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This research aims to improve skill assessment in open surgery by classifying surgical tasks using hand kinematics.

Cross‐Stream and Cross‐Channel Attention Networks for Surgical Skill Classification in Open Surgery From Hand Kinematics

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This research aims to improve skill assessment in open surgery by classifying surgical tasks using hand kinematics.
Twenty trainees performed three open surgery tasks: knot tying (KT), continuous suturing (CS), and interrupted suturing (IS), totaling 201 trials.
Deep learning models, specifically LSTM and Transformer architectures with cross-stream and cross-channel attention, were evaluated for binary skill classification.
Agreement with an independent reviewer was assessed to validate the model's performance.
The inter-hand model outperformed the reviewer in accuracy for all tasks: 0.88 versus 0.86 for KT, 0.84 versus 0.83 for CS, and 0.81 versus 0.68 for IS.
High-skill recognition was more effective for knot tying, while low-skill recognition was better for continuous and interrupted suturing.

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Randomized trial evaluates surgical skill classification through hand kinematics, highlighting performance indicators in trainees.

Loukas et al. (2026) studied this question.

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