Artificial intelligence for prediction of COVID-19 progression using CT imaging and clinical data.
Eur Radiol
; 32(1): 205-212, 2022 Jan.
Artículo
en Inglés
| MEDLINE | ID: covidwho-1293361
ABSTRACT
OBJECTIVES:
Early recognition of coronavirus disease 2019 (COVID-19) severity can guide patient management. However, it is challenging to predict when COVID-19 patients will progress to critical illness. This study aimed to develop an artificial intelligence system to predict future deterioration to critical illness in COVID-19 patients.METHODS:
An artificial intelligence (AI) system in a time-to-event analysis framework was developed to integrate chest CT and clinical data for risk prediction of future deterioration to critical illness in patients with COVID-19.RESULTS:
A multi-institutional international cohort of 1,051 patients with RT-PCR confirmed COVID-19 and chest CT was included in this study. Of them, 282 patients developed critical illness, which was defined as requiring ICU admission and/or mechanical ventilation and/or reaching death during their hospital stay. The AI system achieved a C-index of 0.80 for predicting individual COVID-19 patients' to critical illness. The AI system successfully stratified the patients into high-risk and low-risk groups with distinct progression risks (p < 0.0001).CONCLUSIONS:
Using CT imaging and clinical data, the AI system successfully predicted time to critical illness for individual patients and identified patients with high risk. AI has the potential to accurately triage patients and facilitate personalized treatment. KEY POINT ⢠AI system can predict time to critical illness for patients with COVID-19 by using CT imaging and clinical data.Palabras clave
Texto completo:
Disponible
Colección:
Bases de datos internacionales
Base de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Estudio de cohorte
/
Estudio experimental
/
Estudio observacional
/
Estudio pronóstico
/
Ensayo controlado aleatorizado
Límite:
Humanos
Idioma:
Inglés
Revista:
Eur Radiol
Asunto de la revista:
Radiología
Año:
2022
Tipo del documento:
Artículo
País de afiliación:
S00330-021-08049-8
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