ArticleEuropean journal of radiology2020
Deep learning analysis provides accurate COVID-19 diagnosis on chest computed tomography.
Article in European journal of radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for imaging-based COVID-19 detection: Systematic review comparing added value of AI versus human readers.European journal of radiology · 2021Pooled it
- Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis.Academic radiology · 2021Pooled it
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- Artificial Intelligence-Assisted Chest X-ray for the Diagnosis of COVID-19: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2023Review
- Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review.Neural processing letters · 2022Review
- An original deep learning model using limited data for COVID-19 discrimination: A multicenter study.Medical physics · 2022Article
- Deep Learning-Enabled Technologies for Bioimage Analysis.Micromachines · 2022Review
- The application of a deep learning system developed to reduce the time for RT-PCR in COVID-19 detection.Scientific reports · 2022Article
- Deep Transfer Learning for COVID-19 Detection and Lesion Recognition Using Chest CT Images.Computational and mathematical methods in medicine · 2022Article
- Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images.Computers in biology and medicine · 2021Article
- The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review.Diagnostics (Basel, Switzerland) · 2021Review
- A comprehensive review of imaging findings in COVID-19 - status in early 2021.European journal of nuclear medicine and molecular imaging · 2021Review
- Construction of nanomaterials as contrast agents or probes for glioma imaging.Journal of nanobiotechnology · 2021Review
- A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography.Scientific reports · 2021Article
- Machine learning models for predicting critical illness risk in hospitalized patients with COVID-19 pneumonia.Journal of thoracic disease · 2021Article
- The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis.Informatics in medicine unlocked · 2021Review
- Artificial Intelligence Clinicians Can Use Chest Computed Tomography Technology to Automatically Diagnose Coronavirus Disease 2019 (COVID-19) Pneumonia and Enhance Low-Quality Images.Infection and drug resistance · 2021Article
- COVIDC: An expert system to diagnose COVID-19 and predict its severity using chest CT scans: Application in radiology.Informatics in medicine unlocked · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionComputed 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.
methodsA 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.
resultsThe 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.
conclusionAccurate 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.
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