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001006738 1001_ $$0P:(DE-HGF)0$$aFonck, Simon$$b0$$eCorresponding author
001006738 1112_ $$a16. Interdisziplinäres Symposium Automatisierungstechnische Verfahren für die Medizintechnik$$cGießen$$d2023-03-30 - 2023-03-31$$gAUTOMED$$wGermany
001006738 245__ $$aImplementation of ResNet-50 for the Detection of ARDS in Chest X-Rays using transfer-learning
001006738 260__ $$aLübeck$$bInfinite Science GmbH$$c2023
001006738 300__ $$aID 742
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001006738 520__ $$aAcute Respiratory Distress Syndrome is a severe condition with high morbidity and mortality. The current standard for the diagnosis of ARDS was proposed by the Berlin-Definition in 2012. However, studies have shown, that ARDS is often recognized too late or not at all. Smart methods, like machine learning algorithms, may help clinicians to identify ARDS earlier and therefore initiate the appropriate therapy. To address the imaging assessment of the Berlin-Definition, a deep learning model for the detection of ARDS in x-rays is proposed. The model achieved an AUCscore of 92.6%, a sensitivity of 87% and a specificity of 97%.
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001006738 7001_ $$0P:(DE-Juel1)185651$$aFritsch, Sebastian$$b1$$ufzj
001006738 7001_ $$0P:(DE-HGF)0$$aNottenkämper, Gina$$b2
001006738 7001_ $$0P:(DE-HGF)0$$aStollenwerck, Andre$$b3
001006738 773__ $$0PERI:(DE-600)3023403-7$$n1$$pID 742$$tProceedings on automation in medical engineering$$v2$$y2023
001006738 8564_ $$uhttps://www.journals.infinite-science.de/index.php/automed/article/view/742
001006738 8564_ $$uhttps://juser.fz-juelich.de/record/1006738/files/ResNet-50%20for%20the%20Detection%20of%20ARDS.pdf$$yOpenAccess
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001006738 9141_ $$y2023
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