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  1. Home
  3. Ai-based Markerless Computer Vision Framework For Open Surgery Skill Assessment: A Prototype Assessment Framework.
  1. Home
  3. Ai-based Markerless Computer Vision Framework For Open Surgery Skill Assessment: A Prototype Assessment Framework.

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Related Experiment Video

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality
07:46

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality

Published on: August 9, 2024

AI-Based Markerless Computer Vision Framework for Open Surgery Skill Assessment: A Prototype Assessment Framework.

Alejandro Zulbaran-Rojas1, Mohammad Dehghan Rouzi1,2, Natasha Hansraj1,3

  • 1Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA.

Surgical Innovation
|May 14, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study shows an AI system can track hand motions from surgical videos, generating skill scores that align with expert evaluations. This technology offers a feasible way to assess surgical performance using kinematics.

Keywords:
artificial intelligencecomputer visiondeep learninghand kinematicshuman motion analysisinterpretable performance metricsmarkerless motion capturemotion visualizationsurgical educationsurgical skill assessment

Related Experiment Videos

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality
07:46

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality

Published on: August 9, 2024

Area of Science:

  • Surgical Education
  • Medical Technology
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) enables hand motion tracking from standard surgical video recordings.
  • Translating these data into meaningful performance metrics remains challenging.
  • Evaluating the validity of a markerless, AI-driven system for technical skill scoring in open surgery is crucial.

Purpose of the Study:

  • To evaluate the preliminary validity of a markerless, AI-driven system for generating interpretable technical skill scores from an open-surgery task.
  • To assess the correlation between AI-derived kinematic parameters and expert-rated surgical performance.
  • To determine the feasibility of video-based kinematic scoring in surgical training.

Main Methods:

  • Sixteen medical students and one instructor performed a one-handed knot-tying task.
  • A deep learning algorithm tracked 21 hand joints from smartphone video recordings.
  • Kinematic parameters were derived and grouped into economy of motion (EM), flow of motion (FM), and spatial organization (SO) domains, then correlated with expert assessments.

Main Results:

  • AI metrics showed strong correlations with sensor-based measures (r = 0.79-0.88, P < 0.01).
  • EM metrics correlated with product quality (PQ) and technical performance (TP) (r = 0.59-0.67, P < 0.01).
  • Smoothness within the FM domain correlated with PQ and TP (r = 0.56-0.57, P < 0.01).

Conclusions:

  • The AI framework translated hand kinematics into interpretable, cohort-normalized domain-level scores.
  • These AI-derived scores aligned with expert assessment, supporting preliminary construct validity.
  • Findings support the feasibility of video-based kinematic scoring for surgical training, warranting further studies on reliability and generalizability.