Quantitative assessment of laparoscopic camera navigation skill: a retrospective cross-sectional study

Area of Science:

• Surgical Education Technology
• Minimally Invasive Surgery Training
• Objective Skill Assessment

Background:

• Laparoscopic camera navigation (LCN) is critical for minimally invasive surgery success.
• Current LCN assessment methods are subjective, labor-intensive, and not scalable.
• There is a need for objective and efficient LCN skill evaluation tools.

Purpose of the Study:

• To evaluate a low-cost, automated tool for objective LCN skill assessment.
• To determine if the tool can differentiate between varying levels of surgical experience.
• To validate trajectory-based metrics and real-time computer vision for skill evaluation.

Main Methods:

• A cross-sectional observational validation study involving 59 participants (experts to novices).
• Participants performed a standardized LCN task using a custom maze and laparoscope.
• Real-time computer vision and electromagnetic tracking captured trajectory data; automated metrics (space coverage, jerk, smoothness, idle time, total time) were collected and analyzed using PCA for a composite score.

Main Results:

• Experts showed significantly better performance across all metrics compared to novices.
• Key metrics included lower space coverage and shorter task completion times for experts.
• PCA-derived composite scores were significantly higher for experts (92±3) than novices (34±24) (p < .005).

Conclusions:

• The automated, low-cost tool reliably quantifies LCN skill using interpretable metrics.
• The tool demonstrates objectivity and scalability, suitable for surgical education.
• It can be integrated for formative feedback and standardized assessment of LCN proficiency.