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Surgical skill levels: Classification and analysis using deep neural network model and motion signals.

Xuan Anh Nguyen1, Damir Ljuhar2, Maurizio Pacilli2

  • 1Department of Mechanical and Aerospace Engineering, Monash University, Clayton, Victoria, 3800, Australia.

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This study introduces an automated system using deep learning to objectively assess surgical skills. The model accurately identifies trainee proficiency, offering a scalable and consistent alternative to subjective expert evaluations.

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Area of Science:

  • Medical technology
  • Artificial intelligence in surgery
  • Surgical education

Background:

  • Current surgical skill assessment relies on subjective, time-consuming expert observation.
  • A need exists for objective, scalable, and consistent methods to evaluate surgical trainees.
  • Automated systems can provide reliable feedback on surgical proficiency.

Purpose of the Study:

  • To design and evaluate an automated surgical skills evaluation system.
  • To objectively identify the skill levels of surgical trainees.
  • To develop a deep learning model for surgical skill assessment.

Main Methods:

  • A deep neural network model was developed to analyze raw surgical motion data.
  • Inertial measurement unit sensors were used to collect data from participants performing surgical tasks.
  • The model was validated using the JIGSAWS dataset and 15 participants (novices, intermediates, experts).

Main Results:

  • The deep learning model achieved 98.2% accuracy in assessing surgical skills.
  • Performance on the JIGSAWS dataset showed high accuracy: 98.4% for suturing, 98.4% for needle-passing, and 94.7% for knot-tying.
  • The method demonstrated applicability in both open and robot-assisted surgery.

Conclusions:

  • The proposed deep network model can effectively learn features distinguishing different surgical skill levels.
  • This automated system shows potential for objective and reliable surgical skill evaluation.
  • The findings support the use of AI in surgical training and assessment.