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Author Spotlight: Revolutionizing Remote Surgery with Augmented Reality and Robotics for Enhanced Precision and Accessibility

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Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.

Romina Pedrett¹, Pietro Mascagni^2,3, Guido Beldi¹

¹Department of Visceral Surgery and Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.

Surgical Endoscopy

|August 16, 2023

Summary

Artificial intelligence (AI) shows promise for automated surgical skill assessment. While accurate in simulations, AI models need further validation for real-world clinical use in minimally invasive surgery.

Keywords:

Artificial intelligence Minimally invasive surgery Surgical data science Surgical skill assessment Technical skill assessment

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Area of Science:

Surgical Education and Training
Medical Artificial Intelligence
Minimally Invasive Surgery

Background:

Traditional surgical skill assessment relies on subjective expert opinion, which is time-consuming and lacks objectivity.
Artificial intelligence (AI) offers a potential solution for automated technical skill assessment using intraoperative data.
This systematic review evaluates AI models for assessing technical skills in minimally invasive surgery.

Approach:

A systematic literature search was conducted across major databases (Medline, Embase, Web of Science, IEEE Xplore).
Included studies were assessed for risk of bias and quality using established criteria.
Findings were reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement.

Key Points:

50 articles were analyzed, with AI models frequently using motion or kinematic data from surgical videos or robotic systems.
Deep learning models demonstrated good accuracy in predicting technical skills in simulated environments.
However, most AI models are still in development, with limited external validation and high risk of bias in many studies.

Conclusions:

AI demonstrates strong performance in assessing technical skills in minimally invasive surgery.
Current AI models often lack external validity and generalizability for widespread clinical application.
Future development requires benchmarking with standardized metrics and testing in clinical implementation studies.