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Measuring hand movement for suturing skill assessment: A simulation-based study.

Amir Mehdi Shayan¹, Simar Singh¹, Jianxin Gao²

¹Department of Bioengineering, Clemson University, SC.

|August 19, 2023

Summary

This summary is machine-generated.

This study introduces an instrumented suturing simulator using rotational motion analysis to objectively assess surgical skills. The system effectively differentiates between novice, resident, and attending surgeon skill levels, offering a more reliable training tool.

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Area of Science:

Biomedical Engineering
Surgical Education
Medical Simulation

Background:

Simulation-based training is crucial for surgical skills education and patient safety.
Current assessment tools often rely on subjective and resource-intensive human raters.
Automated metrics from sensor data offer objective and efficient performance assessment.

Purpose of the Study:

To develop and validate an instrumented bench suturing simulator for objective assessment of open suturing skills.
To evaluate the effectiveness of automated hand motion metrics, particularly rotational analysis, in differentiating surgical skill levels.

Main Methods:

97 participants (35 attending surgeons, 32 residents, 30 novices) were recruited.
An inertial measurement unit (IMU) was used to capture hand motion data.
Rotational motion analysis metrics were developed to assess suturing performance.

Main Results:

All developed metrics significantly differentiated between novices and experienced surgeons (attendings/residents).
Novel rotational motion metrics distinguished finer skill differences between attending and resident groups.
Traditional metrics like time and path length failed to differentiate between attendings and residents.

Conclusions:

Rotational motion analysis is effective for objectively assessing suturing skills.
IMU-based hand motion metrics provide valuable data for surgical skill assessment.
This technology enhances the objectivity and efficiency of surgical simulator-based training.