Elaine Sui

All Publications

• Hospitalization prediction from the emergency department using computer vision AI with short patient video clips. NPJ digital medicine Ip, W., Xenochristou, M., Sui, E., Ruan, E., Ribeira, R., Dash, D., Srinivasan, M., Artandi, M., Omiye, J. A., Scoulios, N., Hofmann, H. L., Mottaghi, A., Weng, Z., Kumar, A., Ganesh, A., Fries, J., Yeung-Levy, S., Hofmann, L. V. 2024; 7 (1): 371

  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.

  View details for DOI 10.1038/s41746-024-01375-3

  View details for PubMedID 39702364

  View details for PubMedCentralID 10747245

• The Future Is in Sight-Using Computer Vision for Automated Objective Review of Laparoscopic Cholecystectomies Villarreal, J., Egeland, C. L., Sui, E., Rau, A., Goel, S., Sorenson, E. C., Bohn, R., Grantcharov, T. P., Yeung-Levy, S., Jopling, J. K. LIPPINCOTT WILLIAMS & WILKINS. 2024: S424-S425

  View details for Web of Science ID 001348680703042