ArticleNature communications2020
Development and evaluation of an artificial intelligence system for COVID-19 diagnosis.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 236 papers, 6 of them syntheses that pooled it.
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Who cites it
236 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.La Radiologia medica · 2024Pooled it
- Comparative bibliometric analysis of artificial intelligence-assisted polyp diagnosis and AI-assisted digestive endoscopy: trends and growth in AI gastroenterology (2003-2023).Frontiers in medicine · 2024Pooled it
- Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis.European radiology · 2022Pooled it
- A meta-analysis of the diagnostic test accuracy of CT-based radiomics for the prediction of COVID-19 severity.La Radiologia medica · 2022Pooled it
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Diagnostic Test Accuracy of Deep Learning Detection of COVID-19: A Systematic Review and Meta-Analysis.Academic radiology · 2021Pooled it
- A vision-language pretrained transformer for versatile clinical respiratory disease applications.Nature biomedical engineering · 2026Article
- Evaluation of AI tool assisting primary healthcare physicians to diagnostic and treatment tasks.Family medicine and community health · 2026Article
- Performance of AI Approaches for COVID-19 Diagnosis Using Chest CT Scans: The Impact of Architecture and Dataset.RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin · 2026Article
- Utility of the virtual imaging trials methodology for objective characterization of AI systems and training data.Journal of medical imaging (Bellingham, Wash.) · 2026Article
- Guiding Antibiotic Therapy with Machine Learning: Real-World Applications of a CDSS in Bacteremia Management.Life (Basel, Switzerland) · 2025Article
- AI-assisted recurrent laryngeal nerve identification during endoscopic/robotic thyroid surgery based on the CMC-UNet model: a multicenter retrospective study.Journal of robotic surgery · 2025Article
- Multimodal text guided network for chest CT pneumonia classification.Scientific reports · 2025Article
- Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review.Diagnostics (Basel, Switzerland) · 2025Review
- Prospective study using artificial neural networks for identification of high-risk COVID-19 patients.Scientific reports · 2025Article
- Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.Journal of imaging informatics in medicine · 2025Article
- AI-based prediction of androgen receptor expression and its prognostic significance in prostate cancer.Scientific reports · 2025Article
- Artificial intelligence for body composition assessment focusing on sarcopenia.Scientific reports · 2025Article
- Deep learning models for CT image classification: a comprehensive literature review.Quantitative imaging in medicine and surgery · 2025Review
- Article
176 more citing papers are in PubMed but not listed here.
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection of COVID-19 based on chest CT enables timely treatment of patients and helps control the spread of the disease. We proposed an artificial intelligence (AI) system for rapid COVID-19 detection and performed extensive statistical analysis of CTs of COVID-19 based on the AI system. We developed and evaluated our system on a large dataset with more than 10 thousand CT volumes from COVID-19, influenza-A/B, non-viral community acquired pneumonia (CAP) and non-pneumonia subjects. In such a difficult multi-class diagnosis task, our deep convolutional neural network-based system is able to achieve an area under the receiver operating characteristic curve (AUC) of 97.81% for multi-way classification on test cohort of 3,199 scans, AUC of 92.99% and 93.25% on two publicly available datasets, CC-CCII and MosMedData respectively. In a reader study involving five radiologists, the AI system outperforms all of radiologists in more challenging tasks at a speed of two orders of magnitude above them. Diagnosis performance of chest x-ray (CXR) is compared to that of CT. Detailed interpretation of deep network is also performed to relate system outputs with CT presentations. The code is available at https://github.com/ChenWWWeixiang/diagnosis_covid19 .
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