Abstract Preeclampsia remains a major cause of maternal and fetal morbidity worldwide, originating from abnormal placental development and reduced perfusion. Conventional Doppler indices, although widely used, are limited in sensitivity, reproducibility, and early detection. Advances in three-dimensional power Doppler ultrasound (3D PD-US) and its vascular indices – vascularization index (VI), flow index (FI), and vascular FI (VFI) – offer semiquantitative evaluation of intraplacental microvascular function. Following Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 guidelines, this structured narrative synthesis reviewed literature from PubMed, Scopus, and EMBASE (2000–July 2025). A total of 1174 records were identified; after removing 328 duplicates, 846 titles and abstracts were screened, 137 full-text articles were assessed for eligibility, and 74 studies were included in the final synthesis assessing Doppler-based perfusion, biomarkers, or artificial intelligence (AI) integration in preeclampsia. Findings consistently demonstrate reduced VI, FI, and VFI in affected pregnancies, with FI showing the strongest reproducibility across cohorts. Emerging AI models trained on voxel-level Doppler data and angiogenic biomarkers such as placental growth factor and soluble fms-like tyrosine kinase-1 achieved high predictive accuracies (area under the curve 0.85–0.91) for early disease risk, although external validation remains limited. Integration of 3D PD-US indices with AI and biomarkers may enable early risk stratification, personalized monitoring, and informed timing of intervention. Heterogeneity in imaging protocols and the absence of meta-analytic pooling remain key limitations. Placental perfusion, re-envisioned through advanced imaging and AI analytics, emerges as a cornerstone biomarker for anticipatory, precision-based obstetric care.
Andonotopo et al. (Thu,) studied this question.