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

IF 2.3 3区 医学 Q3 ENGINEERING, BIOMEDICAL
Saurav Sharma, Maria Vannucci, Leonardo Pestana Legori, Mario Scaglia, Giovanni Guglielmo Laracca, Didier Mutter, Sergio Alfieri, Pietro Mascagni, Nicolas Padoy
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引用次数: 0

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 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.

基于快照中心视频分析的腹腔镜胆囊切除术早期手术难度评估。
目的:腹腔镜胆囊切除术(LC)手术难度(LCOD)变化很大,影响手术结果。尽管LC在外科工作流程分析方面有广泛的研究,但利用术中视频数据探索LCOD的努力有限。早期识别LCOD可以让外科专家及时检查,加强手术室(OR)计划,改善手术效果。方法:我们提出了利用有限的视频观察进行早期LCOD评估的临床任务。我们设计了一个深度学习模型SurgPrOD,通过分析观察到的LC视频的全局和局部时间分辨率(快照)的特征来评估LCOD。此外,我们提出了一种新的以快照为中心的注意力(SCA)模块,该模块跨快照工作,以增强LCOD预测。我们引入了具有视频级LCOD标签的CholeScore数据集来验证我们的方法。结果:我们在CholeScore数据集的3个LCOD评估量表上对SurgPrOD进行了评估。在我们评估早期和稳定正确预测的新指标上,SurgPrOD超过基线至少0.22个点。在F1得分和top1准确率方面,SurgPrOD分别比基线提高了至少9个百分点和5个百分点,证明了其在正确预测方面的有效性。结论:我们提出了一种新的LCOD早期评估任务和一种新的模型,即SurgPrOD,从全局和局部角度分析手术视频。我们在CholeScore数据集上的研究结果为使用术中视频数据研究LCOD建立了新的基准。
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来源期刊
International Journal of Computer Assisted Radiology and Surgery
International Journal of Computer Assisted Radiology and Surgery ENGINEERING, BIOMEDICAL-RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
CiteScore
5.90
自引率
6.70%
发文量
243
审稿时长
6-12 weeks
期刊介绍: The International Journal for Computer Assisted Radiology and Surgery (IJCARS) is a peer-reviewed journal that provides a platform for closing the gap between medical and technical disciplines, and encourages interdisciplinary research and development activities in an international environment.
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