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Cross-Stream and Cross-Channel Attention Networks for Surgical Skill Classification in Open Surgery From Hand
Constantinos Loukas1, Konstantina Prevezanou1, Ioannis Seimenis1
1Laboratory of Medical Physics, Medical School, National and Kapodistrian University of Athens, Athens, Greece.
Background:
Despite the extensive research on skill assessment in minimally invasive surgery, applications in open surgery (OS) remain limited.
Methods:
Twenty trainees performed three OS tasks-knot tying (KT), continuous suturing (CS), and interrupted suturing (IS)-yielding 201 trials. Various deep learning models based on LSTM and Transformer were evaluated for binary skill classification using hand kinematics. The proposed architecture integrates cross-stream and cross-channel attention mechanisms to capture inter-hand, intra-hand, and stream-level motion interactions. Agreement with an independent reviewer was also assessed.
Results:
The inter-hand model achieved the best performance across all tasks and outperformed the reviewer in multiple metrics (e.g., Accuracy: 0.88 vs. 0.86 KT; 0.84 vs. 0.83 CS; 0.81 vs. 0.68 IS). High-skill recognition was better for KT, whereas low-skill recognition was better for the more demanding tasks, CS and IS.
Conclusions:
Our study highlights the importance of capturing hand kinematic relationships as key indicators of surgical performance.