An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data.
NPJ Digit Med
; 5(1): 5, 2022 Jan 14.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1625359
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.
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Tipo de estudio:
Estudio de cohorte
/
Estudio experimental
/
Estudio observacional
/
Estudio pronóstico
Idioma:
Inglés
Revista:
NPJ Digit Med
Año:
2022
Tipo del documento:
Artículo
País de afiliación:
S41746-021-00546-w
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