Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography
| Autor: | H. Kaplan, C. Krestan, Domagoj Javor, A. Kaplan, Stefan Puchner, Pascal A. T. Baltzer |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Male
Coronavirus disease 2019 (COVID-19) Computed tomography Sensitivity and Specificity Article 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Medical imaging medicine Humans Radiology Nuclear Medicine and imaging Lung Operating point Learning classifier system Receiver operating characteristic medicine.diagnostic_test Coronavirus disease 2019 business.industry Deep learning COVID-19 Reproducibility of Results Pattern recognition General Medicine Middle Aged ROC Curve Radiology Nuclear Medicine and imaging 030220 oncology & carcinogenesis Radiographic Image Interpretation Computer-Assisted Female Artificial intelligence business Tomography X-Ray Computed Classifier (UML) |
| Zdroj: | European Journal of Radiology |
| ISSN: | 0720-048X |
| DOI: | 10.1016/j.ejrad.2020.109402 |
| Popis: | 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 ( 0.05). At the rule-out threshold, sensitivity (100 %) and specificity (60 %) differed significantly from the radiologists (p |
| Databáze: | OpenAIRE |
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