Both non-private baseline models achieved an area under the ROC curve (AUC) of 0. 94 on the binary classification task of detecting the presence of a medical finding.
Chest X-ray is one of the most widespread examinations of the human body.
The automatic detection of critical findings in chest X-rays (CXR), such as pneumothorax, is important for assisting radiologists in their clinical workflow like triaging time-sensitive cases and screening for incidental findings.
Our simulations demonstrate that smart worklist prioritization by AI can reduce the average RTAT for critical findings in CXRs while maintaining a small maximum RTAT as FIFO.
Over the last years, Deep Learning has been successfully applied to a broad range of medical applications.
Pneumothorax is a critical condition that requires timely communication and immediate action.
Chest radiography is the most common clinical examination type.
The increased availability of X-ray image archives (e. g. the ChestX-ray14 dataset from the NIH Clinical Center) has triggered a growing interest in deep learning techniques.
Convolutional neural networks demonstrated outstanding empirical results in computer vision and speech recognition tasks where labeled training data is abundant.