A Standardized Temporal Segmentation Framework and Annotation Resource Library in Robotic Surgery

Busisiwe Mlambo MD , Mallory Shields PhD , Simon Bach MD , Armin Bauer PhD , Andrew Hung MD , Omar Yusef Kudsi MD , Felix Neis MD , John Lazar MD , Daniel Oh MD , Robert Perez MD , Seth Rosen MD , Naeem Soomro MD , Michael Stany MD , Mark Tousignant MD , Christian Wagner MD , Ken Whaler MS , Lilia Purvis MS , Benjamin Mueller BS , Sadia Yousaf MD , Casey Troxler BS , Anthony Jarc PhD
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引用次数: 0

Abstract

Objective

To develop and share the first clinical temporal annotation guide library for 10 robotic procedures accompanied with a standardized ontology framework for surgical video annotation.

Patients and Methods

A standardized temporal annotation framework of surgical videos paired with consistent, procedure-specific annotation guides is critical to enable comparisons of surgical insights and facilitate large-scale insights for exceptional surgical practice. Existing ontologies and guidance not only provide foundational frameworks but also provide limited scalability in clinical settings. Building on these, we developed a temporal annotation framework with nested surgical phases, steps, tasks, and subtasks. Procedure-specific annotation resource guides consistent with this framework that define each surgical segment with formulaic start and stop parameters and surgical objectives were iteratively created across 7 years (January 1, 2018, to January 1, 2025) through global research collaborations with surgeon researchers and industry scientists.

Results

We provide the first resource library of annotation guides for 10 common robotic procedures consistent with our proposed temporal annotation framework, enabling consistent annotations for clinicians and large-scale data comparisons with computer-readable examples. These have been used in over 13,000 annotated surgical cases globally, demonstrating reproducibility and broad applicability.

Conclusion

This resource library and accompanying ontology framework provide critical structure for standardized temporal segmentation in robotic surgery. This framework has been applied globally in private studies examining surgical objective performance metrics, surgical education, workflow characterization, outcome prediction, algorithms for surgical activity recognition, and more. Adoption of these resources will unify clinical, academic, and industry efforts, ultimately catalyzing transformational advancements in surgical practice.
机器人手术标准化时间分割框架及标注资源库
目的开发并共享首个针对10个机器人手术过程的临床时间注释指南库,并提供标准化的手术视频注释本体框架。手术视频的标准化时间注释框架与一致的、特定于手术的注释指南相结合,对于实现手术见解的比较和促进对特殊手术实践的大规模见解至关重要。现有的本体和指南不仅提供了基础框架,而且在临床环境中提供了有限的可扩展性。在此基础上,我们开发了一个具有嵌套手术阶段、步骤、任务和子任务的临时注释框架。通过与外科医生研究人员和行业科学家的全球研究合作,在7年(2018年1月1日至2025年1月1日)期间迭代创建了与该框架一致的特定程序注释资源指南,该指南使用公式化的开始和停止参数和手术目标定义了每个手术段。我们提供了第一个与我们提出的时间注释框架一致的10种常见机器人程序的注释指南资源库,使临床医生能够进行一致的注释,并与计算机可读的示例进行大规模数据比较。这些方法已在全球超过13,000例带注释的手术病例中使用,证明了可重复性和广泛的适用性。结论该资源库及其配套的本体框架为机器人手术中标准化的时间分割提供了关键结构。该框架已在全球范围内的私人研究中应用,用于检查手术客观绩效指标,外科教育,工作流程表征,结果预测,手术活动识别算法等。这些资源的采用将统一临床、学术和行业的努力,最终促进外科实践的转型进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Mayo Clinic Proceedings. Digital health
Mayo Clinic Proceedings. Digital health Medicine and Dentistry (General), Health Informatics, Public Health and Health Policy
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