Unsupervised Machine Learning for Placental Disease Using Cell Spatial Organization

Artificial intelligence (AI) screening of placental slides and patient data uncovers decisive biomarkers, most notably decidual vasculopathy, that predict recurrent preeclampsia and adverse maternal–child outcomes1–4. Clinical adoption of such screening methods remains limited because current models often misclassify cases and lack biologically grounded interpretability5 for their predictions. In our study, interpretability refers to the ability to detect differences in the spatial organization of extravillous trophoblast cells (EVT) relative to red blood cells (RBC) within placental vessels rather than relying on data‑driven heuristics that yield black‑box diagnoses. This study introduces the optical density morphology mapping technique (ODMMT), an AI framework that delivers biologically interpretable unsupervised classification, automates the correction of misclassifications, and makes AI pipelines more deployable for real‑time diagnosis. The framework comprises three steps: extracting EVT–RBC organizations via optical density masking, converting them into z‑scores with a normalizing flow model, and calculating a morphology separation score for clustering and biological interpretation. ODMMT surpasses other AI frameworks, achieving two distinct clusters in distinguishing healthy from diseased vessels on the experimental dataset. This advancement automates AI misclassification correction and quantifies RBC–EVT spatial organization, providing biomarker insights from unlabeled images to improve diagnosis and better understand the maternal-fetal relationship.