Automatic ARDS surveillance with chest X-ray recognition using convolutional neural networks

This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs. A diagnostic performance study was conducted using Chest X-Ray images from adult patients admitted...

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Bibliographic Details
Published in:Journal of critical care 2024-08, Vol.82, p.154794-154794, Article 154794
Main Authors: Ye, Run Zhou, Lipatov, Kirill, Diedrich, Daniel, Bhattacharyya, Anirban, Erickson, Bradley J., Pickering, Brian W., Herasevich, Vitaly
Format: Article
Language:English
Subjects:
Accuracy
ARDS
Convolutional neural network
COVID-19
Datasets
Deep learning
Intensive care
Keywords
Machine learning
Mortality
Neural networks
Patient admissions
Pediatrics
Pneumonia
Radiology
Surveillance
Ventilators
X-rays
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Summary:This study aims to design, validate and assess the accuracy a deep learning model capable of differentiation Chest X-Rays between pneumonia, acute respiratory distress syndrome (ARDS) and normal lungs. A diagnostic performance study was conducted using Chest X-Ray images from adult patients admitted to a medical intensive care unit between January 2003 and November 2014. X-ray images from 15,899 patients were assigned one of three prespecified categories: “ARDS”, “Pneumonia”, or “Normal”. A two-step convolutional neural network (CNN) pipeline was developed and tested to distinguish between the three patterns with sensitivity ranging from 91.8% to 97.8% and specificity ranging from 96.6% to 98.8%. The CNN model was validated with a sensitivity of 96.3% and specificity of 96.6% using a previous dataset of patients with Acute Lung Injury (ALI)/ARDS. The results suggest that a deep learning model based on chest x-ray pattern recognition can be a useful tool in distinguishing patients with ARDS from patients with normal lungs, providing faster results than digital surveillance tools based on text reports. A CNN-based deep learning model showed clinically significant performance, providing potential for faster ARDS identification. Future research should prospectively evaluate these tools in a clinical setting. •CNN model distinguishes ARDS, pneumonia, normal lung X-rays.•High sensitivity (91.8–97.8%) and specificity (96.6–98.8%) achieved.•Model validated with 96.3% sensitivity, 96.6% specificity.•Faster, accurate ARDS detection using CNN technology.
ISSN:0883-9441
1557-8615
DOI:10.1016/j.jcrc.2024.154794