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.
【저자키워드】 Computer science, Radiography, 【초록키워드】 COVID-19, Prognosis, Pneumonia, severity, diagnostic, Diagnosis, progression, RT-PCR, Accuracy, Chest, Interpretation, COVID-19 diagnosis, automated, chest X-ray, prospective cohort, platform, Image, high risk, Generalizability, Repository, archive, while, mitigate, DICOM, collected, develop, evaluated, notable, 【제목키워드】 COVID-19, Chest, automated, Clinical data,