RT Journal Article
SR Electronic
T1 An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography
JF ERJ Open Research
JO erjor
FD European Respiratory Society
SP 00579-2021
DO 10.1183/23120541.00579-2021
VO 8
IS 2
A1 Akshayaa Vaidyanathan
A1 Julien Guiot
A1 Fadila Zerka
A1 Flore Belmans
A1 Ingrid Van Peufflik
A1 Louis Deprez
A1 Denis Danthine
A1 Gregory Canivet
A1 Philippe Lambin
A1 Sean Walsh
A1 Mariaelena Occhipinti
A1 Paul Meunier
A1 Wim Vos
A1 Pierre Lovinfosse
A1 Ralph T.H. Leijenaar
YR 2022
UL http://openres.ersjournals.com/content/8/2/00579-2021.abstract
AB Purpose In this study, we propose an artificial intelligence (AI) framework based on three-dimensional convolutional neural networks to classify computed tomography (CT) scans of patients with coronavirus disease 2019 (COVID-19), influenza/community-acquired pneumonia (CAP), and no infection, after automatic segmentation of the lungs and lung abnormalities.Methods The AI classification model is based on inflated three-dimensional Inception architecture and was trained and validated on retrospective data of CT images of 667 adult patients (no infection n=188, COVID-19 n=230, influenza/CAP n=249) and 210 adult patients (no infection n=70, COVID-19 n=70, influenza/CAP n=70), respectively. The model's performance was independently evaluated on an internal test set of 273 adult patients (no infection n=55, COVID-19 n= 94, influenza/CAP n=124) and an external validation set from a different centre (305 adult patients: COVID-19 n=169, no infection n=76, influenza/CAP n=60).Results The model showed excellent performance in the external validation set with area under the curve of 0.90, 0.92 and 0.92 for COVID-19, influenza/CAP and no infection, respectively. The selection of the input slices based on automatic segmentation of the abnormalities in the lung reduces analysis time (56 s per scan) and computational burden of the model. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) score of the proposed model is 47% (15 out of 32 TRIPOD items).Conclusion This AI solution provides rapid and accurate diagnosis in patients suspected of COVID-19 infection and influenza.A fully automated artificial intelligence-based network is proposed to classify CT volumes of patients affected with COVID-19 or influenza/CAP, and in the uninfected https://bit.ly/3MJrVRi