Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained...

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Bibliographic Details
Published in:Scientific reports 2022-04, Vol.12 (1), p.5616-5616, Article 5616
Main Authors: Gourdeau, Daniel, Potvin, Olivier, Archambault, Patrick, Chartrand-Lefebvre, Carl, Dieumegarde, Louis, Forghani, Reza, Gagné, Christian, Hains, Alexandre, Hornstein, David, Le, Huy, Lemieux, Simon, Lévesque, Marie-Hélène, Martin, Diego, Rosenbloom, Lorne, Tang, An, Vecchio, Fabrizio, Yang, Issac, Duchesne, Nathalie, Duchesne, Simon
Format: Article
Language:English
Subjects:
639/705/117
692/700/1421/1770
Algorithms
Chest
Coronaviruses
COVID-19
COVID-19 - diagnostic imaging
Data collection
Datasets
Deep Learning
Humanities and Social Sciences
Humans
multidisciplinary
Patients
Pleural effusion
Radiography
ROC Curve
Science
Science (multidisciplinary)
X-rays
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Summary:Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the course of the disease is closely linked to the outcome. As such, evaluation of future severity from current CXR would be highly desirable. We trained a repurposed deep learning algorithm on the CheXnet open dataset (224,316 chest X-ray images of 65,240 unique patients) to extract features that mapped to radiological labels. We collected CXRs of COVID-19-positive patients from an open-source dataset (COVID-19 image data collection) and from a multi-institutional local ICU dataset. The data was grouped into pairs of sequential CXRs and were categorized into three categories: ‘Worse’, ‘Stable’, or ‘Improved’ on the basis of radiological evolution ascertained from images and reports. Classical machine-learning algorithms were trained on the deep learning extracted features to perform immediate severity evaluation and prediction of future radiological trajectory. Receiver operating characteristic analyses and Mann-Whitney tests were performed. Deep learning predictions between “Worse” and “Improved” outcome categories and for severity stratification were significantly different for three radiological signs and one diagnostic (‘Consolidation’, ‘Lung Lesion’, ‘Pleural effusion’ and ‘Pneumonia’; all P  
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-022-09356-w