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

[INTRODUCTION] To systematically evaluate the diagnostic performance of artificial intelligence (AI) models in predicting postoperative complications following flap surgery, and to compare the efficacy of different input modalities used in model training.

[METHODS] A comprehensive literature search was conducted across PubMed, Embase, Scopus, and Web of Science to identify studies utilizing AI for flap monitoring and postoperative complication prediction. A total of 12 studies comprising 18,520 patients and 32,148 input data points were included. Pooled sensitivity, specificity, likelihood ratios, and SROC curves were calculated using a bivariate random-effects model.

[RESULTS] The meta-analysis revealed a pooled sensitivity of 78.0% [95% CI: 0.54-0.91] and a pooled specificity of 88.0% [95% CI: 0.76-0.94]. The positive and negative likelihood ratios were 6.36 [95% CI: 2.54-15.91] and 0.25 [95% CI: 0.10-0.64], respectively. The area under the SROC curve was 0.91 [95% CI: 0.88-0.93], indicating excellent overall diagnostic performance.

[CONCLUSION] AI models, particularly those incorporating photographic data and deep learning models, demonstrate high diagnostic accuracy and hold promise as adjunct tools for postoperative flap monitoring.