Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.

Autor: Javor D; Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria., Kaplan H; Deepinsights Study Group for Artificial Intelligence, Vienna, Austria., Kaplan A; Deepinsights Study Group for Artificial Intelligence, Vienna, Austria., Puchner SB; Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria. Electronic address: stefan.puchner@meduniwien.ac.at., Krestan C; Department of Radiology, Sozialmedizinisches Zentrum Süd - Kaiser-Franz-Josef Spital, Vienna, Austria., Baltzer P; Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Jazyk: angličtina
Zdroj: European journal of radiology [Eur J Radiol] 2020 Dec; Vol. 133, pp. 109402. Date of Electronic Publication: 2020 Nov 04.
DOI: 10.1016/j.ejrad.2020.109402
Abstrakt: Introduction: Computed Tomography is an essential diagnostic tool in the management of COVID-19. Considering the large amount of examinations in high case-load scenarios, an automated tool could facilitate and save critical time in the diagnosis and risk stratification of the disease.
Methods: A novel deep learning derived machine learning (ML) classifier was developed using a simplified programming approach and an open source dataset consisting of 6868 chest CT images from 418 patients which was split into training and validation subsets. The diagnostic performance was then evaluated and compared to experienced radiologists on an independent testing dataset. Diagnostic performance metrics were calculated using Receiver Operating Characteristics (ROC) analysis. Operating points with high positive (>10) and low negative (<0.01) likelihood ratios to stratify the risk of COVID-19 being present were identified and validated.
Results: The model achieved an overall accuracy of 0.956 (AUC) on an independent testing dataset of 90 patients. Both rule-in and rule out thresholds were identified and tested. At the rule-in operating point, sensitivity and specificity were 84.4 % and 93.3 % and did not differ from both radiologists (p > 0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p < 0.05). Likelihood ratios and a Fagan nomogram provide prevalence independent test performance estimates.
Conclusion: Accurate diagnosis of COVID-19 using a basic deep learning approach is feasible using open-source CT image data. In addition, the machine learning classifier provided validated rule-in and rule-out criteria could be used to stratify the risk of COVID-19 being present.
(Copyright © 2020. Published by Elsevier B.V.)
Databáze: MEDLINE
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