Ip Wui, Xenochristou Maria, Sui Elaine, Ruan Elyse, Ribeira Ryan, Dash Debadutta, Srinivasan Malathi, Artandi Maja, Omiye Jesutofunmi A, Scoulios Nicholas, Hofmann Hayden L, Mottaghi Ali, Weng Zhenzhen, Kumar Abhinav, Ganesh Ananya, Fries Jason, Yeung-Levy Serena, Hofmann Lawrence V
Department of Pediatrics, Stanford University School of Medicine, Palo Alto, CA, USA.
Department of Biomedical Data Science, Stanford University School of Medicine, Palo Alto, CA, USA.
NPJ Digit Med. 2024 Dec 19;7(1):371. doi: 10.1038/s41746-024-01375-3.
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.
在本研究中，我们调查了计算机视觉人工智能算法利用短视频片段预测急诊科（ED）患者处置情况的性能。临床医生通常基于简短观察，运用“直观判断”或临床整体印象来辅助分诊。我们假设人工智能同样可以利用患者的外貌进行处置预测。数据收集自一家学术性急诊科的成年患者，通过手机视频记录患者执行简单任务的情况。与使用分诊临床数据的模型（曲线下面积[AUC] = 0.678 [95%置信区间0.668, 0.687]）相比，我们仅使用视频的人工智能算法在预测住院情况方面表现更佳（AUC = 0.693 [95%置信区间0.689, 0.696]）。将视频和分诊数据相结合可实现最高的预测性能（AUC = 0.714 [95%置信区间0.709, 0.719]）。本研究证明了视频人工智能算法在支持急诊科分诊以及缓解高需求时期医疗能力压力方面的潜力。
