IBC Oxford University Oral Abstract 49 - AI-assisted surgical fellowship training assessment through laparoscopic sleeve gastrectomy video analytics

Abstract Background The advent of artificial intelligence (AI) in surgical training presents a transformative opportunity to enhance the assessment and education of surgical fellows. This study investigates the application of AI in evaluating the performance of surgeons by analyzing videos of laparoscopic sleeve gastrectomy procedures uploaded to a specialized platform, Surgeryview.ai. Methods Three surgeons contributed to this study: two were professor bariatric surgeons and one was a fellow bariatric surgeon in training. A total of 466 videos of gastric sleeve surgeries were analyzed. Of these, 413 videos were performed by the professor surgeons (Surgeon 1: 180, Surgeon 2: 233) between September 2022 and May 2024, and 53 surgeries were performed by the fellow surgeon in training over a period of 10 months (August 2023–May 2024). The analyzed videos were chosen randomly, and any surgeries Involving an additional procedure to the gastric sleeve (for example hiatal hernia repair) were excluded. Patient data, including gender, age, and initial BMI, were recorded, ensuring anonymity. Results The AI platform provided comprehensive longitudinal analytics, detailing the time taken to complete each surgical step: access to the abdominal cavity, greater curvature dissection, stapling, specimen removal, oversewing, and leak test. The platform also analyzed the impact of patient demographics on surgery time. Professors showed consistent efficiency, while fellows demonstrated significant improvement over time. Notably, surgery duration was influenced by patient-specific factors such as BMI and age. The fellow bariatric surgeon in training showed significant improvement across all metrics over the trimesters, with overall surgery time reduced by approximately 23.83%, greater curvature dissection (step 2) time by 28.37%, and oversuture of the staple line (step 5) time by 28.74%. When broken down by BMI, age, and gender, indicate that improvements are generally consistent across these variables, with the most significant improvements seen in lower BMI and younger age groups. These advancements indicate that the fellow has substantially closed the performance gap, particularly in the critical surgical steps. Conclusion AI assisted platforms like Surgeryview.ai offer valuable insights into surgical performance, enabling objective assessment and personalized feedback for fellows. This technology can enhance surgical training programs by identifying specific areas for improvement and tracking progress over time, ultimately aiming to standardize surgical education and improve patient outcomes.