Related Experiment Video
Updated: Oct 3, 2026
Learning Modern Laryngeal Surgery in a Dissection Laboratory
Published on: March 18, 2020
Decoding surgical competence: surgical gesture analysis can determine competence in simulated VATS lobectomy, a
Annarita Ghosh1,2, Rikke Groth Olsen3,4, Tamim Haidari5
1Department of Cardiothoracic Surgery, Rigshospitalet, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark. annarita.ghosh.dyhr@regionh.dk.
Background:
Minimally invasive thoracic surgery has become the standard of care, but is technically demanding, making objective assessment of technical competency essential during training. Video based surgical gesture analysis is a novel and objective method to evaluate technical skills during training for video-assisted thoracoscopic (VATS) lobectomies.
Methods:
VATS lobectomy videos performed on a virtual reality simulator were annotated using a predefined surgical gesture taxonomy including blunt dissection, sharp dissection, ribbon use, and stapler use. Gesture metrics included duration, frequency, idle time, and entropy. Principal component analysis and a support vector machine were used to generate gesture-based performance scores. Validity evidence was assessed against simulator-derived metrics and human expert ratings using the VATS assessment tool (VATSAT).
Results:
Surgical gesture analysis (SGA) was performed on 123 simulated VATS lobectomy procedures where 22 novices, 10 intermediates, and 9 experienced VATS surgeons performed three procedures each. Experienced surgeons had shorter procedure time (24.2 ± 9.2 min) than both intermediates (35.5 ± 9.9 min, p < 0.001) and novices (40.1 ± 14.3 min, p < 0.001), and mean gesture duration was significantly shorter for experienced (7 ± 1 s) compared with intermediates (8 ± 2 s, p = 0.026) and novices (11 ± 3 s, p < 0.001). Variability in gesture duration also decreased with experience (p < 0.001) and overall entropy showed significant group differences for novices compared with intermediates (p = 0.02) and experts (p = 0.049). Gestures correlated with simulator metrics and VATSAT scores, suggesting these reflected similar performance dimensions. SGA was able to classify experience level with a pass/fail threshold, achieving a sensitivity of 96.1% and specificity of 97.5%.
Conclusion:
SGA can be used to assess technical performance for simulated VATS lobectomies. It was able to differentiate between experience levels for multiple parameters, and has potential advantages compared with existing assessments such as rating scales and simulator metrics. Further studies are needed to explore the applicability on real procedures and the clinical implications of Surgical Gesture Analysis.
