Artificial intelligence–enabled rapid diagnosis of patients with COVID-19
Open accessPublished version of a preprint
Abstract
For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test is routinely used. However, this test can take up to 2 d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT-PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT-PCR assay and next-generation sequencing RT-PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT-PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.
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Alternative approaches to COVID-19 triage use radiological imaging, which is less readily available and involves patient exposure to ionising radiation, or infrequently requested laboratory predicting markers, such as IL-6.
“By contrast, alternative approaches to COVID-19 triage use radiological imaging, which is less readily available and involves patient exposure to ionising radiation,22 or infrequently requested laboratory predicting markers, such as IL-6.23, 24”
From Real-world evaluation of rapid and laboratory-free COVID-19 triage for emergency care: external validation and pilot deployment of artificial intelligence driven screening (2022) · cites this paper as [22]
The paper describes chest CT as a tool for COVID-19 triage and says thoracic radiologists may not be available at every institution (Abstract; page not provided). It does not say imaging itself is less readily available or discuss ionising-radiation exposure; instead, it describes CT as widely used in some countries because of its speed and availability. The IL-6 clause is attached to other citations, not this source. (LLM, conf=0.84)
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References 36 · Cited by 1,084
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- Authors
- Mei, X., Lee, H.-C., Diao, K.-Y., Huang, M., Lin, B., Liu, C., Xie, Z., Ma, Y., Robson, P. M., Chung, M., Bernheim, A., Mani, V., Calcagno, C., Li, K., Li, S., Shan, H., Lv, J., Zhao, T., Xia, J., Long, Q., Steinberger, S., Jacobi, A., Deyer, T., Luksza, M., Liu, F., Little, B. P., Fayad, Z. A., & Yang, Y.
- Affiliations
- Icahn School of Medicine at Mount Sinai (US)
- Second Affiliated Hospital of Zhejiang University (CN)
- First Affiliated Hospital of Bengbu Medical College (CN)
- Sun Yat-sen University (CN)
- Fuyang Second People's Hospital (CN)
- Wuhan University (CN)
- Weill Cornell Medicine (US)
- Massachusetts General Hospital (US)
- Corresponding author
- Little, Brent P.
- Fayad, Zahi A.
- Yang, Yang
- ORCID
- Mei, Xueyan
- Lee, Hao-Chih
- Huang, Mingqian
- Lin, Bin
- Liu, Chenyu
- Xie, Zongyu
- Ma, Yixuan
- Robson, Philip M.
- Chung, Michael
- Bernheim, Adam
- Mani, Venkatesh
- Calcagno, Claudia
- Li, Kunwei
- Li, Shaolin
- Shan, Hong
- Lv, Jian
- Zhao, Tongtong
- Steinberger, Sharon
- Jacobi, Adam
- Deyer, Timothy
- Liu, Fang
- Little, Brent P.
- Fayad, Zahi A.
- Yang, Yang
- Venue
- Nature Medicine
- Published
- 2020-05-19
- Volume
- 26
- Issue
- 8
- Pages
- 1224-1228
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- PMC
- PMC7446729
- OpenAlex
- W3025948831
- Cited by
- 1,084
- References
- 36
- Citations by year
- 2026: 33 · 2025: 79 · 2024: 114 · 2023: 155 · 2022: 200 · 2021: 347 · 2020: 156
- FWCI
- 104.60
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Nature Medicine (publishedVersion)
- medRxiv (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- PubMed Central (submittedVersion)
- Funders
- from Astra Zeneca, Daiichi Sankyo, Alexion and Horizon Pharma
- U.S. Department of Health & Human Services | NIH | National Center for Advancing Translational Sciences (UL1TR001433)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › COVID-19 diagnosis using AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 detection and testing
- Keywords
- COVID-19 pandemic, SARS-CoV-2, artificial intelligence, chest CT, rapid diagnosis, RT-PCR, clinical decision support, normal CT findings, diagnostic accuracy, radiologist comparison
- MeSH terms
- Betacoronavirus, COVID-19, SARS-CoV-2, Adult, Artificial Intelligence, Female, Humans, Male, Middle Aged, Pneumonia, Viral, Thorax, Tomography, X-Ray Computed, Coronavirus Infections, Pandemics, Real-Time Polymerase Chain Reaction
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