Diagnostic Accuracy of AI in Prediction and Assessment of Compromised Free Flaps: Systematic Review and Meta-Analysis.

BACKGROUND

Vascular compromise remains the leading cause of free-flap failure. AI-based monitoring and prediction tools have emerged as a promising adjunct for postoperative free flap monitoring and early detection of vascular compromise. Previous systematic reviews included limited evidence or broadly evaluated reconstructive outcomes.

OBJECTIVE

This systematic review and meta-analysis assessed the diagnostic accuracy of AI for postoperative free flap monitoring and flap compromise detection.

METHODS

Following PRISMA-DTA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies), PubMed, Embase, Cochrane, Web of Science, and Scopus were searched from inception to June 21, 2026. Studies developing or validating AI models for free flap monitoring or compromise prediction were included. Pooled sensitivity, specificity, area under the curve (AUC), and diagnostic odds ratio (DOR) were estimated using a hierarchical bivariate random-effects model. Risk-of-bias and applicability were assessed with Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), and the certainty of evidence was evaluated using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach. Prespecified subgroup analyses by modality, model-fit diagnostics, sensitivity analyses, and publication-bias testing with Deeks funnel plot were undertaken. The protocol was prospectively registered in PROSPERO (CRD420251175572).

RESULTS

Of 2098 records identified, 18 studies met the inclusion criteria. Of these, 17 studies were included in the quantitative synthesis. Pooled analysis demonstrated an overall sensitivity of 0.83 (95% CI 0.70-0.91; prediction interval [PI] 0.21-0.99), specificity of 0.87 (95% CI 0.65-0.96; PI 0.03-1.00), AUC of 0.92 (95% CI 0.90-0.94), and DOR 36.17 (95% CI 8.27-158.26; PI 0.09-14886.43). Image-based AI models demonstrated superior performance, with a sensitivity of 0.92 (95% CI 0.81-0.97; PI 0.43-0.99), specificity of 0.95 (95% CI 0.86-0.98; PI 0.43-1.00), and AUC of 0.98 (95% CI 0.96-0.99). Nonimage-based models had sensitivity (0.69, 95% CI 0.45-0.86; PI 0.11-0.98) specificity (0.72, 95% CI 0.18-0.97; PI 0.00-1.00), and AUC (0.79, 95% CI 0.75-0.82). For vascular compromise detection, pooled sensitivity was 0.92 (95% CI 0.81-0.97; PI 0.44-0.99) and specificity was 0.91 (95% CI 0.79-0.97; PI 0.35-1.00). Between-study heterogeneity was substantial, QUADAS-2 identified methodological concerns in several studies, and GRADE rated the certainty of evidence as low.

CONCLUSIONS

AI demonstrated favorable diagnostic performance for detecting free flap vascular compromise, particularly with image-based models. These findings support the use of AI as an adjunct to conventional postoperative flap monitoring. Nevertheless, the low certainty of evidence, substantial heterogeneity, and limited external validation indicate that prospective multicenter validation and standardized reporting are required before routine clinical implementation.