Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.

ABSTRACT: Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or 'equivocal' images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the 'equivocal' class using an uncertainty-aware label sm

SUBMITTER: Arora M 

PROVIDER: S-EPMC10451804 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature