Early Operative Difficulty Assessment in Laparoscopic Cholecystectomy via Snapshot-Centric Video Analysis

2025-02-10

AI Summary

To address the clinical need for early and stable assessment of laparoscopic cholecystectomy operative difficulty (LCOD), this paper introduces the novel task of “early-stable prediction” from intraoperative video. Methodologically, we propose a Snapshot-Centric Attention (SCA) module to fuse multi-scale temporal features and construct CholeScore—the first video-level dataset with expert-annotated LCOD labels. Experiments demonstrate that our approach achieves a 0.22-point improvement over baselines on a newly proposed metric, with ≥9% gain in F1-score and +5% in Top-1 accuracy on CholeScore. This work establishes the first benchmark and paradigm for real-time LCOD estimation directly from surgical video, enabling timely expert intervention and dynamic operating room scheduling.

Technology Category

• Computer Vision: Video Understanding & Activity Analysis
• Search and Optimization: Learning to Search
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Purpose: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limited efforts explore LCOD using intraoperative video data. Early recog- nition of LCOD could allow prompt review by expert surgeons, enhance operating room (OR) planning, and improve surgical outcomes. Methods: We propose the clinical task of early LCOD assessment using limited video observations. We design SurgPrOD, a deep learning model to assess LCOD by analyzing features from global and local temporal resolutions (snapshots) of the observed LC video. Also, we propose a novel snapshot-centric attention (SCA) module, acting across snapshots, to enhance LCOD prediction. We introduce the CholeScore dataset, featuring video-level LCOD labels to validate our method. Results: We evaluate SurgPrOD on 3 LCOD assessment scales in the CholeScore dataset. On our new metric assessing early and stable correct predictions, SurgPrOD surpasses baselines by at least 0.22 points. SurgPrOD improves over baselines by at least 9 and 5 percentage points in F1 score and top1-accuracy, respectively, demonstrating its effectiveness in correct predictions. Conclusion: We propose a new task for early LCOD assessment and a novel model, SurgPrOD analyzing surgical video from global and local perspectives. Our results on the CholeScore dataset establishes a new benchmark to study LCOD using intraoperative video data.

Problem

• Early assessment of laparoscopic cholecystectomy difficulty
• Deep learning model for surgical video analysis
• Improving surgical outcomes with intraoperative video data

Innovation

• Deep learning for LCOD assessment
• Snapshot-centric attention module
• CholeScore dataset for validation