Unsupervised Machine Learning for the Identification of Latent First-Trimester Obstetric Phenotypes Associated with Maternal and Perinatal Morbidity

Background/Objectives: Artificial intelligence may improve obstetric risk stratification by identifying clinically meaningful patterns that are not captured by single-outcome prediction models. This study evaluated whether unsupervised machine learning applied to first-trimester maternal, biophysical, and biochemical data could identify phenotypes associated with adverse obstetric outcomes. Methods: We analyzed 1583 first-trimester records that were ambispectively collected including maternal characteristics, obstetric history, mean arterial pressure, uterine artery pulsatility index, nuchal translucency, and PAPP-A. Principal component analysis followed by k-means clustering was used to derive AI phenotypes. Associations were tested for hypertensive, metabolic, delivery, neonatal, and actionable obstetric outcomes. Robustness of the clustering solution and phenotype–outcome associations was assessed using inverse probability weighting, false discovery rate correction, adverse-event burden scoring, cross-algorithm agreement analyses, and multiple sensitivity analyses. Results: Four clinically interpretable phenotypes were identified. Phenotype 4 represented a cardiometabolic–vascular subgroup that showed the greatest enrichment for adverse obstetric outcomes. It had higher rates of maternal metabolic or hypertensive complications (52.4%; RR 4.31) and clinically actionable adverse pregnancy outcomes (57.1%; RR 2.31). These associations remained significant after false discovery rate correction and were consistent in inverse probability weighting analyses. Phenotype 4 also had the highest cumulative adverse-event burden score (1.29 versus 0.35–0.47). Exploratory analyses demonstrated high specificity (97.2%) but limited sensitivity for clinically actionable adverse pregnancy outcomes. Incremental discrimination over conventional predictors was observed only for selected secondary outcomes, including early preterm birth and low Apgar score. Conclusions: Unsupervised AI phenotyping identified a cardiometabolic–vascular pregnancy subgroup associated with increased obstetric morbidity.