Functional Data Analysis of Hand Rotation for Open Surgical Suturing Skill Assessment.

Area of Science:

• Surgical Education
• Biomechanical Analysis
• Data Science

Background:

• Assessing surgical skill objectively is crucial for effective training.
• Traditional metrics may not capture the nuances of dynamic movements like suturing.
• Simulation-based training offers a controlled environment for skill evaluation.

Purpose of the Study:

• To apply Functional Data Analysis (FDA) to hand roll velocity during simulated radial suturing.
• To evaluate the effectiveness of FDA in differentiating surgical skill levels.
• To enhance objective feedback and efficiency in surgical simulation training.

Main Methods:

• Hand roll velocity data from 96 subjects on the SutureCoach simulator were analyzed.
• Functional k-means clustering with dynamic time-warping was used to group subjects.
• Performance was categorized into advanced surgeons, trainees, and novices.

Main Results:

• Both 3-cluster and 6-cluster models successfully grouped subjects by distinct skill characteristics.
• Cluster membership correlated significantly with expert ratings, clinical status, and simulator metrics.
• FDA provided interpretable and clinically relevant insights into suturing performance dynamics.

Conclusions:

• Functional data analysis is a powerful tool for objective surgical skill assessment.
• FDA can identify key components of suturing proficiency.
• This approach can improve the autonomy and efficiency of simulation-based surgical training.