Computer Vision Technologies for Rapid Detection of the Acute Respiratory Distress Syndrome
Abstract
This report summarizes the progress made over the first year on the grant: W81XWH2010496/Computer Vision Technologies for Rapid Detection of the Acute Respiratory Distress Syndrome. The Acute Respiratory Distress Syndrome (ARDS) is a critical illness syndrome with a 35 percent mortality rate. We proposed to develop computer vision technologies powered by deep convolutional neural networks to automatically identify chest x-ray findings consistent with ARDS with expert-level accuracy. During the first year of the grant, we successfully finalized development of the initial deep learning model, demonstrating its physician-expert level performance on detecting ARDS within a cohort of patients from the University of Michigan and University of Pennsylvania. This work was published in the journal Lancet: Digital Health. We also made substantial progress toward incorporating a lung- segmentation algorithm into our chest x-ray processing pipeline. Overall, we are on track towards completion of all aspects of grant.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2021
- Accession Number
- AD1149397
Entities
People
- Michael W. Sjoding
Organizations
- Board of Regents of the University of Michigan