tetano
Editor, Senior Moderator
ERJ Open Res
. 2022 May 3;8(2):00579-2021.
doi: 10.1183/23120541.00579-2021. eCollection 2022 Apr.
An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography
Akshayaa Vaidyanathan[SUP] 1 2 3 [/SUP], Julien Guiot[SUP] 4 3 [/SUP], Fadila Zerka[SUP] 1 2 [/SUP], Flore Belmans[SUP] 1 [/SUP], Ingrid Van Peufflik[SUP] 1 [/SUP], Louis Deprez[SUP] 5 [/SUP], Denis Danthine[SUP] 5 [/SUP], Gregory Canivet[SUP] 6 [/SUP], Philippe Lambin[SUP] 2 [/SUP], Sean Walsh[SUP] 1 [/SUP], Mariaelena Occhipinti[SUP] 1 [/SUP], Paul Meunier[SUP] 5 [/SUP], Wim Vos[SUP] 1 [/SUP], Pierre Lovinfosse[SUP] 7 [/SUP], Ralph T H Leijenaar[SUP] 1 [/SUP]
Affiliations
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.
. 2022 May 3;8(2):00579-2021.
doi: 10.1183/23120541.00579-2021. eCollection 2022 Apr.
An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography
Akshayaa Vaidyanathan[SUP] 1 2 3 [/SUP], Julien Guiot[SUP] 4 3 [/SUP], Fadila Zerka[SUP] 1 2 [/SUP], Flore Belmans[SUP] 1 [/SUP], Ingrid Van Peufflik[SUP] 1 [/SUP], Louis Deprez[SUP] 5 [/SUP], Denis Danthine[SUP] 5 [/SUP], Gregory Canivet[SUP] 6 [/SUP], Philippe Lambin[SUP] 2 [/SUP], Sean Walsh[SUP] 1 [/SUP], Mariaelena Occhipinti[SUP] 1 [/SUP], Paul Meunier[SUP] 5 [/SUP], Wim Vos[SUP] 1 [/SUP], Pierre Lovinfosse[SUP] 7 [/SUP], Ralph T H Leijenaar[SUP] 1 [/SUP]
Affiliations
- PMID: 35509437
- PMCID: PMC8958945
- DOI: 10.1183/23120541.00579-2021
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.