| 1. |
- Böhmer, Jens, 1981, et al.
(författare)
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Absolute Quantification of Donor-Derived Cell-Free DNA in Pediatric and Adult Patients After Heart Transplantation: A Prospective Study.
- 2023
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Ingår i: Transplant international : official journal of the European Society for Organ Transplantation. - 0934-0874 .- 1432-2277. ; 36
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Tidskriftsartikel (refereegranskat)abstract
- In this prospective study we investigated a cohort after heart transplantation with a novel PCR-based approach with focus on treated rejection. Blood samples were collected coincidentally to biopsies, and both absolute levels of dd-cfDNA and donor fraction were reported using digital PCR. 52 patients (11 children and 41 adults) were enrolled (NCT03477383, clinicaltrials.gov), and 557 plasma samples were analyzed. 13 treated rejection episodes >14days after transplantation were observed in 7 patients. Donor fraction showed a median of 0.08% in the cohort and was significantly elevated during rejection (median 0.19%, p < 0.0001), using a cut-off of 0.1%, the sensitivity/specificity were 92%/56% (AUC ROC-curve: 0.78). Absolute levels of dd-cfDNA showed a median of 8.8 copies/mL and were significantly elevated during rejection (median 23, p = 0.0001). Using a cut-off of 7.5 copies/mL, the sensitivity/specificity were 92%/43% for donor fraction (AUC ROC-curve: 0.75). The results support the feasibility of this approach in analyzing dd-cfDNA after heart transplantation. The obtained values are well aligned with results from other trials. The possibility to quantify absolute levels adds important value to the differentiation between ongoing graft damage and quiescent situations.
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| 2. |
- Javeed, Ashir, 1989-, et al.
(författare)
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Decision Support System for Predicting Mortality in Cardiac Patients Based on Machine Learning
- 2023
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Ingår i: Applied Sciences. - : MDPI. - 2076-3417. ; 13:8
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Tidskriftsartikel (refereegranskat)abstract
- Researchers have proposed several automated diagnostic systems based on machine learning and data mining techniques to predict heart failure. However, researchers have not paid close attention to predicting cardiac patient mortality. We developed a clinical decision support system for predicting mortality in cardiac patients to address this problem. The dataset collected for the experimental purposes of the proposed model consisted of 55 features with a total of 368 samples. We found that the classes in the dataset were highly imbalanced. To avoid the problem of bias in the machine learning model, we used the synthetic minority oversampling technique (SMOTE). After balancing the classes in the dataset, the newly proposed system employed a (Formula presented.) statistical model to rank the features from the dataset. The highest-ranked features were fed into an optimized random forest (RF) model for classification. The hyperparameters of the RF classifier were optimized using a grid search algorithm. The performance of the newly proposed model ((Formula presented.) _RF) was validated using several evaluation measures, including accuracy, sensitivity, specificity, F1 score, and a receiver operating characteristic (ROC) curve. With only 10 features from the dataset, the proposed model (Formula presented.) _RF achieved the highest accuracy of 94.59%. The proposed model (Formula presented.) _RF improved the performance of the standard RF model by 5.5%. Moreover, the proposed model (Formula presented.) _RF was compared with other state-of-the-art machine learning models. The experimental results show that the newly proposed decision support system outperforms the other machine learning systems using the same feature selection module ((Formula presented.)).
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| 3. |
- Wang, Chunliang, 1980-, et al.
(författare)
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An interactive software module for visualizing coronary arteries in CT angiography
- 2008
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Ingår i: International Journal of Computer Assisted Radiology and Surgery. - Heidelberg/Berlin : Springer. - 1861-6410 .- 1861-6429. ; 3:1-2, s. 11-18
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Tidskriftsartikel (refereegranskat)abstract
- A new software module for coronary artery segmentation and visualization in CT angiography (CTA) datasets is presented, which aims to interactively segment coronary arteries and visualize them in 3D with maximum intensity projection (MIP) and volume rendering (VRT).Materials and Methods: The software was built as a plug-in for the open-source PACS workstation OsiriX. The main segmentation function is based an optimized “virtual contrast injection” algorithm, which uses fuzzy connectedness of the vessel lumen to separate the contrast-filled structures from each other. The software was evaluated in 42 clinical coronary CTA datasets acquired with 64-slice CT using isotropic voxels of 0.3–0.5 mm.Results: The median processing time was 6.4 min, and 100% of main branches (right coronary artery, left circumflex artery and left anterior descending artery) and 86.9% (219/252) of visible minor branches were intact. Visually correct centerlines were obtained automatically in 94.7% (321/339) of the intact branches.Conclusion: The new software is a promising tool for coronary CTA post-processing providing good overviews of the coronary artery with limited user interaction on low-end hardware, and the coronary CTA diagnosis procedure could potentially be more time-efficient than using thin-slab technique.
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| 4. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Integrating automatic and interactive method for coronary artery segmentation : let PACS workstation think ahead
- 2010
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Ingår i: International Journal of Computer Assisted Radiology and Surgery. - : Springer Science and Business Media LLC. - 1861-6410 .- 1861-6429. ; 5:3, s. 275-285
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To provide an efficient method to extract useful information from the increasing amount of coronary CTA.Methods: A quantitative coronary CTA analysis tool was built on OsiriX, which integrates both fully automatic and interactive methods for coronary artery extraction. The computational power of an ordinary PC is exploited by running the non-supervised coronary artery segmentation and centerline tracking in the background as soon as the images are received. When the user opens the data, the software provides a real-time interactive analysis environment.Results: The average overlap between the centerline created in our software and the reference standard was 96.0%. The average distance between them was 0.38 mm. The automatic procedure runs for 3-5 min as a single-thread application in background. Interactive processing takes 3 min in average.Conclusion: In preliminary experiments, the software achieved higher efficiency than the former interactive method, and reasonable accuracy compared to manual vessel extraction.
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| 5. |
- Liu, Xixi, 1995, et al.
(författare)
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Deep Nearest Neighbors for Anomaly Detection in Chest X-Rays
- 2024
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Ingår i: 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
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Konferensbidrag (refereegranskat)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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| 6. |
- Wang, Chunliang, 1980-
(författare)
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Computer Assisted Coronary CT Angiography Analysis : Disease-centered Software Development
- 2009
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- The substantial advances of coronary CTA have resulted in a boost of use of this new technique in the last several years, which brings a big challenge to radiologists by the increasing number of exams and the large amount of data for each patient. The main goal of this study was to develop a computer tool to facilitate coronary CTA analysis by combining knowledge of medicine and image processing.Firstly, a competing fuzzy connectedness tree algorithm was developed to segment the coronary arteries and extract centerlines for each branch. The new algorithm, which is an extension of the “virtual contrast injection” method, preserves the low density soft tissue around the coronary, which reduces the possibility of introducing false positive stenoses during segmentation.Secondly, this algorithm was implemented in open source software in which multiple visualization techniques were integrated into an intuitive user interface to facilitate user interaction and provide good over¬views of the processing results. Considerable efforts were put on optimizing the computa¬tional speed of the algorithm to meet the clinical requirements.Thirdly, an automatic seeding method, that can automatically remove rib cage and recognize the aortic root, was introduced into the interactive segmentation workflow to further minimize the requirement of user interactivity during post-processing. The automatic procedure is carried out right after the images are received, which saves users time after they open the data. Vessel enhance¬ment and quantitative 2D vessel contour analysis are also included in this new version of the software. In our preliminary experience, visually accurate segmentation results of major branches have been achieved in 74 cases (42 cases reported in paper II and 32 cases in paper III) using our software with limited user interaction. On 128 branches of 32 patients, the average overlap between the centerline created in our software and the manually created reference standard was 96.0%. The average distance between them was 0.38 mm, lower than the mean voxel size. The automatic procedure ran for 3-5 min as a single-thread application in the background. Interactive processing took 3 min in average with the latest version of software. In conclusion, the presented software provides fast and automatic coron¬ary artery segmentation and visualization. The accuracy of the centerline tracking was found to be acceptable when compared to manually created centerlines.
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| 7. |
- Baldanzi, Gabriel, et al.
(författare)
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OSA Is Associated With the Human Gut Microbiota Composition and Functional Potential in the Population-Based Swedish CardioPulmonary bioImage Study
- 2023
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Ingår i: Chest. - : Elsevier. - 0012-3692 .- 1931-3543. ; 164:2, s. 503-516
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Obstructive sleep apnea (OSA) is a common sleep-breathing disorder linked to increased risk of cardiovascular disease. Intermittent hypoxia and intermittent airway obstruction, hallmarks of OSA, have been shown in animal models to induce substantial changes to the gut microbiota composition and subsequent transplantation of fecal matter to other animals induced changes in blood pressure and glucose metabolism.RESEARCH QUESTION: Does obstructive sleep apnea in adults associate with the composition and metabolic potential of the human gut microbiota?STUDY DESIGN AND METHODS: We used respiratory polygraphy data from up to 3,570 individuals aged 50-64 from the population-based Swedish CardioPulmonary bioImage Study combined with deep shotgun metagenomics of fecal samples to identify cross-sectional associations between three OSA parameters covering apneas and hypopneas, cumulative sleep time in hypoxia and number of oxygen desaturation events with gut microbiota composition. Data collection about potential confounders was based on questionnaires, on-site anthropometric measurements, plasma metabolomics, and linkage with the Swedish Prescribed Drug Register.RESULTS: We found that all three OSA parameters were associated with lower diversity of species in the gut. Further, the OSA-related hypoxia parameters were in multivariable-adjusted analysis associated with the relative abundance of 128 gut bacterial species, including higher abundance of Blautia obeum and Collinsela aerofaciens. The latter species was also independently associated with increased systolic blood pressure. Further, the cumulative time in hypoxia during sleep was associated with the abundance of genes involved in nine gut microbiota metabolic pathways, including propionate production from lactate. Lastly, we observed two heterogeneous sets of plasma metabolites with opposite association with species positively and negatively associated with hypoxia parameters, respectively.INTERPRETATION: OSA-related hypoxia, but not the number of apneas/hypopneas, is associated with specific gut microbiota species and functions. Our findings lay the foundation for future research on the gut microbiota-mediated health effects of OSA.
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| 8. |
- Hagberg, Eva, et al.
(författare)
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Semi-supervised learning with natural language processing for right ventricle classification in echocardiography—a scalable approach
- 2022
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Ingår i: Computers in Biology and Medicine. - : Elsevier BV. - 0010-4825 .- 1879-0534. ; 143
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Tidskriftsartikel (refereegranskat)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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| 9. |
- Gonzalez, Manuel, et al.
(författare)
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Effect of a lifestyle-focused electronic patient support application for improving risk factor management, self-rated health, and prognosis in post-myocardial infarction patients : study protocol for a multi-center randomized controlled trial
- 2019
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Ingår i: Trials. - : Springer Science and Business Media LLC. - 1745-6215. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Cardiac rehabilitation (CR) programs addressing risk factor management, educational interventions, and exercise contribute to reduce mortality after myocardial infarction (MI). However, the fulfillment of guideline-recommended CR targets is currently unsatisfactory. eHealth, i.e., the use of electronic communication for healthcare, including the use of mobile smartphone applications combined with different sensors and interactive computerized programs, offers a new array of possibilities to provide clinical care. The present study aims to assess the efficacy of a web-based application (app) designed to support persons in adhering to lifestyle advice and medication as a complement to traditional CR programs for improvement of risk factors and clinical outcomes in patients with MI compared with usual care. METHODS/DESIGN: An open-label multi-center randomized controlled trial is being conducted at different CR centers from three Swedish University Hospitals. The aim is to include 150 patients with MI < 75 years of age who are confident smartphone and/or Internet users. In addition to participation in CR programs according to the usual routine at each center, patients randomized to the intervention arm will receive access to the web-based app. A CR nurse reviews the patients' self-reported data twice weekly through a medical interface at the clinic. The primary outcome of the study will be change in submaximal exercise capacity (in watts) between 2 and 4 weeks after discharge and when the patient has completed his/her exercise program at the CR center, usually around 3-6 months post-discharge. Secondary outcomes include changes in self-reported physical activity, objectively assessed physical activity by accelerometry, self-rated health, dietary, and smoking habits, body mass index, blood pressure, blood lipids, and glucose/HbA1c levels between inclusion and follow-up visits during the first year post-MI. Additionally, we will assess uptake and adherence to the application, the number of CR staff contacts, and the incidence of cardiovascular events at 1 and 3 years after the MI. Patient recruitment started in 2016, and the first study results are expected in the beginning of 2019. DISCUSSION: The present study will add evidence to whether electronic communication can be used to improve traditional CR programs for patients after MI. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03260582 . Retrospectively registered on 24 August 2017.
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| 10. |
- Belda, E., et al.
(författare)
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Impairment of gut microbial biotin metabolism and host biotin status in severe obesity: effect of biotin and prebiotic supplementation on improved metabolism
- 2022
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Ingår i: Gut. - : BMJ. - 0017-5749 .- 1468-3288. ; 71:12
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Tidskriftsartikel (refereegranskat)abstract
- Objectives Gut microbiota is a key component in obesity and type 2 diabetes, yet mechanisms and metabolites central to this interaction remain unclear. We examined the human gut microbiome's functional composition in healthy metabolic state and the most severe states of obesity and type 2 diabetes within the MetaCardis cohort. We focused on the role of B vitamins and B7/B8 biotin for regulation of host metabolic state, as these vitamins influence both microbial function and host metabolism and inflammation. Design We performed metagenomic analyses in 1545 subjects from the MetaCardis cohorts and different murine experiments, including germ-free and antibiotic treated animals, faecal microbiota transfer, bariatric surgery and supplementation with biotin and prebiotics in mice. Results Severe obesity is associated with an absolute deficiency in bacterial biotin producers and transporters, whose abundances correlate with host metabolic and inflammatory phenotypes. We found suboptimal circulating biotin levels in severe obesity and altered expression of biotin-associated genes in human adipose tissue. In mice, the absence or depletion of gut microbiota by antibiotics confirmed the microbial contribution to host biotin levels. Bariatric surgery, which improves metabolism and inflammation, associates with increased bacterial biotin producers and improved host systemic biotin in humans and mice. Finally, supplementing high-fat diet-fed mice with fructo-oligosaccharides and biotin improves not only the microbiome diversity, but also the potential of bacterial production of biotin and B vitamins, while limiting weight gain and glycaemic deterioration. Conclusion Strategies combining biotin and prebiotic supplementation could help prevent the deterioration of metabolic states in severe obesity.
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