2021/05/10 by Yutong Ban, Guy Rosman, Ban, Yutong +15 · 1 citation
Computer Science · Medicine · #Colorectal Cancer Screening and Detection #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning in Healthcare #Surgical Simulation and Training
paper · pdf · doi:10.48550/arxiv.2105.04642
openalex publication_date 2021/05/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Comprehension of surgical workflow is the foundation upon which artificial intelligence (AI) and machine learning (ML) holds the potential to assist intraoperative decision-making and risk mitigation. In this work, we move beyond mere identification of past surgical phases, into the prediction of future surgical steps and specification of the transitions between them. We use a novel Generative Adversarial Network (GAN) formulation to sample future surgical phases trajectories conditioned on past video frames from laparoscopic cholecystectomy (LC) videos and compare it to state-of-the-art approaches for surgical video analysis and alternative prediction methods. We demonstrate the GAN formulation's effectiveness through inferring and predicting the progress of LC videos. We quantify the horizon-accuracy trade-off and explored average performance, as well as the performance on the more challenging, and clinically relevant transitions between phases. Furthermore, we conduct a survey, asking 16 surgeons of different specialties and educational levels to qualitatively evaluate predicted surgery phases.