Biologically inspired digital histology for deep phenotyping of placental composition changes across major lesion types.

BACKGROUND: Placental histopathology provides important insights into maternal and fetal health, yet the organ's spatial heterogeneity poses significant challenges for objective and reproducible histological analysis. Systematic assessment of cellular and structural composition across placental slides remains limited by the scale and subjectivity of manual evaluation. Quantitative approaches are therefore needed to characterise placental responses to injury beyond visually apparent lesions. METHODS: We applied the Histology Analysis Pipeline.PY (HAPPY), a biologically inspired hierarchical deep learning framework for quantitative single-cell-resolution analysis of Haematoxylin and Eosin (H&E) slides, to 130 placental parenchyma slides from 62 singleton full-term live births. The dataset included healthy normal controls and four common placental lesion types: infarction, perivillous fibrin, avascular villi, and intervillous thrombosis. Cell-type and tissue-structure compositions were quantified, and slide-level deviation from a healthy reference was assessed using compositional data analysis. RESULTS: Placental slides with lesions exhibited significant cellular composition differences compared with healthy controls, including increased extravillous trophoblast and leukocyte densities and decreased Hofbauer cell densities. These cellular changes were accompanied by tissue-level alterations, particularly increased fibrin deposition and changes in villous structure. Compositional deviation increased with infarction size but not with other lesion types. Notably, compositional differences were also detected in slides without an apparent lesion from placentas with lesion(s) elsewhere, indicating organ-wide responses extending beyond focal pathology. CONCLUSIONS: Quantitative deep phenotyping reveals widespread cellular and structural changes associated with placental lesions, including effects not evident on routine histological assessment. These findings demonstrate the potential of AI-based digital histology to complement conventional placental pathology in research and clinical settings.