Machine Learning-Based Flap Takeback Prediction Modeling: Theory for a Real-Time, Patient-Specific Postoperative Flap Monitoring and Alert System.

Journal: Microsurgery
Published Date: Sep 1, 2025

Abstract

BACKGROUND: Postoperative free flap monitoring is crucial yet taxing, requiring frequent and often subjective assessments to detect early signs of compromise. The present study aims to develop a machine learning model to predict the risk of flap take-back reoperation due to arterial and/or venous compromise, as a basis for real-time risk monitoring and alerts.

Authors

  • Olachi O Oleru
    Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Kim-Anh-Nhi Nguyen
    Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, NY.
  • Peter Taub
    Division of Plastic and Reconstructive Surgery, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Arash Kia
    Department of Mathematics & Statistics, University of Limerick, Limerick, Ireland.

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