore also a better classification performance in discriminating between neural activations from the different phalanges. We observed that on-scalp MEG provided better classification performance during an early post-stimulus period (10–20 ms). This corresponded to the electroencephalographic (EEG) component P16/N16 and was an unexpected observation as this component is usually not observed in conventional MEG. This finding shows that on-scalp MEG enables a richer registration of the cortical signal, indicating a sensitivity to what are potentially sources in the thalamo-cortical radiation. We had originally expected that on-scalp MEG would provide better classification accuracy based on activity in proximity to the P60m component compared to conventional MEG. This component indeed allowed for the best classification performance for both MEG systems (60–75%, chance 50%). However, we did not find that on-scalp MEG allowed for better classification than conventional MEG at this latency. We suggest that this absence of differences is due to the limited sensor coverage in the recording, in combination with our strategy for positioning the on-scalp MEG sensors. We show how the current sensor coverage may have limited our chances to register the necessary between-phalange source field dissimilarities for fair hypothesis testing, an approach we otherwise believe to be useful for future benchmarking measurements. © 2020 The Authors
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16.
  • Böhler, Christian, et al. (author)
  • Multilayer Arrays for Neurotechnology Applications (MANTA): Chronically Stable Thin-Film Intracortical Implants
  • 2023
  • In: Advanced Science. - : John Wiley & Sons. - 2198-3844. ; 10:14
  • Journal article (peer-reviewed)abstract
    • Flexible implantable neurointerfaces show great promise in addressing one of the major challenges of implantable neurotechnology, namely the loss of signal connected to unfavorable probe tissue interaction. The authors here show how multilayer polyimide probes allow high-density intracortical recordings to be combined with a reliable long-term stable tissue interface, thereby progressing toward chronic stability of implantable neurotechnology. The probes could record 10–60 single units over 5 months with a consistent peak-to-peak voltage at dimensions that ensure robust handling and insulation longevity. Probes that remain in intimate contact with the signaling tissue over months to years are a game changer for neuroscience and, importantly, open up for broader clinical translation of systems relying on neurotechnology to interface the human brain.
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17.
  • Koriakina, Nadezhda, 1991-, et al. (author)
  • Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection
  • 2024
  • In: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 19:4 April
  • Journal article (peer-reviewed)abstract
    • The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset—PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.
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18.
  • Frishammar, Johan, et al. (author)
  • Digital health platforms for the elderly? Key adoption and usage barriers and ways to address them
  • 2023
  • In: Technological forecasting & social change. - : Elsevier. - 0040-1625 .- 1873-5509. ; 189
  • Journal article (peer-reviewed)abstract
    • Digital healthcare platforms (DHPs) represent a relatively new phenomenon that could provide a valuable complement to physical primary care – for example, by reducing costs, improving access to healthcare, and allowing patient monitoring. However, such platforms are mainly used today by the younger generations, which creates a “digital divide” between the younger and the elderly. This article aims to identify: i) the perceived key barriers that inhibit adoption and usage of DHPs by the elderly, and ii) what DHP providers can do to facilitate increased adoption and usage by the elderly. The article draws on qualitative interviews with elderly and complementary process data from a major Swedish DHP. We find that the elderly perceives two key barriers to initial adoption of DHPs: i) negative attitudes and technology anxiety and ii) one key barrier affecting both adoption and usage – lack of trust. The analysis also identifies multiple development suggestions for DHP improvement to better accommodate the needs of the elderly, including suggestions for application development and tailored education activities. We provide an integrated framework outlining the key barriers perceived and ways to address them. In so doing, we contribute to the literature on mHealth and to the literature on platforms in healthcare.
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19.
  • Ge, Chenjie, 1991, et al. (author)
  • Deep semi-supervised learning for brain tumor classification
  • 2020
  • In: BMC Medical Imaging. - : Springer Science and Business Media LLC. - 1471-2342. ; 20:1
  • Journal article (peer-reviewed)abstract
    • Background: This paper addresses issues of brain tumor, glioma, classification from four modalities of Magnetic Resonance Image (MRI) scans (i.e., T1 weighted MRI, T1 weighted MRI with contrast-enhanced, T2 weighted MRI and FLAIR). Currently, many available glioma datasets often contain some unlabeled brain scans, and many datasets are moderate in size. Methods: We propose to exploit deep semi-supervised learning to make full use of the unlabeled data. Deep CNN features were incorporated into a new graph-based semi-supervised learning framework for learning the labels of the unlabeled data, where a new 3D-2D consistent constraint is added to make consistent classifications for the 2D slices from the same 3D brain scan. A deep-learning classifier is then trained to classify different glioma types using both labeled and unlabeled data with estimated labels. To alleviate the overfitting caused by moderate-size datasets, synthetic MRIs generated by Generative Adversarial Networks (GANs) are added in the training of CNNs. Results: The proposed scheme has been tested on two glioma datasets, TCGA dataset for IDH-mutation prediction (molecular-based glioma subtype classification) and MICCAI dataset for glioma grading. Our results have shown good performance (with test accuracies 86.53% on TCGA dataset and 90.70% on MICCAI dataset). Conclusions: The proposed scheme is effective for glioma IDH-mutation prediction and glioma grading, and its performance is comparable to the state-of-the-art.
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20.
  • Jacobsson, Martin, 1976-, et al. (author)
  • Deep Learning-Based Early Prediction of Intraoperative Hypotension
  • 2021
  • Conference paper (peer-reviewed)abstract
    • This work focuses on predicting near-term onset of hypotension prior to onset using convolutional neural networks. Based solely on the arterial blood pressure curve, our initial attempt can predict an onset with 60% sensitivity and 80% specificity 5-15 minutes before onset.Clinical relevance Hypotension is common during large surgery. By identifying and treating hypotensive episodes early, preferably even before onset, hypotension and its associate post- surgery complications are reduced. Even a prediction with 80% sensitivity/specificity is valuable for the anesthesiologist. 
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21.
  • Simistira Liwicki, Foteini, et al. (author)
  • Bimodal electroencephalography-functional magnetic resonance imaging dataset for inner-speech recognition
  • 2023
  • In: Scientific Data. - : Springer Nature. - 2052-4463. ; 10
  • Journal article (peer-reviewed)abstract
    • The recognition of inner speech, which could give a ‘voice’ to patients that have no ability to speak or move, is a challenge for brain-computer interfaces (BCIs). A shortcoming of the available datasets is that they do not combine modalities to increase the performance of inner speech recognition. Multimodal datasets of brain data enable the fusion of neuroimaging modalities with complimentary properties, such as the high spatial resolution of functional magnetic resonance imaging (fMRI) and the temporal resolution of electroencephalography (EEG), and therefore are promising for decoding inner speech. This paper presents the first publicly available bimodal dataset containing EEG and fMRI data acquired nonsimultaneously during inner-speech production. Data were obtained from four healthy, right-handed participants during an inner-speech task with words in either a social or numerical category. Each of the 8-word stimuli were assessed with 40 trials, resulting in 320 trials in each modality for each participant. The aim of this work is to provide a publicly available bimodal dataset on inner speech, contributing towards speech prostheses.
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22.
  • Larsson, Julia, et al. (author)
  • Optimizing study design in LPS challenge studies for quantifying drug induced inhibition of TNF? response: Did we miss the prime time?
  • 2022
  • In: European Journal of Pharmaceutical Sciences. - : Elsevier BV. - 0928-0987 .- 1879-0720. ; 176
  • Journal article (peer-reviewed)abstract
    • In this work we evaluate the study design of LPS challenge experiments used for quantification of drug induced inhibition of TNF alpha response and provide general guidelines of how to improve the study design. Analysis of model simulated data, using a recently published TNF alpha turnover model, as well as the optimal design tool PopED have been used to find the optimal values of three key study design variables - time delay between drug and LPS administration, LPS dose, and sampling time points - that in turn could make the resulting TNF alpha response data more informative. Our findings suggest that the current rule of thumb for choosing the time delay should be reconsidered, and that the placement of the measurements after maximal TNF alpha response are crucial for the quality of the experiment. Furthermore, a literature study summarizing a wide range of published LPS challenge studies is provided, giving a broader perspective of how LPS challenge studies are usually conducted both in a preclinical and clinical setting.
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23.
  • Lind, Carl, et al. (author)
  • Reducing postural load in order picking through a smart workwear system using real-time vibrotactile feedback
  • 2020
  • In: Applied Ergonomics. - : Elsevier. - 0003-6870 .- 1872-9126. ; 89
  • Journal article (peer-reviewed)abstract
    • Vibrotactile feedback training may be one possible method for interventions that target at learning better work techniques and improving postures in manual handling. This study aimed to evaluate the short term effect of real-time vibrotactile feedback on postural exposure using a smart workwear system for work postures intervention in simulated industrial order picking. Fifteen workers at an industrial manufacturing plant performed order-picking tasks, in which the vibrotactile feedback was used for postural training at work. The system recorded the trunk and upper arm postures. Questionnaires and semi-structured interviews were conducted about the users’ experience of the system. The results showed reduced time in trunk inclination ≥20°, ≥30° and ≥45° and dominant upper arm elevation ≥30° and ≥45° when the workers received feedback, and for trunk inclination ≥20°, ≥30° and ≥45° and dominant upper arm elevation ≥30°, after feedback withdrawal. The workers perceived the system as useable, comfortable, and supportive for learning. The system has the potential of contributing to improved postures in order picking through an automated short-term training program. © 2020 Elsevier Ltd
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24.
  • Pozzoli, Susanna, et al. (author)
  • Domain expertise–agnostic feature selection for the analysis of breast cancer data*
  • 2020
  • In: Artificial Intelligence in Medicine. - : Elsevier B.V.. - 0933-3657 .- 1873-2860. ; 108
  • Journal article (peer-reviewed)abstract
    • Progress in proteomics has enabled biologists to accurately measure the amount of protein in a tumor. This work is based on a breast cancer data set, result of the proteomics analysis of a cohort of tumors carried out at Karolinska Institutet. While evidence suggests that an anomaly in the protein content is related to the cancerous nature of tumors, the proteins that could be markers of cancer types and subtypes and the underlying interactions are not completely known. This work sheds light on the potential of the application of unsupervised learning in the analysis of the aforementioned data sets, namely in the detection of distinctive proteins for the identification of the cancer subtypes, in the absence of domain expertise. In the analyzed data set, the number of samples, or tumors, is significantly lower than the number of features, or proteins; consequently, the input data can be thought of as high-dimensional data. The use of high-dimensional data has already become widespread, and a great deal of effort has been put into high-dimensional data analysis by means of feature selection, but it is still largely based on prior specialist knowledge, which in this case is not complete. There is a growing need for unsupervised feature selection, which raises the issue of how to generate promising subsets of features among all the possible combinations, as well as how to evaluate the quality of these subsets in the absence of specialist knowledge. We hereby propose a new wrapper method for the generation and evaluation of subsets of features via spectral clustering and modularity, respectively. We conduct experiments to test the effectiveness of the new method in the analysis of the breast cancer data, in a domain expertise–agnostic context. Furthermore, we show that we can successfully augment our method by incorporating an external source of data on known protein complexes. Our approach reveals a large number of subsets of features that are better at clustering the samples than the state-of-the-art classification in terms of modularity and shows a potential to be useful for future proteomics research.
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25.
  • de Dios, Eddie, et al. (author)
  • Introduction to Deep Learning in Clinical Neuroscience
  • 2022
  • In: Acta Neurochirurgica, Supplement. - Cham : Springer International Publishing. - 2197-8395 .- 0065-1419. ; 134, s. 79-89
  • Book chapter (other academic/artistic)abstract
    • The use of deep learning (DL) is rapidly increasing in clinical neuroscience. The term denotes models with multiple sequential layers of learning algorithms, architecturally similar to neural networks of the brain. We provide examples of DL in analyzing MRI data and discuss potential applications and methodological caveats. Important aspects are data pre-processing, volumetric segmentation, and specific task-performing DL methods, such as CNNs and AEs. Additionally, GAN-expansion and domain mapping are useful DL techniques for generating artificial data and combining several smaller datasets. We present results of DL-based segmentation and accuracy in predicting glioma subtypes based on MRI features. Dice scores range from 0.77 to 0.89. In mixed glioma cohorts, IDH mutation can be predicted with a sensitivity of 0.98 and specificity of 0.97. Results in test cohorts have shown improvements of 5–7% in accuracy, following GAN-expansion of data and domain mapping of smaller datasets. The provided DL examples are promising, although not yet in clinical practice. DL has demonstrated usefulness in data augmentation and for overcoming data variability. DL methods should be further studied, developed, and validated for broader clinical use. Ultimately, DL models can serve as effective decision support systems, and are especially well-suited for time-consuming, detail-focused, and data-ample tasks.
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26.
  • Jacobsson, Martin, 1976-, et al. (author)
  • The role of compression in large scale data transfer and storage of typical biomedical signals at hospitals
  • 2023
  • In: Health Informatics Journal. - : Sage Publications. - 1460-4582 .- 1741-2811. ; 29:4
  • Journal article (peer-reviewed)abstract
    • In modern hospitals, monitoring patients’ vital signs and other biomedical signals is standard practice. With the advent of data-driven healthcare, Internet of medical things, wearable technologies, and machine learning, we expect this to accelerate and to be used in new and promising ways, including early warning systems and precision diagnostics. Hence, we see an ever-increasing need for retrieving, storing, and managing the large amount of biomedical signal data generated. The popularity of standards, such as HL7 FHIR for interoperability and data transfer, have also resulted in their use as a data storage model, which is inefficient. This article raises concern about the inefficiency of using FHIR for storage of biomedical signals and instead highlights the possibility of a sustainable storage based on data compression. Most reported efforts have focused on ECG signals; however, many other typical biomedical signals are understudied. In this article, we are considering arterial blood pressure, photoplethysmography, and respiration. We focus on simple lossless compression with low implementation complexity, low compression delay, and good compression ratios suitable for wide adoption. Our results show that it is easy to obtain a compression ratio of 2.7:1 for arterial blood pressure, 2.9:1 for photoplethysmography, and 4.1:1 for respiration.
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27.
  • Palmquist, Anders, 1977, et al. (author)
  • Complex geometry and integrated macro-porosity: Clinical applications of electron beam melting to fabricate bespoke bone-anchored implants
  • 2023
  • In: Acta Biomaterialia. - : Elsevier BV. - 1742-7061 .- 1878-7568. ; 156, s. 125-145
  • Research review (peer-reviewed)abstract
    • The last decade has witnessed rapid advancements in manufacturing technologies for biomedical implants. Additive manufacturing (or 3D printing) has broken down major barriers in the way of producing complex 3D geometries. Electron beam melting (EBM) is one such 3D printing process applicable to metals and alloys. EBM offers build rates up to two orders of magnitude greater than comparable laser-based technologies and a high vacuum environment to prevent accumulation of trace elements. These features make EBM particularly advantageous for materials susceptible to spontaneous oxidation and nitrogen pick-up when exposed to air (e.g., titanium and titanium-based alloys). For skeletal reconstruction(s), anatomical mimickry and integrated macro-porous architecture to facilitate bone ingrowth are undoubtedly the key features of EBM manufactured implants. Using finite element modelling of physiological loading conditions, the design of a prosthesis may be further personalised. This review looks at the many unique clinical applications of EBM in skeletal repair and the ground-breaking innovations in prosthetic rehabilitation. From a simple acetabular cup to the fifth toe, from the hand-wrist complex to the shoulder, and from vertebral replacement to cranio-maxillofacial reconstruction, EBM has experienced it all. While sternocostal reconstructions might be rare, the repair of long bones using EBM manufactured implants is becoming exceedingly frequent. Despite the various merits, several challenges remain yet untackled. Nevertheless, with the capability to produce osseointegrating implants of any conceivable shape/size, and permissive of bone ingrowth and functional loading, EBM can pave the way for numerous fascinating and novel applications in skeletal repair, regeneration, and rehabilitation. Statement of significance: Electron beam melting (EBM) offers unparalleled possibilities in producing contaminant-free, complex and intricate geometries from alloys of biomedical interest, including Ti6Al4V and CoCr. We review the diverse range of clinical applications of EBM in skeletal repair, both as mass produced off-the-shelf implants and personalised, patient-specific prostheses. From replacing large volumes of disease-affected bone to complex, multi-material reconstructions, almost every part of the human skeleton has been replaced with an EBM manufactured analog to achieve macroscopic anatomical-mimickry. However, various questions regarding long-term performance of patient-specific implants remain unaddressed. Directions for further development include designing personalised implants and prostheses based on simulated loading conditions and accounting for trabecular bone microstructure with respect to physiological factors such as patient's age and disease status.
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28.
  • Borrelli, P., et al. (author)
  • Artificial intelligence-aided CT segmentation for body composition analysis: a validation study
  • 2021
  • In: European Radiology Experimental. - : Springer Science and Business Media LLC. - 2509-9280. ; 5:1
  • Journal article (peer-reviewed)abstract
    • BackgroundBody composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT slice. Our goal was to develop an artificial-intelligence (AI)-based method for automated quantification of three-dimensional SAT and muscle volumes from CT images.MethodsEthical approvals from Gothenburg and Lund Universities were obtained. Convolutional neural networks were trained to segment SAT and muscle using manual segmentations on CT images from a training group of 50 patients. The method was applied to a separate test group of 74 cancer patients, who had two CT studies each with a median interval between the studies of 3days. Manual segmentations in a single CT slice were used for comparison. The accuracy was measured as overlap between the automated and manual segmentations.ResultsThe accuracy of the AI method was 0.96 for SAT and 0.94 for muscle. The average differences in volumes were significantly lower than the corresponding differences in areas in a single CT slice: 1.8% versus 5.0% (p <0.001) for SAT and 1.9% versus 3.9% (p < 0.001) for muscle. The 95% confidence intervals for predicted volumes in an individual subject from the corresponding single CT slice areas were in the order of 20%.Conclusions The AI-based tool for quantification of SAT and muscle volumes showed high accuracy and reproducibility and provided a body composition analysis that is more relevant than manual analysis of a single CT slice.
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29.
  • Zanoli, Massimiliano, 1989 (author)
  • Ultra wideband microwave hyperthermia for brain cancer treatment
  • 2022
  • Doctoral thesis (other academic/artistic)abstract
    • Despite numerous clinical trials demonstrating that microwave hyperthermia is a powerful adjuvant modality in the treatment of cancers, there have been few instances where this method has been applied to brain tumors. The reason is a combination of anatomical and physiological factors in this site that require an extra degree of accuracy and precision in the thermal dose delivery. Current clinical applicators are not able to provide such control, partly because they are designed to operate at a single fixed frequency. In terms of treatment planning, the use of a single frequency is limiting as the size of the focal spot cannot be modified to accommodate the specific tumor volume and location. The introduction of ultra wide-band (UWB) systems opens up an opportunity to overcome these limitations, as they convey the possibility of adapting the focal spot and obtaining different power deposition patterns to reduce the heating of healthy tissues. In this thesis, we explore whether the current SAR-based treatment planning methods can be meaningfully translated to the UWB setting and propose new solutions for deep UWB microwave hyperthermia. We analyze the most commonly used cost functions for treatment planning optimization and discuss their suitability for use with UWB systems. Then, we propose a novel SAR-based cost function (HCQ) for UWB optimization that exhibits a high correlation with the resulting tumor temperature. To solve for the HCQ, we describe a novel, time-reversal-based, iterative scheme for a rapid and efficient optimization of UWB treatment plans. Next, we investigate the design possibilities of UWB brain applicators and introduce a fast E-field approximation scheme to quickly explore a large number of array configurations. The method determines the best antenna arrangement around the head with respect to the multiple objectives and requirements of clinical hyperthermia. Together, the proposed solutions manage to achieve the level of tumor coverage and hot-spot suppression that is necessary for a successful treatment. Finally, we investigate the benefit of integrating hyperthermia delivered by an optimized UWB applicator into the radiation therapy plan for a pediatric medulloblastoma patient. The results suggest that UWB microwave hyperthermia for brain cancer treatment is feasible and motivate efforts for further development of UWB applicators and systems.
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30.
  • Lee, Eunji, 1980, et al. (author)
  • Development of Verified Innovation Process for Healthcare Solutions (VIPHS): A Stepwise Model for Digital Health
  • 2023
  • In: Studies in health technology and informatics. - 1879-8365 .- 0926-9630. ; 302, s. 736-740
  • Journal article (peer-reviewed)abstract
    • Many digital health projects often stop in the pilot or test phase. Realisation of new digital health services is often challenging due to lack of guidelines for the step-by-step roll-out and implementation of the systems when changing work processes and procedures are needed. This study describes development of the Verified Innovation Process for Healthcare Solutions (VIPHS) - a stepwise model for digital health innovation and utilisation using service design principles. A multiple case study (two cases) involving participant observation, role play, and semi-structured interviews were conducted for the model development in prehospital settings. The model might be helpful to support realisation of innovative digital health projects in a holistic, disciplined, and strategic way.
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31.
  • Morin, Maxim, et al. (author)
  • Skin hydration dynamics investigated by electrical impedance techniques in vivo and in vitro
  • 2020
  • In: Scientific reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 10:1, s. 17218-
  • Journal article (peer-reviewed)abstract
    • Skin is easily accessible for transdermal drug delivery and also attractive for biomarker sampling. These applications are strongly influenced by hydration where elevated hydration generally leads to increased skin permeability. Thus, favorable transdermal delivery and extraction conditions can be easily obtained by exploiting elevated skin hydration. Here, we provide a detailed in vivo and in vitro investigation of the skin hydration dynamics using three techniques based on electrical impedance spectroscopy. Good correlation between in vivo and in vitro results is demonstrated, which implies that simple but realistic in vitro models can be used for further studies related to skin hydration (e.g., cosmetic testing). Importantly, the results show that hydration proceeds in two stages. Firstly, hydration between 5 and 10 min results in a drastic skin impedance change, which is interpreted as filling of superficial voids in skin with conducting electrolyte solution. Secondly, a subtle impedance change is observed over time, which is interpreted as leveling of the water gradient across skin leading to structural relaxation/changes of the macromolecular skin barrier components. With respect to transdermal drug delivery and extraction of biomarkers; 1 h of hydration is suggested to result in beneficial and stable conditions in terms of high skin permeability and extraction efficiency.
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32.
  • Westin, Karin, et al. (author)
  • Detection of interictal epileptiform discharges: A comparison of on-scalp MEG and conventional MEG measurements
  • 2020
  • In: Clinical Neurophysiology. - : Elsevier BV. - 1872-8952 .- 1388-2457. ; 131:8, s. 1711-1720
  • Journal article (peer-reviewed)abstract
    • Objective: Conventional MEG provides an unsurpassed ability to, non-invasively, detect epileptic activity. However, highly resolved information on small neuronal populations required in epilepsy diagnostics is lost and can be detected only intracranially. Next-generation on-scalp magnetencephalography (MEG) sensors aim to retrieve information unavailable to conventional non-invasive brain imaging techniques. To evaluate the benefits of on-scalp MEG in epilepsy, we performed the first-ever such measurement on an epilepsy patient. Methods: Conducted as a benchmarking study focusing on interictal epileptiform discharge (IED) detectability, an on-scalp high-temperature superconducting quantum interference device magnetometer (high-Tc SQUID) system was compared to a conventional, low-temperature SQUID system. Coregistration of electroencephalopraphy (EEG) was performed. A novel machine learning-based IED-detection algorithm was developed to aid identification of on-scalp MEG unique IEDs. Results: Conventional MEG contained 24 IEDs. On-scalp MEG revealed 47 IEDs (16 co-registered by EEG, 31 unique to the on-scalp MEG recording). Conclusion: Our results indicate that on-scalp MEG might capture IEDs not seen by other non-invasive modalities. Significance: On-scalp MEG has the potential of improving non-invasive epilepsy evaluation.
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33.
  • Lindgren, Helena, et al. (author)
  • The STAR-C Intelligent Coach : a Cross- Disciplinary Design Process of a Behaviour Change Intervention in Primary Care
  • 2020
  • In: pHealth 2020. - : IOS Press. - 9781643681122 ; , s. 203-208
  • Conference paper (peer-reviewed)abstract
    • A broad range of aspects are needed to be taken into consideration in the design and development of personalized coaching systems based on artificial intelligence methodologies. This research presents the initial phase of joining different professional and stakeholder perspectives on behavior change technologies into a flexible design proposal for a digital coaching system. The diversity and sometimes opposed views on content, behavior, purposes and context were managed using a structured argument-based design approach, which also feed into the behavior of the personalized system. Results include a set of personalization strategies that will be further elaborated with the target user group to manage sensitive issues such as ethics, social norms, privacy, motivation, autonomy and social relatedness.
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34.
  • Loskutova, Ksenia (author)
  • Perfluorocarbon microdroplets stabilized by cellulose nanofibers : Toward ultrasound-mediated diagnostics and therapy
  • 2023
  • Doctoral thesis (other academic/artistic)abstract
    • Ultrasound contrast agents consist of gas-filled micrometer-sized bubbles that are injectedinto the blood stream. Ultrasound contrast agents are an invaluable tool for ultrasound imaging of the cardiac muscle and highly vascularized structures such as kidneys and the liver. The ability of gas-filled microbubbles to enhance contrast in ultrasound imaging comes from their increased scattering ability due to significantly lower compressibility compared to surrounding soft tissues.The discovery of acoustic droplet vaporization, the phase-transition of liquid-filleddroplets into gas-filled microbubbles upon ultrasound exposure, has expanded the potential utility of ultrasound-mediated diagnostics and therapy to include applications such as gas embolization, histotripsy, and localized drug delivery. Multiple requirements are put onto both gas-filled microbubbles and phase-change contrast agents: they have to be non-toxic, acoustically active at clinically relevant pressure amplitudes, and their dynamic behavior has to be predictable to maximize the therapeutic or diagnostic effect while minimizing mechanical damage to surrounding healthy tissue. Novel designs of phase-change contrast agents that are able to undergo acoustic droplet vaporization could enable improved in vivo stability compared to conventional gas-filled ultrasound contrast agents.Pickering emulsions, with solid particles used as stabilizing agents instead of surfactants, have an increased stability compared to conventional emulsions. Cellulose-based Pickering emulsions in particular have previously been investigated for biomedical applications. Cellulose is a suitable material in biomedical applications as it originates from renewable sources, is biocompatible, and the surface can be easily modified. To the author’s current knowledge, cellulose-based Pickering emulsions have not previously been investigated for ultrasound-mediated applications. It is necessary to know the mechanical and acoustic properties of novel formulations and their impact on biological cells for their translation into in vivo research and future clinical use.In this thesis, the acoustic, mechanical, and biological properties of cellulose nanofiber(CNF)-shelled perfluoropentane (PFP) droplets, a type of Pickering emulsion, were investigated for ultrasound-mediated medical applications. Firstly, the current state-of-the-art and development of phase-change contrast agents, the mechanism behind acoustic droplet vaporization, and potential ultrasound-mediated medical applications were investigated. Secondly, a theoretical model that would describe and predict the acoustic response of CNF-shelled PFP droplets undergoing acoustic droplet vaporization was developed. Thirdly, the compressibility of CNF-shelled PFP droplets using an acoustophoretic setup was measured. Later, the effect of the geometry of the surrounding medium and acoustic parameters on the acoustic response of CNF-shelled PFP droplets was explored. Finally, the biocompatibility of CNF-shelled PFP droplets cells was investigated through a hemolysis assay and measurement of change in cell viability of breast cancer cells.The CNF shell has a significant impact on the predicted resonance behavior and compressibility of CNF-shelled PFP droplets, as it has significantly larger bulk and Young’s modulus than previously reported shell materials. The predicted linear resonance behavior was in the upper range of medical ultrasound (5-8 MHz), making harmonic imaging at optimal conditions difficult. However, it was demonstrated that CNF-shelled PFP droplets could be imaged using a nonlinear ultrasound imaging sequence at a frequency regularly used in clinics. Thus, CNF-shelled PFP droplets were able to undergo acoustic droplet vaporization at clinically relevant conditions. The peak negative pressure of the incident acoustic wave had a significant impact on the acoustic response of CNF-shelled PFP droplets, as higher acoustic pressure amplitudes resulted in a more disruptive behavior. Finally, CNF-shelled PFP droplets did not influence the cell viability of breast cancer cells. This was true regardless of whether or not a non-encapsulated cytotoxic drug with a known impact on cell viability was present. In summary, the results of this work showed that CNF-shelled PFP droplets are biocompatible and acoustically active at clinically relevant conditions, which shows that cellulose-based Pickering emulsions have potential in ultrasound-mediated diagnostics and therapy.
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35.
  • Lagerstrand, Kerstin M, et al. (author)
  • Reliable phase-contrast flow volume magnetic resonance measurements are feasible without adjustment of the velocity encoding parameter
  • 2020
  • In: Journal of Medical Imaging. - 2329-4310 .- 2329-4302. ; 7:6
  • Journal article (peer-reviewed)abstract
    • Purpose: To show that adjustment of velocity encoding (VENC) for phase-contrast (PC) flow volume measurements is not necessary in modern MR scanners with effective background velocity offset corrections. Approach: The independence on VENC was demonstrated theoretically, but also experimentally on dedicated phantoms and on patients with chronic aortic regurgitation (n = 17) and one healthy volunteer. All PC measurements were performed using a modern MR scanner, where the pre-emphasis circuit but also a subsequent post-processing filter were used for effective correction of background velocity offset errors. Results: The VENC level strongly affected the velocity noise level in the PC images and, hence, the estimated peak flow velocity. However, neither the regurgitant blood flow volume nor the mean flow velocity displayed any clinically relevant dependency on the VENC level. Also, the background velocity offset was shown to be close to zero (
  •  
36.
  • Lu, Han, et al. (author)
  • A microfluidic perspective on conventional in vitro transcranial direct current stimulation methods
  • 2023
  • In: Journal of Neuroscience Methods. - : Elsevier B.V.. - 0165-0270 .- 1872-678X. ; 385
  • Research review (peer-reviewed)abstract
    • Transcranial direct current stimulation (tDCS) is a promising non-invasive brain stimulation method to treat neurological and psychiatric diseases. However, its underlying neural mechanisms warrant further investigation. Indeed, dose–response interrelations are poorly understood. Placing explanted brain tissue, mostly from mice or rats, into a uniform direct current electric field (dcEF) is a well-established in vitro system to elucidate the neural mechanism of tDCS. Nevertheless, we will show that generating a defined, uniform, and constant dcEF throughout a brain slice is challenging. This article critically reviews the methods used to generate and calibrate a uniform dcEF. We use finite element analysis (FEA) to evaluate the widely used parallel electrode configuration and show that it may not reliably generate uniform dcEF within a brain slice inside an open interface or submerged chamber. Moreover, equivalent circuit analysis and measurements inside a testing chamber suggest that calibrating the dcEF intensity with two recording electrodes can inaccurately capture the true EF magnitude in the targeted tissue when specific criteria are not met. Finally, we outline why microfluidic chambers are an effective and calibration-free approach of generating spatiotemporally uniform dcEF for DCS in vitro studies, facilitating accurate and fine-scale dcEF adjustments. We are convinced that improving the precision and addressing the limitations of current experimental platforms will substantially improve the reproducibility of in vitro experimental results. A better mechanistic understanding of dose–response relations will ultimately facilitate more effective non-invasive stimulation therapies in patients.
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37.
  • Mosin, Vasilii, 1993, et al. (author)
  • Comparing autoencoder-based approaches for anomaly detection in highway driving scenario images
  • 2022
  • In: SN Applied Sciences. - : Springer Science and Business Media LLC. - 2523-3971 .- 2523-3963. ; 4:12
  • Journal article (peer-reviewed)abstract
    • Autoencoder-based anomaly detection approaches can be used for precluding scope compliance failures of the automotive perception. However, the applicability of these approaches for the automotive domain should be thoroughly investigated. We study the capability of two autoencoder-based approaches using reconstruction errors and bottleneck-values for detecting semantic anomalies in automotive images. As a use-case, we consider a specific highway driving scenario identifying if there are any vehicles in the field of view of a front-looking camera. We conduct a series of experiments with two simulated driving scenario datasets and measure anomaly detection performance for different cases. We systematically test different autoencoders and training parameters, as well as the influence of image colors. We show that the autoencoder-based approaches demonstrate promising results for detecting semantic anomalies in highway driving scenario images in some cases. However, we also observe the variability of anomaly detection performance between different experiments. The autoencoder-based approaches are capable of detecting semantic anomalies in highway driving scenario images to some extent. However, further research with other use-cases and real datasets is needed before they can be safely applied in the automotive domain.
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38.
  • Shaner, Sebastian, et al. (author)
  • Brain stimulation-on-a-chip: a neuromodulation platform for brain slices
  • 2023
  • In: Lab on a Chip. - : Royal Society of Chemistry. - 1473-0197 .- 1473-0189. ; 23:23, s. 4967-4985
  • Journal article (peer-reviewed)abstract
    • Electrical stimulation of ex vivo brain tissue slices has been a method used to understand mechanisms imparted by transcranial direct current stimulation (tDCS), but there are significant direct current electric field (dcEF) dosage and electrochemical by-product concerns in conventional experimental setups that may impact translational findings. Therefore, we developed an on-chip platform with fluidic, electrochemical, and magnetically-induced spatial control. Fluidically, the chamber geometrically confines precise dcEF delivery to the enclosed brain slice and allows for tissue recovery in order to monitor post-stimulation effects. Electrochemically, conducting hydrogel electrodes mitigate stimulation-induced faradaic reactions typical of commonly-used metal electrodes. Magnetically, we applied ferromagnetic substrates beneath the tissue and used an external permanent magnet to enable in situ rotational control in relation to the dcEF. By combining the microfluidic chamber with live-cell calcium imaging and electrophysiological recordings, we showcased the potential to study the acute and lasting effects of dcEFs with the potential of providing multi-session stimulation. This on-chip bioelectronic platform presents a modernized yet simple solution to electrically stimulate explanted tissue by offering more environmental control to users, which unlocks new opportunities to conduct thorough brain stimulation mechanistic investigations.
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39.
  • Palm, Klas, PhD, 1964-, et al. (author)
  • Identifying and Addressing Barriers and Facilitators for the Implementation of Internet of Things in Distributed Care : Protocol for a Case Study
  • 2023
  • In: JMIR Research Protocols. - : JMIR Publications. - 1929-0748. ; 12
  • Journal article (peer-reviewed)abstract
    • Background: The internet of things (IoT) is recognized as a valuable approach to supporting health care to achieve quality and person-centered care. This study aims to identify the facilitators and barriers associated with implementing IoT solutions in health care within a Scandinavian context. It addresses the pressing need to adapt health care systems to the demographic changes occurring in Scandinavia. The vision of "Vision eHealth 2025," a long-term strategic direction for digitalization in Sweden, serves as the background for this project. The implementation of IoT solutions is a crucial aspect of achieving the vision's goal of making Sweden a global leader in using digitalization and eHealth opportunities by 2025. IoT is recognized as a valuable approach to supporting health care to achieve quality and person-centered care. Previous research has shown that there is a gap in our understanding of social and organizational challenges related to IoT and that the implementation and introduction of new technology in health care is often problematic.Objective: In this study, we will identify facilitating and hindering factors for the implementation of IoT solutions in social and health care.Methods: We will use an explorative design with a case study approach. The data collection will comprise questionnaires and qualitative interviews. Also, a literature review will be conducted at the start of the project. Thus, quantitative and qualitative data will be collected concurrently and integrated into a convergent mixed methods approach.Results: As of June 2023, data for the review and 22 interviews with the stakeholders have been performed. The co-design with stakeholders will be performed in the fall of 2023.Conclusions: This study represents a unique and innovative opportunity to gain new knowledge relevant and useful for future implementation of new technology at health care organizations so they can continue to offer high-quality, person-centered care. The outcomes of this research will contribute to a better understanding of the conditions necessary to implement and fully use the potential of IoT solutions. By developing cocreated implementation strategies, the study seeks to bridge the gap between theory and practice. Ultimately, this project aims to facilitate the adoption of IoT solutions in health care for promoting improved patient care and using technology to meet the evolving needs of health care.
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40.
  • Pirozzi, Ileana, et al. (author)
  • Circulatory Support : Artificial Muscles for the Future of Cardiovascular Assist Devices
  • 2023
  • In: Advanced Materials. - : Wiley. - 0935-9648 .- 1521-4095.
  • Journal article (peer-reviewed)abstract
    • Artificial muscles enable the design of soft implantable devices which are poised to transform the way we mechanically support the heart today. Heart failure is a prevalent and deadly disease, which is treated with the implantation of rotary blood pumps as the only alternative to heart transplantation. The clinically used mechanical devices are associated with severe adverse events, which are reflected here in a comprehensive list of critical requirements for soft active devices of the future: low power, no blood contact, pulsatile support, physiological responsiveness, high cycle life, and less-invasive implantation. In this review, we investigate and critically evaluate prior art in artificial muscles for their applicability in the short and long term. We highlight the main challenges regarding the effectiveness, controllability, and implantability of recently proposed actuators and explore future perspectives for attachment, physiological responsiveness, durability, and biodegradability as well as equitable design considerations.
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41.
  • Lei, Xiangyu, 1992, et al. (author)
  • Model-Based Parametric Study of Surface-Breaking Defect Characterization Using Half-Skip Total Focusing Method
  • 2023
  • In: Journal of nondestructive evaluation. - : Springer Nature. - 0195-9298 .- 1573-4862. ; 42:2
  • Journal article (peer-reviewed)abstract
    • As the demand of structural integrity in manufacturing industries is increasing, the ultrasonic array technique has drawn more attention thanks to its inspection flexibility and versatility. By taking advantage of the possibility of individual triggering of each array element, full matrix capture (FMC) data acquisition strategy has been developed that contains the entire information of an inspection scenario. Total focusing method (TFM) as one of the ultrasonic imaging algorithms, is preferably applied to FMC dataset since it uses all information in FMC to synthetically focus the sound energy at every image pixel in the region of interest. Half-skip TFM (HSTFM) is proposed in multi-mode TFM imaging that involves a backwall reflection wave path, so that the defect profile could be reconstructed for accurate defect characterization. In this paper, a method involving Snell’s law-based wave mode conversion is proposed to account for more reasonable wave propagation time when wave mode conversion happens at backwall reflection in HSTFM. A series of model based simulations (in software simSUNDT) are performed for parametric studies, with the intention of investigating the capability of defect characterization using HSTFM with varying tilt angle and relative position of surface-breaking notch to array probe. The results show that certain TFM modes could help with defect characterization, but the effectiveness is limited with varying defect features. It is inappropriate to address a certain mode for all characterization perspectives but rather a combination, i.e., multi-mode TFM, should be adopted for possible interpretation and characterization of defect features.
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42.
  • Piri, R., et al. (author)
  • Common carotid segmentation in F-18-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based and manual method
  • 2023
  • In: Clinical Physiology and Functional Imaging. - : Wiley. - 1475-0961 .- 1475-097X. ; 43:2, s. 71-77
  • Journal article (peer-reviewed)abstract
    • Background Carotid atherosclerosis is a major cause of stroke, traditionally diagnosed late. Positron emission tomography/computed tomography (PET/CT) with F-18-sodium fluoride (NaF) detects arterial wall micro-calcification long before macro-calcification becomes detectable by ultrasound, CT or magnetic resonance imaging. However, manual PET/CT processing is time-consuming and requires experience. We compared a convolutional neural network (CNN) approach with manual segmentation of the common carotids. Methods Segmentation in NaF-PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients were compared for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, and SUVtotal). SUVmean was the average of SUVmeans within the VOI, SUVmax the highest SUV in all voxels in the VOI, and SUVtotal the SUVmean multiplied by the Vol of the VOI. Intra and Interobserver variability with manual segmentation was examined in 25 randomly selected scans. Results Bias for Vol, SUVmean, SUVmax, and SUVtotal were 1.33 +/- 2.06, -0.01 +/- 0.05, 0.09 +/- 0.48, and 1.18 +/- 1.99 in the left and 1.89 +/- 1.5, -0.07 +/- 0.12, 0.05 +/- 0.47, and 1.61 +/- 1.47, respectively, in the right common carotid artery. Manual segmentation lasted typically 20 min versus 1 min with the CNN-based approach. Mean Vol deviation at repeat manual segmentation was 14% and 27% in left and right common carotids. Conclusions CNN-based segmentation was much faster and provided SUVmean values virtually identical to manually obtained ones, suggesting CNN-based analysis as a promising substitute of slow and cumbersome manual processing.
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43.
  • Polymeri, Erini, et al. (author)
  • Deep learning-based quantification of PET/CT prostate gland uptake : association with overall survival
  • 2020
  • In: Clinical Physiology and Functional Imaging. - Chichester : Blackwell Publishing. - 1475-0961 .- 1475-097X. ; 40:2, s. 106-113
  • Journal article (peer-reviewed)abstract
    • Aim: To validate a deep-learning (DL) algorithm for automated quantification of prostate cancer on positron emission tomography/computed tomography (PET/CT) and explore the potential of PET/CT measurements as prognostic biomarkers. Material and methods: Training of the DL-algorithm regarding prostate volume was performed on manually segmented CT images in 100 patients. Validation of the DL-algorithm was carried out in 45 patients with biopsy-proven hormone-naïve prostate cancer. The automated measurements of prostate volume were compared with manual measurements made independently by two observers. PET/CT measurements of tumour burden based on volume and SUV of abnormal voxels were calculated automatically. Voxels in the co-registered 18F-choline PET images above a standardized uptake value (SUV) of 2·65, and corresponding to the prostate as defined by the automated segmentation in the CT images, were defined as abnormal. Validation of abnormal voxels was performed by manual segmentation of radiotracer uptake. Agreement between algorithm and observers regarding prostate volume was analysed by Sørensen-Dice index (SDI). Associations between automatically based PET/CT biomarkers and age, prostate-specific antigen (PSA), Gleason score as well as overall survival were evaluated by a univariate Cox regression model. Results: The SDI between the automated and the manual volume segmentations was 0·78 and 0·79, respectively. Automated PET/CT measures reflecting total lesion uptake and the relation between volume of abnormal voxels and total prostate volume were significantly associated with overall survival (P = 0·02), whereas age, PSA, and Gleason score were not. Conclusion: Automated PET/CT biomarkers showed good agreement to manual measurements and were significantly associated with overall survival. © 2019 The Authors. Clinical Physiology and Functional Imaging published by John Wiley & Sons Ltd on behalf of Scandinavian Society of Clinical Physiology and Nuclear Medicine
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44.
  • Bondesson, Johan, 1991, et al. (author)
  • Quantification of True Lumen Helical Morphology and Chirality in Type B Aortic Dissections
  • 2021
  • In: American Journal of Physiology - Heart and Circulatory Physiology. - : American Physiological Society. - 1522-1539 .- 0363-6135. ; 320:2, s. H901-H911
  • Journal article (peer-reviewed)abstract
    • Chirality is a fundamental property in many biologic systems. Motivated by previous observations of helical aortic blood flow, aortic tissue fibers, and propagation of aortic dissections, we introduce methods to characterize helical morphology of aortic dissections. After validation on computer generated phantoms, the methods were applied to patients with type B dissection. For this cohort, there was a distinct bimodal distribution of helical propagation of the dissection with either achiral or exclusively right-handed chirality, with no intermediate cases or left-handed cases. This clear grouping indicates that dissection propagation favors these two modes, potentially due to the right-handedness of helical aortic blood flow and cell orientation. The characterization of dissection chirality and quantification of helical morphology advances our understanding of dissection pathology and lays a foundation for applications in clinical research and treatment practice. For example, the chirality and magnitude of helical metrics of dissections may indicate risk of dissection progression, help define treatment and surveillance strategies, and enable development of novel devices that account for various helical morphologies.
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45.
  • Ruffieux, Silvia, 1990 (author)
  • High-temperature superconducting magnetometers for on-scalp MEG
  • 2020
  • Doctoral thesis (other academic/artistic)abstract
    • In the growing field of on-scalp magnetoencephalography (MEG), brain activity is studied by non-invasively mapping the magnetic fields generated by neuronal currents with sensors that are flexibly placed in close proximity to the subject's head. This thesis focuses on high-temperature superconducting magnetometers made from YBa2Cu3Ox-7 (YBCO), which enables a reduction in the sensor-to-room temperature standoff distance from roughly 2 cm (for conventional MEG systems) down to 1 mm. Because of the higher neuromagnetic signal magnitudes available to on-scalp sensors, simulations predict that even a relatively low-sensitivity (higher noise) full-head on-scalp MEG system can extract more information about brain activity than conventional systems. In the first part of this thesis, the development of high critical temperature (high-Tc) superconducting quantum interference device (SQUID) magnetometers for a 7-channel on-scalp MEG system is described. The sensors are single layer magnetometers with a directly coupled pickup loop made on 10 mm × 10 mm substrates using bicrystal grain boundary Josephson junctions. We found that the kinetic inductance strongly varies with film quality and temperature. Determination of all SQUID parameters by combining measurements and inductance simulations led to excellent agreement between experimental results and theoretical predictions. This allowed us to perform an in-depth magnetometer optimization. The best magnetometers achieve a magnetic field noise level of 44 fT/√Hz at 78 K. Fabricated test SQUIDs provide evidence that noise levels below 30 fT/√Hz are possible for high quality junctions with fairly low critical currents and in combination with the optimized pickup loop design. Different feedback methods for operation in a densely-packed on-scalp MEG system were also investigated. Direct injection of current into the SQUID loop was identified as the best on-chip feedback method with feedback flux crosstalk below 0.5%. By reducing the operation temperature, the noise level can be further reduced, however, the effective area also decreases because of the decreasing kinetic inductance contribution. We present a method that allows for one-time sensor calibration independent of temperature. In the second part, the design, operation, and performance of the constructed 7-channel on-scalp MEG system based on the fabricated magnetometers is presented. With a dense (2 mm edge-to-edge) hexagonal head-aligned array, the system achieves a small sensor-to-head standoff distance of 1-3 mm and dense spatial sampling. The magnetic field noise levels are 50-130 fT/√Hz and the sensor-to-sensor feedback flux crosstalk is below 0.6%. MEG measurements with the system demonstrate the feasibility of the approach and indicate that our on-scalp MEG system allows retrieval of information unavailable to conventional MEG. In the third part, two alternative magnetometer types are studied for the next generation system. The first alternative is magnetometers based on Dayem bridge junctions instead of bicrystal grain boundary junctions. With a magnetometer based on the novel grooved Dayem bridge junctions, a magnetic field noise level of 63 fT/√Hz could be achieved, which shows that Dayem bridge junctions are starting to become a viable option for single layer magnetometers. The second alternative are high-Tc SQUID magnetometers with an inductively coupled flux transformer. The best device with bicrystal grain boundary junctions reaches a magnetic field noise level below 11 fT/√Hz and outperforms the best single layer device for frequencies above 20 Hz. In the last part, the potential of kinetic inductance magnetometers (KIMs) is investigated. We demonstrate the first high-Tc KIMs, which can be operated in fields of 9-28 µT and achieve a noise level of 4 pT/√Hz at 10 kHz.
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46.
  • Dartora, Caroline, et al. (author)
  • A deep learning model for brain age prediction using minimally preprocessed T1w images as input
  • 2023
  • In: Frontiers in Aging Neuroscience. - : Frontiers Media SA. - 1663-4365. ; 15
  • Journal article (peer-reviewed)abstract
    • Introduction: In the last few years, several models trying to calculate the biological brain age have been proposed based on structural magnetic resonance imaging scans (T1-weighted MRIs, T1w) using multivariate methods and machine learning. We developed and validated a convolutional neural network (CNN)-based biological brain age prediction model that uses one T1w MRI preprocessing step when applying the model to external datasets to simplify implementation and increase accessibility in research settings. Our model only requires rigid image registration to the MNI space, which is an advantage compared to previous methods that require more preprocessing steps, such as feature extraction. Methods: We used a multicohort dataset of cognitively healthy individuals (age range = 32.0–95.7 years) comprising 17,296 MRIs for training and evaluation. We compared our model using hold-out (CNN1) and cross-validation (CNN2–4) approaches. To verify generalisability, we used two external datasets with different populations and MRI scan characteristics to evaluate the model. To demonstrate its usability, we included the external dataset’s images in the cross-validation training (CNN3). To ensure that our model used only the brain signal on the image, we also predicted brain age using skull-stripped images (CNN4). Results: The trained models achieved a mean absolute error of 2.99, 2.67, 2.67, and 3.08 years for CNN1–4, respectively. The model’s performance in the external dataset was in the typical range of mean absolute error (MAE) found in the literature for testing sets. Adding the external dataset to the training set (CNN3), overall, MAE is unaffected, but individual cohort MAE improves (5.63–2.25 years). Salience maps of predictions reveal that periventricular, temporal, and insular regions are the most important for age prediction. Discussion: We provide indicators for using biological (predicted) brain age as a metric for age correction in neuroimaging studies as an alternative to the traditional chronological age. In conclusion, using different approaches, our CNN-based model showed good performance using one T1w brain MRI preprocessing step. The proposed CNN model is made publicly available for the research community to be easily implemented and used to study ageing and age-related disorders.
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47.
  • Liu, Xixi, 1995, et al. (author)
  • Deep Nearest Neighbors for Anomaly Detection in Chest X-Rays
  • 2024
  • In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - 1611-3349 .- 0302-9743. ; 14349 LNCS, s. 293-302
  • Conference paper (peer-reviewed)abstract
    • Identifying medically abnormal images is crucial to the diagnosis procedure in medical imaging. Due to the scarcity of annotated abnormal images, most reconstruction-based approaches for anomaly detection are trained only with normal images. At test time, images with large reconstruction errors are declared abnormal. In this work, we propose a novel feature-based method for anomaly detection in chest x-rays in a setting where only normal images are provided during training. The model consists of lightweight adaptor and predictor networks on top of a pre-trained feature extractor. The parameters of the pre-trained feature extractor are frozen, and training only involves fine-tuning the proposed adaptor and predictor layers using Siamese representation learning. During inference, multiple augmentations are applied to the test image, and our proposed anomaly score is simply the geometric mean of the k-nearest neighbor distances between the augmented test image features and the training image features. Our method achieves state-of-the-art results on two challenging benchmark datasets, the RSNA Pneumonia Detection Challenge dataset, and the VinBigData Chest X-ray Abnormalities Detection dataset. Furthermore, we empirically show that our method is robust to different amounts of anomalies among the normal images in the training dataset. The code is available at: https://github.com/XixiLiu95/deep-kNN-anomaly-detection.
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48.
  • Pati, Pushpak, et al. (author)
  • Weakly supervised joint whole-slide segmentation and classification in prostate cancer
  • 2023
  • In: Medical Image Analysis. - : Elsevier. - 1361-8415 .- 1361-8423.
  • Journal article (peer-reviewed)abstract
    • The identification and segmentation of histological regions of interest can provide significant support to pathologists in their diagnostic tasks. However, segmentation methods are constrained by the difficulty in obtaining pixel-level annotations, which are tedious and expensive to collect for whole-slide images (WSI). Though several methods have been developed to exploit image-level weak-supervision for WSI classification, the task of segmentation using WSI-level labels has received very little attention. The research in this direction typically require additional supervision beyond image labels, which are difficult to obtain in real-world practice. In this study, we propose WholeSIGHT, a weakly-supervised method that can simultaneously segment and classify WSIs of arbitrary shapes and sizes. Formally, WholeSIGHT first constructs a tissue-graph representation of WSI, where the nodes and edges depict tissue regions and their interactions, respectively. During training, a graph classification head classifies the WSI and produces node-level pseudo-labels via post-hoc feature attribution. These pseudo-labels are then used to train a node classification head for WSI segmentation. During testing, both heads simultaneously render segmentation and class prediction for an input WSI. We evaluate the performance of WholeSIGHT on three public prostate cancer WSI datasets. Our method achieves state-of-the-art weakly-supervised segmentation performance on all datasets while resulting in better or comparable classification with respect to state-of-the-art weakly-supervised WSI classification methods. Additionally, we assess the generalization capability of our method in terms of segmentation and classification performance, uncertainty estimation, and model calibration. Our code is available at: https://github.com/histocartography/wholesight.
  •  
49.
  • Puri, Ashishi, et al. (author)
  • A fractional order-based mixture of central Wishart (FMoCW) model for reconstructing white matter fibers from diffusion MRI
  • 2023
  • In: Psychiatry Research - Neuroimaging. - 0925-4927. ; 333
  • Journal article (peer-reviewed)abstract
    • This paper introduces an algorithm for reconstructing the brain's white matter fibers (WMFs). In particular, a fractional order mixture of central Wishart (FMoCW) model is proposed to reconstruct the WMFs from diffusion MRI data. The pseudo super diffusive modality of anomalous diffusion is coupled with the mixture of central Wishart (MoCW) model to derive the proposed model. We have shown results on multiple synthetic simulations, including fibers orientations in 2 and 3 directions per voxel and experiments on real datasets of rat optic chiasm and a healthy human brain. In synthetic simulations, a varying Rician distributed noise levels, σ=0.01−0.09 is also considered. The proposed model can efficiently distinguish multiple fibers even when the angle of separation between fibers is very small. This model outperformed, giving the least angular error when compared to fractional mixture of Gaussian (MoG), MoCW and mixture of non-central Wishart (MoNCW) models.
  •  
50.
  • Roll, Mikael, et al. (author)
  • Pre-activation negativity (PrAN) : A neural index of predictive strength of phonological cues
  • 2023
  • In: Laboratory Phonology. - : Open Library of the Humanities. - 1868-6354. ; 14:1, s. 1-28
  • Journal article (peer-reviewed)abstract
    • We propose that a recently discovered event-related potential (ERP) component—the pre-activation negativity (PrAN)—indexes the predictive strength of phonological cues, including segments, word tones, and sentence-level tones. Specifically, we argue that PrAN is a reflection of the brain’s anticipation of upcoming speech (segments, morphemes, words, and syntactic structures). Findings from a long series of neurolinguistic studies indicate that the effect can be divided into two time windows with different possible brain sources. Between 136 and 200 ms from stimulus onset, it indexes activity mainly in the primary and secondary auditory cortices, reflecting disinhibition of neurons sensitive to the expected acoustic signal, as indicated by the brain regions’ response to predictive certainty rather than sound salience. After ~200 ms, PrAN is related to activity in Broca’s area, possibly reflecting inhibition of irrelevant segments, morphemes, words, and syntactic structures.
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