Egocentric video analysis for automated assessment of open surgical skills via deep learning.

Purpose: While significant progress has been made in skill assessment for minimally invasive procedures, objective evaluation methods for open surgery remain limited. This paper presents a deep learning framework for assessing technical surgical skills using egocentric video data from open surgery training.

Methods: Our dataset includes 201 videos and corresponding hand kinematics data from three fundamental training task-knot tying (KT), continuous suturing (CS), and interrupted suturing (IS)-performed by 20 participants. Each video was annotated by two experts using a modified OSATS scale (KT: five criteria, total score range: 5-25; CS/IS: seven criteria, total score range: 7-35). We evaluate three temporal architectures (LSTM, TCN, and Transformer), each using ResNet50 as the backbone for spatial feature extraction, and assess them under various training strategies: single-task learning, feature concatenation, pretraining, and multi-task learning with integrated kinematic data. Performance metrics included mean absolute error (MAE) and Spearman correlation coefficient ( $\rho$ ), both with respect to total score prediction.

Results: The Transformer-based models consistently outperformed LSTM and TCN across all tasks. The multi-task Transformer incorporating prediction of task completion time ( ${\text{Transf-MT}}_{\text{T+S}}$ ) achieved the lowest MAE (KT: 1.92, CS: 2.81, and IS: 2.89) and $\rho$ = 0.84- $-$ 0.90. It also demonstrated promising capabilities for early skill assessment by predicting the total score from partial observations-particularly for simpler tasks. Additionally, we show that models trained on consensus expert ratings outperform those trained on individual annotations, highlighting the value of multi-rater ground truth.

Conclusion: This research provides a foundation for objective, automated assessment of open surgical skills, with potential to improve the efficiency and standardization of surgical training.