An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data - Kim, Chris K.; Choi, Ji Whae; Jiao, Zhicheng; Wang, Dongcui; Wu, Jing; Yi, Thomas Y.; Halsey, Kasey C.; Eweje, Feyisope; Tran, Thi My Linh; Liu, Chang; Wang, Robin; Sollee, John; et al. (NPJ digital medicine, 2022)
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.