ArticlePattern recognition letters2021
A practical artificial intelligence system to diagnose COVID-19 using computed tomography: A multinational external validation study.
Article in Pattern recognition letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions.Computers in biology and medicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.Computers in biology and medicine · 2023Article
- MMViT-Seg: A lightweight transformer and CNN fusion network for COVID-19 segmentation.Computer methods and programs in biomedicine · 2023Article
- An implementation of a hybrid method based on machine learning to identify biomarkers in the Covid-19 diagnosis using DNA sequences.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection.Computers in biology and medicine · 2022Article
- Fully automatic pipeline of convolutional neural networks and capsule networks to distinguish COVID-19 from community-acquired pneumonia via CT images.Computers in biology and medicine · 2022Article
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computed tomography has gained an important role in the early diagnosis of COVID-19 pneumonia. However, the ever-increasing number of patients has overwhelmed radiology departments and has caused a reduction in quality of services. Artificial intelligence (AI) systems are the remedy to the current situation. However, the lack of application in real-world conditions has limited their consideration in clinical settings. This study validated a clinical AI system, COVIDiag, to aid radiologists in accurate and rapid evaluation of COVID-19 cases. 50 COVID-19 and 50 non-COVID-19 pneumonia cases were included from each of five centers: Argentina, Turkey, Iran, Netherlands, and Italy. The Dutch database included only 50 COVID-19 cases. The performance parameters namely sensitivity, specificity, accuracy, and area under the ROC curve (AUC) were computed for each database using COVIDiag model. The most common pattern of involvement among COVID-19 cases in all databases were bilateral involvement of upper and lower lobes with ground-glass opacities. The best sensitivity of 92.0% was recorded for the Italian database. The system achieved an AUC of 0.983, 0.914, 0.910, and 0.882 for Argentina, Turkey, Iran, and Italy, respectively. The model obtained a sensitivity of 86.0% for the Dutch database. COVIDiag model could diagnose COVID-19 pneumonia in all of cohorts with AUC of 0.921 (sensitivity, specificity, and accuracy of 88.8%, 87.0%, and 88.0%, respectively). Our study confirmed the accuracy of our proposed AI model (COVIDiag) in the diagnosis of COVID-19 cases. Furthermore, the system demonstrated consistent optimal diagnostic performance on multinational databases, which is critical to determine the generalizability and objectivity of the proposed COVIDiag model. Our results are significant as they provide real-world evidence regarding the applicability of AI systems in clinical medicine.
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