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Can Microinstrument Motion Metrics of Distance, Speed, and Acceleration Indicate Surgical Task Complexity? An

Gleb Danilov1, Vasiliy Kostyumov1,2, Oleg Pilipenko1,2

  • 1Laboratory of Biomedical Informatics and Artificial Intelligence.

Studies in Health Technology and Informatics
|April 9, 2025
PubMed
Summary

AI-driven analysis of microinstrument motion can objectively measure the complexity of microsurgical tasks. These motion metrics offer a novel way to assess surgical skill and adapt to changing operative conditions.

Keywords:
Neurosurgeryartificial intelligencecomputer visionmotion featuressegmentationskills

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Area of Science:

  • Neurosurgery
  • Surgical Skill Assessment
  • Biomechanical Engineering

Background:

  • Objectifying microsurgical technique quality is essential but difficult.
  • Understanding how task complexity affects surgeon motion is crucial for training and evaluation.

Purpose of the Study:

  • To determine if microinstrument motion metrics can reflect the complexity of microsurgical tasks.
  • To investigate the impact of wrist stabilization and muscle load on microsurgical motion patterns.

Main Methods:

  • 13 neurosurgeons performed a thread-cutting task under varying conditions (wrist stabilization, muscle load).
  • A promptable transformer model segmented instruments, extracting motion data (center of mass, velocity, acceleration, jerk, smoothness).
  • 33 statistical features were derived from motion time series, and compared across conditions using the feasts R package.

Main Results:

  • Significant differences in 66 motion features (9.2% of 1782 tests) were found across task conditions.
  • AI-derived motion features demonstrated sensitivity to changes in surgical complexity.
  • The study provides proof-of-concept for using motion analysis in microsurgery.

Conclusions:

  • AI-derived microsurgical motion features can objectively reflect task complexity.
  • These metrics offer a potential tool for assessing surgical proficiency and adapting to operative challenges.
  • Further research can refine these metrics for real-world surgical training and feedback systems.