BACKGROUND: We sought to define the extent to which a motion analysis-based assessment system constructed with simple equipment could measure technical skill objectively and quantitatively. METHODS: An "off-the-shelf" digital video system was used to capture the hand and instrument movement of surgical trainees (beginner level = PGY-1, intermediate level = PGY-3, and advanced level = PGY-5/fellows) while they performed a peg transfer exercise. The video data were passed through a custom computer vision algorithm that analyzed incoming pixels to measure movement smoothness objectively. RESULTS: The beginner-level group had the poorest performance, whereas those in the advanced group generated the highest scores. Intermediate-level trainees scored significantly (p < 0.04) better than beginner trainees. Advanced-level trainees scored significantly better than intermediate-level trainees and beginner-level trainees (p < 0.04 and p < 0.03, respectively). CONCLUSIONS: A computer vision-based analysis of surgical movements provides an objective basis for technical expertise-level analysis with construct validity. The technology to capture the data is simple, low cost, and readily available, and it obviates the need for expert human assessment in this setting.
BACKGROUND: We sought to define the extent to which a motion analysis-based assessment system constructed with simple equipment could measure technical skill objectively and quantitatively. METHODS: An "off-the-shelf" digital video system was used to capture the hand and instrument movement of surgical trainees (beginner level = PGY-1, intermediate level = PGY-3, and advanced level = PGY-5/fellows) while they performed a peg transfer exercise. The video data were passed through a custom computer vision algorithm that analyzed incoming pixels to measure movement smoothness objectively. RESULTS: The beginner-level group had the poorest performance, whereas those in the advanced group generated the highest scores. Intermediate-level trainees scored significantly (p < 0.04) better than beginner trainees. Advanced-level trainees scored significantly better than intermediate-level trainees and beginner-level trainees (p < 0.04 and p < 0.03, respectively). CONCLUSIONS: A computer vision-based analysis of surgical movements provides an objective basis for technical expertise-level analysis with construct validity. The technology to capture the data is simple, low cost, and readily available, and it obviates the need for expert human assessment in this setting.
Authors: Colin F Mackenzie; Shiming Yang; Evan Garofalo; Peter Fu-Ming Hu; Darcy Watts; Rajan Patel; Adam Puche; George Hagegeorge; Valerie Shalin; Kristy Pugh; Guinevere Granite; Lynn G Stansbury; Stacy Shackelford; Samuel Tisherman Journal: World J Surg Date: 2021-01-03 Impact factor: 3.352