Analysis of Acute Respiratory Distress Syndrome using Machine Learning
Harika Koheda, Dr. Vinoda Reddy
Assistant Professor, Department of CSE, Malla Reddy Institute of Technology and Science (Autonomous), Hyderabad.
Associate Professor, Dept. of CSE(AIML), CMR Technical Campus, Hyderabad.
Abstract: Acute respiratory distress syndrome (ARDS) is a common but not well known critical disease condition that is linked to a high death rate. The fact that chest x-rays for ARDS are not always interpreted the same way is a big reason why it is not recognised more often. We wanted to teach a deep convolutional neural network (CNN) how to find signs of ARDS on chest x-rays. CNNs were first trained on 595,506 chest x-rays from two places to find common chest results like opacity and effusion. They were then trained on 8072 chest x-rays that had been marked for ARDS by several doctors using different transfer learning methods. The best CNN was tried on chest x-rays from both an internal and external group. Six doctors, including a chest radiologist and intensive care medicine specialists, looked over a subset of the images. Four hospitals in the US provided chest x-ray statistics. A CNN was able to find ARDS in 1560 chest x-rays from 455 patients with acute hypoxemic respiratory failure, with an area under the receiver operator characteristics curve (AUROC) of 0.92% (95% CI 0.89% to 0.94%). Its AUROC was 0.933 (95% CI 0.888–0.996) for the subset of 413 pictures looked at by at least six doctors, and its sensitivity was 83% (95% CI 74.0% to 91.1%). The AUROC was 0.93% (0.92–0.95%) when images marked as "equivocal" were taken out of the analysis. Chest x-rays can be used to train a CNN to perform as well as a doctor at finding ARDS. More study is needed to see how well these algorithms work for finding ARDS patients in real time so that evidence-based care is followed or to help with current ARDS research.
Keywords: Machine learning, support vector machine, label uncertainty, acute respiratory distress syndrome, sampling from longitudinal electronic health records (EHR).
