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Related Experiment Video

Updated: May 5, 2026

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Kinematic Analysis Using 3D Motion Capture of Drinking Task in People With and Without Upper-extremity Impairments

Published on: March 28, 2018

Cutting Skill Assessment by Motion Analysis Using Deep Learning and Spatial Marker Tracking.

Bai-Quan Su, Xu-Dong Ma, Weihan Li

IEEE Transactions on Bio-Medical Engineering

|March 3, 2025

Summary

This summary is machine-generated.

This study introduces objective scalpel motion analysis for open surgery skill assessment. It quantifies cutting precision using five novel metrics, enabling accurate evaluation of surgeon proficiency.

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Area of Science:

Surgical Skill Assessment
Biomechanics
Computer Vision

Background:

Objective assessment of open surgery skills is lacking, relying on subjective visual observation.
Current methods for evaluating surgical proficiency in open procedures are often imprecise and lack standardization.
Motion analysis is established in endoscopic surgery but not widely adopted for open surgery.

Purpose of the Study:

To develop and validate a novel method for objectively evaluating cutting skill in open surgery using scalpel motion analysis.
To introduce quantitative metrics for assessing surgeon proficiency based on scalpel trajectories.
To establish a standardized approach for open surgery skill evaluation.

Main Methods:

Designed a 3D multiple-facet ArUco code cube for tracking scalpel motion.
Utilized pivot calibration and YOLOv8 model for scalpel identification and tip localization.
Transformed scalpel tip poses from ArUco code coordinate system (ACS) to binocular camera coordinate system (BCS) to define incision curves.
Proposed five metrics: average incision curvature deviation, incision length difference, incision endpoint deviation, average incision deviation, and average cutting jerk.

Main Results:

Successfully established a dataset of scalpel tip poses and transformed them into incision curves.
Demonstrated the effectiveness of the five proposed metrics in quantifying cutting skill.
Experiments with 20 surgeons showed objective differentiation between expert and novice skill levels.
The metrics provided a clear display of individual cutting skills and comparative rankings.

Conclusions:

The proposed scalpel motion analysis method offers a precise and objective approach to evaluating open surgery cutting skills.
The developed metrics provide quantitative insights into surgeon proficiency, addressing the limitations of subjective assessment.
This study lays the groundwork for standardized, data-driven evaluation of surgical competence in open procedures.