AI-ENABLED ASSESSMENT OF CARDIAC FUNCTION AND VIDEO QUALITY IN EMERGENCY DEPARTMENT POINT-OF-CARE ECHOCARDIOGRAMS.
J Emerg Med
; 66(2): 184-191, 2024 02.
Article
em En
| MEDLINE
| ID: mdl-38369413
ABSTRACT
BACKGROUND:
The adoption of point-of-care ultrasound (POCUS) has greatly improved the ability to rapidly evaluate unstable emergency department (ED) patients at the bedside. One major use of POCUS is to obtain echocardiograms to assess cardiac function.OBJECTIVES:
We developed EchoNet-POCUS, a novel deep learning system, to aid emergency physicians (EPs) in interpreting POCUS echocardiograms and to reduce operator-to-operator variability.METHODS:
We collected a new dataset of POCUS echocardiogram videos obtained in the ED by EPs and annotated the cardiac function and quality of each video. Using this dataset, we train EchoNet-POCUS to evaluate both cardiac function and video quality in POCUS echocardiograms.RESULTS:
EchoNet-POCUS achieves an area under the receiver operating characteristic curve (AUROC) of 0.92 (0.89-0.94) for predicting whether cardiac function is abnormal and an AUROC of 0.81 (0.78-0.85) for predicting video quality.CONCLUSIONS:
EchoNet-POCUS can be applied to bedside echocardiogram videos in real time using commodity hardware, as we demonstrate in a prospective pilot study.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Ecocardiografia
/
Sistemas Automatizados de Assistência Junto ao Leito
Limite:
Humans
Idioma:
En
Revista:
J Emerg Med
/
J. emerg. med
/
Journal of emergency medicine
Assunto da revista:
MEDICINA DE EMERGENCIA
Ano de publicação:
2024
Tipo de documento:
Article
País de publicação:
Estados Unidos