Flap failure due to microvascular perfusion compromise represents an important cause of morbidity in reconstructive surgery. Traditional monitoring methods are subjective and lack predictive accuracy. Advanced contrast-based quantitative imaging methods, particularly indocyanine green fluorescence angiography (ICG-FA) and contrast-enhanced ultrasound (CEUS), provide objective hemodynamic information. This systematic review summarizes the evidence for their predictive validity for flap survival. A systematic search of PubMed/MEDLINE, Scopus, Web of Science, and the Cochrane Library was performed from inception up to February 1, 2026. Studies reporting quantitative perfusion parameters measured by ICG-FA or CEUS that were correlated with clinical outcomes (survival, necrosis) were included. Two reviewers independently screened titles/abstracts, conducted full-text reviews, extracted data, and evaluated risk of bias using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool, chosen for its specific design to assess diagnostic accuracy studies. Data were synthesized narratively and, where possible, using meta-analysis. Given the inclusion of both human and animal studies, a sensitivity analysis was planned, excluding animal studies to assess their impact on the primary pooled estimates. Of 2,583 identified records, 28 studies met inclusion criteria (19 ICG-FA, 9 CEUS), encompassing 1,583 flaps. The major quantitative parameters identified were time-to-peak (TTP), maximum fluorescence intensity (MFI), area under the curve (AUC), and inflow/outflow slope. Pooled analysis for ICG-FA demonstrated a significant association between prolonged TTP (mean difference (MD) = 4.2 seconds, 95% confidence interval (CI) 2.8-5.6, p 75%). Quantitative parameters derived from ICG-FA and CEUS demonstrate significant correlations with flap viability and offer greater objectivity than qualitative assessment. Standardization of imaging procedures and diagnostic thresholds is urgently needed to transform these promising techniques into validated, reliable clinical decision-support systems.
Morales et al. (Tue,) studied this question.