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An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.
- Source :
- NPJ Digital Medicine; 1/14/2022, Vol. 5 Issue 1, p1-9, 9p
- Publication Year :
- 2022
-
Abstract
- While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital's image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 23986352
- Volume :
- 5
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- NPJ Digital Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 154708477
- Full Text :
- https://doi.org/10.1038/s41746-021-00546-w