| 1. |
- Askander, Mikael, et al.
(författare)
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Lady Gaga : scenerna, medierna, fansen
- 2014
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Ingår i: Coda: en andra antologi om musik och samhälle.
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Bokkapitel (refereegranskat)abstract
- This is a mapping of my upcoming project on the field of short culture phenomena. One part of that will deal with the music videos of Lady Gaga. In this book chapter I present som basic information on Lady Gaga, and suggestions on how to approach the artist and her artistic/aesthetic expressions. Scenes, stage performance, media, and the different relation to the fans is put into focus, and I mean that one must both take all these aspects into account, and deal with them as combined/integrated parts of thw whole, when trying to understand Lady Gagas world of ideas.
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| 2. |
- Boissin, Constance, et al.
(författare)
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Development and evaluation of deep learning algorithms for assessment of acute burns and the need for surgery
- 2023
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Ingår i: Scientific Reports. - : Springer Nature. - 2045-2322. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- Assessment of burn extent and depth are critical and require very specialized diagnosis. Automated image-based algorithms could assist in performing wound detection and classification. We aimed to develop two deep-learning algorithms that respectively identify burns, and classify whether they require surgery. An additional aim assessed the performances in different Fitzpatrick skin types. Annotated burn (n = 1105) and background (n = 536) images were collected. Using a commercially available platform for deep learning algorithms, two models were trained and validated on 70% of the images and tested on the remaining 30%. Accuracy was measured for each image using the percentage of wound area correctly identified and F1 scores for the wound identifier; and area under the receiver operating characteristic (AUC) curve, sensitivity, and specificity for the wound classifier. The wound identifier algorithm detected an average of 87.2% of the wound areas accurately in the test set. For the wound classifier algorithm, the AUC was 0.885. The wound identifier algorithm was more accurate in patients with darker skin types; the wound classifier was more accurate in patients with lighter skin types. To conclude, image-based algorithms can support the assessment of acute burns with relatively good accuracy although larger and different datasets are needed.
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| 4. |
- Erlinge, D., et al.
(författare)
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Bivalirudin versus Heparin Monotherapy in Myocardial Infarction
- 2017
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Ingår i: New England Journal of Medicine. - : Massachusetts Medical Society. - 0028-4793 .- 1533-4406. ; 377:12, s. 1132-1142
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Tidskriftsartikel (refereegranskat)abstract
- Background The comparative efficacy of various anticoagulation strategies has not been clearly established in patients with acute myocardial infarction who are undergoing percutaneous coronary intervention (PCI) according to current practice, which includes the use of radial-artery access for PCI and administration of potent P2Y12 inhibitors without the planned use of glycoprotein IIb/IIIa inhibitors. Methods In this multicenter, randomized, registry-based, open-label clinical trial, we enrolled patients with either ST-segment elevation myocardial infarction (STEMI) or non-STEMI (NSTEMI) who were undergoing PCI and receiving treatment with a potent P2Y12 inhibitor (ticagrelor, prasugrel, or cangrelor) without the planned use of glycoprotein IIb/IIIa inhibitors. The patients were randomly assigned to receive bivalirudin or heparin during PCI, which was performed predominantly with the use of radial-artery access. The primary end point was a composite of death from any cause, myocardial infarction, or major bleeding during 180 days of follow-up. Results A total of 6006 patients (3005 with STEMI and 3001 with NSTEMI) were enrolled in the trial. At 180 days, a primary end-point event had occurred in 12.3% of the patients (369 of 3004) in the bivalirudin group and in 12.8% (383 of 3002) in the heparin group (hazard ratio, 0.96; 95% confidence interval [CI], 0.83 to 1.10; P=0.54). The results were consistent between patients with STEMI and those with NSTEMI and across other major subgroups. Myocardial infarction occurred in 2.0% of the patients in the bivalirudin group and in 2.4% in the heparin group (hazard ratio, 0.84; 95% CI, 0.60 to 1.19; P=0.33), major bleeding in 8.6% and 8.6%, respectively (hazard ratio, 1.00; 95% CI, 0.84 to 1.19; P=0.98), definite stent thrombosis in 0.4% and 0.7%, respectively (hazard ratio, 0.54; 95% CI, 0.27 to 1.10; P=0.09), and death in 2.9% and 2.8%, respectively (hazard ratio, 1.05; 95% CI, 0.78 to 1.41; P=0.76). Conclusions Among patients undergoing PCI for myocardial infarction, the rate of the composite of death from any cause, myocardial infarction, or major bleeding was not lower among those who received bivalirudin than among those who received heparin monotherapy. (Funded by the Swedish Heart-Lung Foundation and others; VALIDATE-SWEDEHEART ClinicalTrialsRegister.eu number, 2012-005260-10 ; ClinicalTrials.gov number, NCT02311231 .).
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| 6. |
- Holmstrom, Oscar, et al.
(författare)
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Detection of breast cancer lymph node metastases in frozen sections with a point-of care low-cost microscope scanner
- 2019
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 14:3
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Tidskriftsartikel (refereegranskat)abstract
- Background Detection of lymph node metastases is essential in breast cancer diagnostics and staging, affecting treatment and prognosis. lntraoperative microscopy analysis of sentinel lymph node frozen sections is standard for detection of axillary metastases but requires access to a pathologist for sample analysis. Remote analysis of digitized samples is an alternative solution but is limited by the requirement for high-end slide scanning equipment.Objective To determine whether the image quality achievable with a low-cost, miniature digital microscope scanner is sufficient for detection of metastases in breast cancer lymph node frozen sections.Methods Lymph node frozen sections from 79 breast cancer patients were digitized using a prototype miniature microscope scanner and a high-end slide scanner. Images were independently reviewed by two pathologists and results compared between devices with conventional light microscopy analysis as ground truth.Results Detection of metastases in the images acquired with the miniature scanner yielded an overall sensitivity of 91% and specificity of 99% and showed strong agreement when compared to light microscopy (k = 0.91). Strong agreement was also observed when results were compared to results from the high-end slide scanner (k = 0.94). A majority of discrepant cases were micrometastases and sections of which no anticytokeratin staining was available.ConclusionAccuracy of detection of metastatic cells in breast cancer sentinel lymph node frozen sections by visual analysis of samples digitized using low-cost, point-of-care microscopy is comparable to analysis of digital samples scanned using a high-end, whole slide scanner. This technique could potentially provide a workflow for digital diagnostics in resource-limited settings, facilitate sample analysis at the point-of-care and reduce the need for trained experts on-site during surgical procedures.
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| 7. |
- Holmström, Oscar, et al.
(författare)
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A novel deep learning-based point-of-care diagnostic method for detecting Plasmodium falciparum with fluorescence digital microscopy.
- 2020
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:11
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Malaria remains a major global health problem with a need for improved field-usable diagnostic tests. We have developed a portable, low-cost digital microscope scanner, capable of both brightfield and fluorescence imaging. Here, we used the instrument to digitize blood smears, and applied deep learning (DL) algorithms to detect Plasmodium falciparum parasites.METHODS: Thin blood smears (n = 125) were collected from patients with microscopy-confirmed P. falciparum infections in rural Tanzania, prior to and after initiation of artemisinin-based combination therapy. The samples were stained using the 4',6-diamidino-2-phenylindole fluorogen and digitized using the prototype microscope scanner. Two DL algorithms were trained to detect malaria parasites in the samples, and results compared to the visual assessment of both the digitized samples, and the Giemsa-stained thick smears.RESULTS: Detection of P. falciparum parasites in the digitized thin blood smears was possible both by visual assessment and by DL-based analysis with a strong correlation in results (r = 0.99, p < 0.01). A moderately strong correlation was observed between the DL-based thin smear analysis and the visual thick smear-analysis (r = 0.74, p < 0.01). Low levels of parasites were detected by DL-based analysis on day three following treatment initiation, but a small number of fluorescent signals were detected also in microscopy-negative samples.CONCLUSION: Quantification of P. falciparum parasites in DAPI-stained thin smears is feasible using DL-supported, point-of-care digital microscopy, with a high correlation to visual assessment of samples. Fluorescent signals from artefacts in samples with low infection levels represented the main challenge for the digital analysis, thus highlighting the importance of minimizing sample contaminations. The proposed method could support malaria diagnostics and monitoring of treatment response through automated quantification of parasitaemia and is likely to be applicable also for diagnostics of other Plasmodium species and other infectious diseases.
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| 8. |
- Holmström, Oscar, et al.
(författare)
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Point-of-Care Digital Cytology With Artificial Intelligence for Cervical Cancer Screening in a Resource-Limited Setting
- 2021
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Ingår i: JAMA Network Open. - : American Medical Association (AMA). - 2574-3805. ; 4:3
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Tidskriftsartikel (refereegranskat)abstract
- Importance: Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programs. The creation of a diagnostic system to digitize Papanicolaou test samples and analyze them using a cloud-based deep learning system (DLS) may provide needed cervical cancer screening to resource-limited areas.Objective: To determine whether artificial intelligence-supported digital microscopy diagnostics can be implemented in a resource-limited setting and used for analysis of Papanicolaou tests.Design, Setting, and Participants: In this diagnostic study, cervical smears from 740 HIV-positive women aged between 18 and 64 years were collected between September 1, 2018, and September 30, 2019. The smears were digitized with a portable slide scanner, uploaded to a cloud server using mobile networks, and used to train and validate a DLS for the detection of atypical cervical cells. This single-center study was conducted at a local health care center in rural Kenya.Exposures: Detection of squamous cell atypia in the digital samples by analysis with the DLS.Main Outcomes and Measures: The accuracy of the DLS in the detection of low- and high-grade squamous intraepithelial lesions in Papanicolaou test whole-slide images.Results: Papanicolaou test results from 740 HIV-positive women (mean [SD] age, 41.8 [10.3] years) were collected. The DLS was trained using 350 whole-slide images and validated on 361 whole-slide images (average size, 100 387 x 47 560 pixels). For detection of cervical cellular atypia, sensitivities were 95.7% (95% CI, 85.5%-99.5%) and 100% (95% CI, 82.4%-100%), and specificities were 84.7% (95% CI, 80.2%-88.5%) and 78.4% (95% CI, 73.6%-82.4%), compared with the pathologist assessment of digital and physical slides, respectively. Areas under the receiver operating characteristic curve were 0.94 and 0.96, respectively. Negative predictive values were high (99%-100%), and accuracy was high, particularly for the detection of high-grade lesions. Interrater agreement was substantial compared with the pathologist assessment of digital slides (kappa = 0.72) and fair compared with the assessment of glass slides (kappa = 0.36). No samples that were classified as high grade by manual sample analysis had false-negative assessments by the DLS.Conclusions and Relevance: In this study, digital microscopy with artificial intelligence was implemented at a rural clinic and used to detect atypical cervical smears with a high sensitivity compared with visual sample analysis.
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| 9. |
- Holmström, Oscar, et al.
(författare)
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Point-of-care mobile digital microscopy and deep learning for the detection of soil-transmitted helminths and Schistosoma haematobium
- 2017
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Ingår i: Global Health Action. - : Informa UK Limited. - 1654-9716 .- 1654-9880. ; 10:sup3
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Microscopy remains the gold standard in the diagnosis of neglected tropical diseases. As resource limited, rural areas often lack laboratory equipment and trained personnel, new diagnostic techniques are needed. Low-cost, point-of-care imaging devices show potential in the diagnosis of these diseases. Novel, digital image analysis algorithms can be utilized to automate sample analysis.OBJECTIVE: Evaluation of the imaging performance of a miniature digital microscopy scanner for the diagnosis of soil-transmitted helminths and Schistosoma haematobium, and training of a deep learning-based image analysis algorithm for automated detection of soil-transmitted helminths in the captured images.METHODS: A total of 13 iodine-stained stool samples containing Ascaris lumbricoides, Trichuris trichiura and hookworm eggs and 4 urine samples containing Schistosoma haematobium were digitized using a reference whole slide-scanner and the mobile microscopy scanner. Parasites in the images were identified by visual examination and by analysis with a deep learning-based image analysis algorithm in the stool samples. Results were compared between the digital and visual analysis of the images showing helminth eggs.RESULTS: Parasite identification by visual analysis of digital slides captured with the mobile microscope was feasible for all analyzed parasites. Although the spatial resolution of the reference slide-scanner is higher, the resolution of the mobile microscope is sufficient for reliable identification and classification of all parasites studied. Digital image analysis of stool sample images captured with the mobile microscope showed high sensitivity for detection of all helminths studied (range of sensitivity = 83.3-100%) in the test set (n = 217) of manually labeled helminth eggs.CONCLUSIONS: In this proof-of-concept study, the imaging performance of a mobile, digital microscope was sufficient for visual detection of soil-transmitted helminths and Schistosoma haematobium. Furthermore, we show that deep learning-based image analysis can be utilized for the automated detection and classification of helminths in the captured images.
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| 10. |
- Linder, Nina, et al.
(författare)
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Deep learning for detecting tumour-infiltrating lymphocytes in testicular germ cell tumours
- 2019
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Ingår i: Journal of Clinical Pathology. - : BMJ Publishing Group Ltd. - 0021-9746 .- 1472-4146. ; 72:2, s. 157-164
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Tidskriftsartikel (refereegranskat)abstract
- AIMS: To evaluate if a deep learning algorithm can be trained to identify tumour-infiltrating lymphocytes (TILs) in tissue samples of testicular germ cell tumours and to assess whether the TIL counts correlate with relapse status of the patient.METHODS: TILs were manually annotated in 259 tumour regions from 28 whole-slide images (WSIs) of H&E-stained tissue samples. A deep learning algorithm was trained on half of the regions and tested on the other half. The algorithm was further applied to larger areas of tumour WSIs from 89 patients and correlated with clinicopathological data.RESULTS: A correlation coefficient of 0.89 was achieved when comparing the algorithm with the manual TIL count in the test set of images in which TILs were present (n=47). In the WSI regions from the 89 patient samples, the median TIL density was 1009/mm2. In seminomas, none of the relapsed patients belonged to the highest TIL density tertile (>2011/mm2). TIL quantifications performed visually by three pathologists on the same tumours were not significantly associated with outcome. The average interobserver agreement between the pathologists when assigning a patient into TIL tertiles was 0.32 (Kappa test) compared with 0.35 between the algorithm and the experts, respectively. A higher TIL density was associated with a lower clinical tumour stage, seminoma histology and lack of lymphovascular invasion.CONCLUSIONS: Deep learning-based image analysis can be used for detecting TILs in testicular germ cell cancer more objectively and it has potential for use as a prognostic marker for disease relapse.
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