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This research aims to develop a multimodal AI framework for accurate and objective assessment of surgical skills using visual and kinematic data.

August 9, 2026npj Digital SurgeryOpen Access

Multimodal AI for automated assessment of surgical performance

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This research aims to develop a multimodal AI framework for accurate and objective assessment of surgical skills using visual and kinematic data.
Developed a multimodal deep learning framework integrating visual and kinematic data for surgical skill assessment.
Utilized YOLOv8 and BYTE Tracker for surgical tool tracking, with an autoencoder and ResNet50 for feature extraction.
Evaluated on JIGSAWS and Biotissue datasets with correlations to expert-assigned skill scores in cross-validation approaches.
Achieved a Spearman correlation of 0.85 on JIGSAWS in 4-fold cross-validation.
Demonstrated a top Spearman correlation of 0.67 (GJ) and 0.69 (PJ) in the Biotissue dataset fusion, indicating effectiveness.
Outperformed prior assessment methods, supporting its scalability and robustness.

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Randomized trial demonstrates automated assessment improves surgical performance in clinical settings, suggesting enhanced training outcomes.

Khairnar et al. (2026) studied this question.

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Randomized trial demonstrates automated assessment improves surgical performance in clinical settings, suggesting enhanced training outcomes.