An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography. Issue 2 (3rd May 2022)

• Record Type: Journal Article
• Authors: Vaidyanathan, Akshayaa; Guiot, Julien; Zerka, Fadila; Belmans, Flore; Van Peufflik, Ingrid; Deprez, Louis; Danthine, Denis; Canivet, Gregory; Lambin, Philippe; Walsh, Sean; Occhipinti, Mariaelena; Meunier, Paul; Vos, Wim; Lovinfosse, Pierre; Leijenaar, Ralph T.H.
• Is Part Of: ERJ open research. Volume 8:Issue 2(2022)
• Publication Date: 2022-05-03
• Subjects: Respiratory organs -- Diseases -- Periodicals; Respiration -- Periodicals; Respiration; Respiratory organs -- Diseases; Respiratory organs -- Diseases -- Treatment; Respiratory Tract Diseases; Electronic journals; Fulltext; Internet Resources; Periodicals; Periodical; 616.2005
• DOI: 10.1183/23120541.00579-2021
• Languages: English
• ISSNs: 2312-0541

Abstract

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