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Artificial intelligence accurately predicts free flap complications after reconstructive microsurgery. This machine learning model enhances postoperative monitoring, especially where specialist staff are limited.

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Area of Science:

  • Microsurgery
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Postoperative free flap monitoring is crucial in reconstructive microsurgery.
  • Current methods rely on specialist staff, limiting access in underserved regions.
  • This study explores AI for objective, accessible flap monitoring.

Purpose of the Study:

  • To apply artificial intelligence for postoperative free flap monitoring.
  • To validate machine learning's ability to predict and differentiate flap circulation issues.
  • To assess the feasibility of AI in improving reconstructive microsurgery accessibility.

Main Methods:

  • Prospectively collected data from 176 patients undergoing free flap surgery.
  • Utilized free flap photographs and clinical evaluation measures.
  • Employed SMOTE-Tomek for data balancing and random forest for prediction, with Shapley Additive Explanations for interpretation.

Main Results:

  • A random forest model achieved 98.4% accuracy in predicting flap circulation.
  • Key predictors for vascular compromise included temperature and color differences.
  • The model successfully differentiated between normal, arterial insufficiency, and venous insufficiency.

Conclusions:

  • Machine learning reliably differentiates postoperative free flap circulation types.
  • This AI approach can alleviate the burden of flap monitoring.
  • It has the potential to expand reconstructive microsurgery availability globally.