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November 13, 2025MicrosurgeryOpen Access

Diagnostic Accuracy of Artificial Intelligence Models for Predicting Postoperative Complications Following Free Flap Reconstruction: A Systematic Review and Meta‐Analysis

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Authors

RSRamin ShekouhiHDHassan DarabiHCHarvey Chim

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Overview

Systematic review demonstrates high accuracy of AI models for predicting postoperative complications in flap surgery, highlighting their potential as monitoring tools.

Key Points

  • To evaluate the diagnostic performance of AI models in predicting postoperative complications after flap surgery.
  • Systematic literature search across multiple databases
  • Inclusion of 12 studies involving 18,520 patients
  • Calculation of pooled sensitivity and specificity using bivariate random-effects model
  • Pooled sensitivity was 78.0% and specificity was 88.0%
  • Positive and negative likelihood ratios were 6.36 and 0.25, respectively
  • Area under the SROC curve was 0.91, indicating excellent diagnostic performance

Cite This Study

Shekouhi et al. (2025) studied this question.

synapsesocial.com/papers/692523bbc0ce034ddc354adfhttps://doi.org/10.1002/micr.70143
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Role of Artificial Intelligence in Predicting Flap Outcomes in Plastic Surgery: Protocol of a Systematic Review2022 · 8 citations
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  3. 3Seeing Beyond the Surgeon’s Eye2026
  4. 4Machine learning approaches overcome imbalanced clinical data for intraoral free flap monitoring2025
  5. 5Performance of artificial intelligence models for predicting intraoperative complications during surgery in real time: a systematic review and meta-analysis protocol2025 · 3 citations