Acute respiratory distress syndrome (ARDS) diagnosis by image processing – UROP Spring Symposium 2021

Acute respiratory distress syndrome (ARDS) diagnosis by image processing

Allen Li

Allen Li

Pronouns: he/him/his

Research Mentor(s): Kayvan Najarian, Professor
Research Mentor School/College/Department: Department of Computational Medicine & Bioinformatics, Michigan Medicine
Presentation Date: Thursday, April 22, 2021
Session: Session 3 (1pm-1:50pm)
Breakout Room: Room 15
Presenter: 7

Event Link


Despite the vast amount of data available from hospitals on ARDS, much of it is left unused because working with all the available data can be slow and often inconsistent. This project focuses on chest x-ray scans, aiming to turn chest x-rays into privileged information (information that is not always available but can be useful) in order to improve training and increase the accuracy of ARDS classification when available. The project was done largely in Python with the exception of the segmentation algorithm (written in MATLAB), and classification training and accuracy tests for different features were done using a large amount of data from Michigan Medicine. My project’s findings have yielded a 0.72 AUC average score when doing binary classification on chest X-Rays using features extracted from image processing. Privileged information for machine learning is a very important step in ensuring that we harness all the data available, and my project’s findings will help inform future models and serve to show an extra pathway to improve classification accuracy.

Authors: Allen Li, Narathip Reamaroon, Reza Soroushmehr
Research Method: Computer Programming

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