Huy Le

Louis Gagnon

Raphaelle Giguère

Alexandre Hains

Simon Lemieux

Marie-Hélène Lévesque

Simon Nepveu

Lorne Rosenbloom

An Tang

Issac Yang

Nathalie Duchesne … (voir 1 de plus)

Simon Duchesne

2022-04-13

Scientific Reports (publié)

TRACKING AND PREDICTING COVID-19 RADIOLOGICAL TRAJECTORY USING DEEP LEARNING ON CHEST X-RAYS: INITIAL ACCURACY TESTING

Simon Duchesne

Olivier Potvin

Daniel Gourdeau

Patrick Archambault

Carl Chartrand-Lefebvre

Louis Dieumegarde

Reza Forghani

Alexandre Hains

David Hornstein

Simon Lemieux

Marie-Hélène Lévesque

Diego Martin

Lorne Rosenbloom

An Tang

Fabrizio Vecchio

Issac Yang

Nathalie Duchesne

2022-04-04

Scientific Reports (publié)

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

Daniel Gourdeau

Olivier Potvin

Patrick Archambault

Carl Chartrand-Lefebvre

Louis Dieumegarde

Reza Forghani

Alexandre Hains

David Hornstein

Simon Lemieux

Marie-Hélène Lévesque

Diego Martin

Lorne Rosenbloom

An Tang

Fabrizio Vecchio

Issac Yang

Nathalie Duchesne

Simon Duchesne

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity… (voir plus) 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 0.05). Features from the first CXR of each pair could correctly predict the outcome category between ‘Worse’ and ‘Improved’ cases with a 0.81 (0.74–0.83 95% CI) AUC in the open-access dataset and with a 0.66 (0.67–0.64 95% CI) AUC in the ICU dataset. Features extracted from the CXR could predict disease severity with a 52.3% accuracy in a 4-way classification. Severity evaluation trained on the COVID-19 image data collection had good out-of-distribution generalization when testing on the local dataset, with 81.6% of intubated ICU patients being classified as critically ill, and the predicted severity was correlated with the clinical outcome with a 0.639 AUC. CXR deep learning features show promise for classifying disease severity and trajectory. Once validated in studies incorporating clinical data and with larger sample sizes, this information may be considered to inform triage decisions.

2022-04-04

Scientific Reports (publié)

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

Daniel Gourdeau

Olivier Potvin

Patrick Archambault

Carl Chartrand-Lefebvre

Louis Dieumegarde

Reza Forghani

Alexandre Hains

David Hornstein

Simon Lemieux

Marie-Hélène Lévesque

Diego Martin

Lorne Rosenbloom

An Tang

Fabrizio Vecchio

Issac Yang

Nathalie Duchesne

Simon Duchesne

2022-04-04

Scientific Reports (publié)