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Reliability Volume: a novel metric for surgical skill evaluation
Zhipu Yu1, Qinghua Liu1, Jin Zhang2
1School of Physics and Electronic Information, Yan'an University, Yan'an, China.
Frontiers in Medicine
|October 3, 2025
Summary
This study introduces Reliability Volume (RV), a new metric for surgical skill assessment. RV evaluates both precision and consistency over time, offering a better way to track learning during repetitive training.
Area of Science:
- Surgical training and assessment
- Medical simulation
- Robotic surgery
Background:
- Current surgical skill metrics often fail to capture performance changes over multiple sessions.
- Fatigue and performance drift are critical factors in surgical training that are not adequately addressed by single-session assessments.
- There is a need for comprehensive metrics that integrate spatial accuracy with performance consistency.
Purpose of the Study:
- To introduce Reliability Volume (RV), an integrated metric for evaluating surgical skill.
- To address the limitations of existing metrics by incorporating empirical reliability estimation.
- To provide a dynamic and practice-oriented assessment framework for surgical training.
Main Methods:
- Developed Reliability Volume (RV) by combining trajectory similarity with threshold counts for empirical reliability estimation.
- Applied RV to assess surgical skill in a knot-tying task with assistive devices over multiple sessions.
- Investigated RV's ability to quantify spatial precision and consistent task execution.
Main Results:
- Reliability Volume (RV) effectively quantifies both spatial precision and the probability of consistent task execution.
- RV demonstrated the ability to track learning progression over multiple training sessions.
- The metric showed compatibility with real-time feedback systems for closed-loop training.
Conclusions:
- Reliability Volume (RV) offers a dynamic, comprehensive, and practice-oriented framework for surgical skill assessment.
- RV addresses the limitations of single-session metrics by accounting for fatigue and performance drift.
- This metric has the potential to enhance surgical training by providing reliable, real-time feedback.
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