ArticleMayo Clinic proceedings. Digital health2025
A Standardized Temporal Segmentation Framework and Annotation Resource Library in Robotic Surgery.
Article in Mayo Clinic proceedings. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Ruling the robot: a phase-based analysis of resident autonomy across core robotic general surgery procedures.Surgical endoscopy · 2026Article
- Measurement of trainee autonomy in robotic-assisted lung resections using objective performance indicators.Journal of robotic surgery · 2026Article
- Applications of Reinforcement Learning for Autonomous Surgical Robotics: A Systematic Review.Biomimetics (Basel, Switzerland) · 2026Review
- Standardization of surgical gesture taxonomy: a SAGES Delphi consensus study.Surgical endoscopy · 2026Article
- Weakly supervised pre-training for surgical step recognition using unannotated and heterogeneously labeled videos.International journal of computer assisted radiology and surgery · 2026Article
- Opportunities and obstacles in harnessing intraoperative data.Journal of abdominal wall surgery : JAWS · 2026Article
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Authors and funding
24 authors.
Funding
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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.
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Registered trials
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