Automated Performance Metrics for Objective Surgical Skill Assessment in Laparoscopic Training

Asaf Arad, Julia Leyva I. Torres, Kristian Nyborg Jespersen, Nicolaj Boelt Pedersen, Pablo Rey Valiente, Alaa El-Hussuna, Andreas Møgelmose

Abstract: The assessment of surgical skill is critical in advancing surgical training and enhancing the performance of surgeons. Traditional evaluation methods relying on human observation and checklists are often biased and inefficient, prompting the need for automated and objective systems. This study explores the use of Automated Performance Metrics (APMs) in laparoscopic surgeries, using video-based data and advanced object tracking techniques. A pipeline was developed, combining a fine-tuned YOLO11 model for detection with state-of-the-art multi-object trackers (MOTs) for tracking surgical tools. Metrics such as path length, velocity, acceleration, jerk, and working area were calculated to assess technical performance. BoT-SORT emerged as the most effective tracker, achieving the highest HOTA and MOTA, enabling robust tool tracking. The system successfully extracted APMs to evaluate and compare surgical performance, demonstrating its potential for objective assessment. This work validates

External IDs:dblp:conf/visigrapp/AradTJPVEM25