Computer Vision Technologies for Rapid Detection of the Acute Respiratory Distress Syndrome

Abstract

This report summarizes the progress made over the second 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% 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 second year of the grant, we performed several computational analyses to improve our published ARDS model including 1) increasing the model pre-training time, 2) investigating the effect of training a model on larger chest x-ray images, 3) incorporating a lung-segmentation algorithm into our chest x-ray processing pipeline. Of the three strategies, increasing training time resulted in the largest improvement in model validation performance over our base model.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2022
Accession Number
AD1195571

Entities

People

  • Michael W. Sjoding

Organizations

  • University of Michigan

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Acute Respiratory Distress Syndrome
  • Artificial Intelligence
  • Artificial Intelligence Computing
  • Artificial Intelligence Software
  • Automata Theory
  • Biomedical Research
  • Computer Science
  • Computer Vision
  • Computers
  • Convolutional Neural Networks
  • Deep Learning
  • Health Care
  • Health Services
  • Machine Learning
  • Medical Personnel
  • Michigan
  • Neural Networks
  • Patient Care
  • Physicians
  • Standards
  • Training
  • X Rays

Readers

  • Neural Network Machine Learning.
  • Toxicology/Environmental Toxicology
  • Trauma Surgery or Emergency Medicine.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks