期刊
NPJ DIGITAL MEDICINE
卷 5, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41746-021-00546-w
关键词
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资金
- Brown University COVID-19 seed grant
- Amazon Web Services for the Diagnostic Development Initiative
- National Cancer Institute [F30CA239407]
The study developed a diagnostic model for the detection and evaluation of COVID-19 pneumonia, integrating it into an automated triage pipeline. By collecting a large number of chest X-rays and related data, the study demonstrated the value of the pipeline through assessing the model's generalizability and comparing it with traditional radiologist interpretation.
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.
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