Proceedings on Automation in Medical Engineering
Vol. 2 No. 1 (2023): Proc AUTOMED

Applications in medicine, ID 742

Implementation of ResNet-50 for the Detection of ARDS in Chest X-Rays using transfer-learning

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Simon Fonck (Informatik 11 - Embedded Software), Sebastian Fritsch (Department of Intensive Care Medicine, University Hospital Aachen), Gina Nottenkämper (Informatik 11 - Embedded Software), André Stollenwerk (Informatik 11 - Embedded Software)


Acute 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 AUC score of 92.6%, a sensitivity of 87% and a specificity of 97%.

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