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Related Experiment Video

Updated: Jan 16, 2026

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

A Comprehensive Protocol for Manual Segmentation of the Medial Temporal Lobe Structures

Published on: July 2, 2014

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

Busisiwe Mlambo¹, Mallory Shields¹, Simon Bach²

¹Advanced Research and Analytics, Intuitive Surgical, Sunnyvale, CA.

Mayo Clinic Proceedings. Digital Health

|October 6, 2025

Summary

This summary is machine-generated.

This study introduces the first clinical temporal annotation guide library and ontology framework for robotic surgery. This standardized approach enables consistent surgical video analysis and facilitates advancements in surgical practice globally.

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Area of Science:

Robotic Surgery
Surgical Video Analysis
Medical Informatics

Background:

Standardized temporal annotation of surgical videos is crucial for comparing surgical insights and identifying best practices.
Existing ontologies lack scalability for clinical settings.
A novel temporal annotation framework with nested surgical phases, steps, tasks, and subtasks was developed.

Purpose of the Study:

To develop and share the first clinical temporal annotation guide library for 10 robotic procedures.
To establish a standardized ontology framework for surgical video annotation.
To enable consistent annotations and large-scale data comparisons in robotic surgery.

Main Methods:

Iterative creation of procedure-specific annotation guides over 7 years (2018-2025).
Global research collaboration with surgeons and industry scientists.
Development of a temporal annotation framework with nested surgical phases, steps, tasks, and subtasks.

Main Results:

A library of annotation guides for 10 common robotic procedures was created, consistent with the temporal annotation framework.
The framework enables consistent annotations for clinicians and large-scale data comparisons.
Over 13,000 surgical cases have been annotated globally, demonstrating reproducibility and broad applicability.

Conclusions:

The resource library and ontology framework provide structure for standardized temporal segmentation in robotic surgery.
The framework supports diverse applications including performance metrics, education, workflow analysis, and AI development.
Adoption of these resources will unify efforts and advance surgical practice.