Hospitalization prediction from the emergency department using computer vision AI with short patient video clips

IF 12.4 1区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Wui Ip, Maria Xenochristou, Elaine Sui, Elyse Ruan, Ryan Ribeira, Debadutta Dash, Malathi Srinivasan, Maja Artandi, Jesutofunmi A. Omiye, Nicholas Scoulios, Hayden L. Hofmann, Ali Mottaghi, Zhenzhen Weng, Abhinav Kumar, Ananya Ganesh, Jason Fries, Serena Yeung-Levy, Lawrence V. Hofmann
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Abstract

In this study, we investigate the performance of computer vision AI algorithms in predicting patient disposition from the emergency department (ED) using short video clips. Clinicians often use “eye-balling” or clinical gestalt to aid in triage, based on brief observations. We hypothesize that AI can similarly use patient appearance for disposition prediction. Data were collected from adult patients at an academic ED, with mobile phone videos capturing patients performing simple tasks. Our AI algorithm, using video alone, showed better performance in predicting hospital admissions (AUROC = 0.693 [95% CI 0.689, 0.696]) compared to models using triage clinical data (AUROC = 0.678 [95% CI 0.668, 0.687]). Combining video and triage data achieved the highest predictive performance (AUROC = 0.714 [95% CI 0.709, 0.719]). This study demonstrates the potential of video AI algorithms to support ED triage and alleviate healthcare capacity strains during periods of high demand.

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来源期刊
CiteScore
25.10
自引率
3.30%
发文量
170
审稿时长
15 weeks
期刊介绍: npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.
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