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An externally validated fully automated deep learning algorithm to classify COVID-19 and other pneumonias on chest computed tomography
- Source :
- ERJ Open Research, Vol 8, Iss 2 (2022)
- Publication Year :
- 2022
- Publisher :
- European Respiratory Society, 2022.
-
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.
- Subjects :
- Medicine
Subjects
Details
- Language :
- English
- ISSN :
- 23120541
- Volume :
- 8
- Issue :
- 2
- Database :
- Directory of Open Access Journals
- Journal :
- ERJ Open Research
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.06c0f2983a8e45f79819d1d0caf8e1e8
- Document Type :
- article
- Full Text :
- https://doi.org/10.1183/23120541.00579-2021