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COMPASS: a formal framework and aggregate dataset for generalized surgical procedure modeling.

Kay Hutchinson1, Ian Reyes2,3, Zongyu Li4

  • 1Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, 22903, USA. kch4fk@virginia.edu.

International Journal of Computer Assisted Radiology and Surgery
|May 5, 2023
PubMed
Summary

A new framework models surgical tasks using motion primitives (MPs) for objective labeling and data aggregation. This approach enhances surgical process analysis, skill assessment, and autonomy by creating a comprehensive dataset for detailed bimanual coordination studies.

Keywords:
Minimally invasive surgeryRobotic surgerySurgical contextSurgical gesture recognitionSurgical process modeling

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Area of Science:

  • Robotics and Artificial Intelligence in Surgery
  • Surgical Data Science
  • Human-Computer Interaction in Medicine

Background:

  • Minimally invasive surgery (MIS) requires objective analysis of complex tasks.
  • Current surgical datasets are often heterogeneous and difficult to aggregate.
  • Standardized methods for surgical task modeling and segmentation are needed.

Purpose of the Study:

  • To propose a formal framework for modeling and segmenting minimally invasive surgical tasks.
  • To enable objective labeling and aggregation of diverse surgical datasets.
  • To facilitate the development of advanced AI models for surgical analysis.

Main Methods:

  • Surgical tasks modeled as finite state machines using motion primitives (MPs) as basic actions.
  • Surgical context labeling developed from video data with automatic translation to MP labels.
  • Creation of the COntext and Motion Primitive Aggregate Surgical Set (COMPASS) from three public datasets.

Main Results:

  • Context labeling achieved near-perfect agreement between crowd-sourcing and expert surgeons.
  • The COMPASS dataset nearly triples existing data for modeling and analysis.
  • Enabled generation of separate transcripts for left and right tool movements.

Conclusions:

  • The framework provides high-quality, context-based labeling of surgical data.
  • MPs enable dataset aggregation and separate analysis of bimanual coordination.
  • Supports development of explainable AI for improved surgical process analysis, skill assessment, error detection, and autonomy.