手术培训中的手术视频自动分析:范围界定综述。

IF 3.5 3区 医学 Q1 SURGERY
BJS Open Pub Date : 2024-09-03 DOI:10.1093/bjsopen/zrae124
Lachlan Dick, Connor P Boyle, Richard J E Skipworth, Douglas S Smink, Victoria Ruth Tallentire, Steven Yule
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引用次数: 0

摘要

背景:用于外科培训的手术视频越来越多。新兴技术现在可以评估视频片段并自动生成衡量标准,这些标准可用于改进手术表现评估。然而,目前还缺乏对哪些技术功能对外科培训影响最大的全面了解。本范围综述旨在探讨自动视频分析技术目前在外科培训中的应用:根据范围界定综述的 PRISMA 扩展(PRISMA-ScR)指南,对 PubMed、Scopus、Web of Science 和 Cochrane 数据库进行了检索,检索期至 2023 年 9 月 29 日。检索词包括 "学员"、"视频分析 "和 "教育"。文章由两名审稿人独立筛选,以确定将自动视频分析技术应用于学员操作的研究。结果:结果:在筛选出的 6736 篇文章中,确定了 13 项研究。计算机视觉跟踪是常用的视频分析方法。对过程(如器械移动)、结果(如术中阶段持续时间)和关键安全要素(如腹腔镜胆囊切除术中的关键安全观)进行了描述。自动化指标能够区分不同的技能水平(例如顾问与实习生),并与传统的评估方法相关联。缺乏对培训的纵向应用,只有一项定性研究报告了学员使用自动视频分析的经验:结论:自动视频分析产生的绩效指标多种多样,涵盖多个领域。对分析技术和生成的指标进行验证是未来研究的重点,之后可以建立证据来证明其对培训的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated analysis of operative video in surgical training: scoping review.

Background: There is increasing availability of operative video for use in surgical training. Emerging technologies can now assess video footage and automatically generate metrics that could be harnessed to improve the assessment of operative performance. However, a comprehensive understanding of which technology features are most impactful in surgical training is lacking. The aim of this scoping review was to explore the current use of automated video analytics in surgical training.

Methods: PubMed, Scopus, the Web of Science, and the Cochrane database were searched, to 29 September 2023, following PRISMA extension for scoping reviews (PRISMA-ScR) guidelines. Search terms included 'trainee', 'video analytics', and 'education'. Articles were screened independently by two reviewers to identify studies that applied automated video analytics to trainee-performed operations. Data on the methods of analysis, metrics generated, and application to training were extracted.

Results: Of the 6736 articles screened, 13 studies were identified. Computer vision tracking was the common method of video analysis. Metrics were described for processes (for example movement of instruments), outcomes (for example intraoperative phase duration), and critical safety elements (for example critical view of safety in laparoscopic cholecystectomy). Automated metrics were able to differentiate between skill levels (for example consultant versus trainee) and correlated with traditional methods of assessment. There was a lack of longitudinal application to training and only one qualitative study reported the experience of trainees using automated video analytics.

Conclusion: The performance metrics generated from automated video analysis are varied and encompass several domains. Validation of analysis techniques and the metrics generated are a priority for future research, after which evidence demonstrating the impact on training can be established.

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来源期刊
BJS Open
BJS Open SURGERY-
CiteScore
6.00
自引率
3.20%
发文量
144
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