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Purpose <p>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 recognition of LCOD could allow prompt review by expert surgeons, enhance operating room (OR) planning, and improve surgical outcomes.</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>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 establish a new benchmark to study LCOD using intraoperative video data.</p>

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Early operative difficulty assessment in laparoscopic cholecystectomy via snapshot-centric video analysis

  • 期刊论文

  • 发表日期：

    2025年4月21日

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  • Saurav Sharma,
  • Maria Vannucci,
  • Leonardo Pestana Legori,
  • Mario Scaglia,
  • Giovanni Guglielmo Laracca,
  • Didier Mutter,
  • Sergio Alfieri,
  • Pietro Mascagni,
  • Nicolas Padoy

摘要

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 recognition 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 establish a new benchmark to study LCOD using intraoperative video data.