AI-derived oocyte morphology and follicular fluid biomarkers in donors.

Oocyte quality is a key determinant of reproductive success, yet its assessment in assisted reproduction largely relies on subjective morphological criteria. Artificial intelligence (AI)-based image analysis has introduced greater objectivity into oocyte evaluation; however, the biological features captured by AI-derived morphological scores remain incompletely defined. In this study, we integrated AI-based oocyte morphology with molecular profiling of follicular fluid (FF) to identify biological correlates of oocyte competence. Reproductive outcomes were analysed in 49 young oocyte donors (20-33 years), while FF samples pooled per woman from a subset of 25 donors were analysed for metabolic (glucose, total cholesterol, triglycerides, HDL, LDL, APOA1), extracellular matrix-related (HSPG2/Perlecan), signaling-related (Gremlin-1), and fertility-related (AMH, LH, FSH) biomarkers. AI-derived oocyte quality scores were positively associated with specific intrafollicular markers, including glucose, total cholesterol, HDL, AMH, and HSPG2, while no associations were observed with triglycerides, LDL, Gremlin-1, LH, or FSH. APOA1 showed a positive trend with the AI score. These findings provide a biological context for AI-based oocyte morphological assessment by linking digital image-derived scores with metabolic and structural features of the follicular microenvironment. The integration of AI-driven morphology with donor follicular fluid biomarker profiling may contribute to the development of more objective and biologically informed approaches for oocyte quality evaluation in assisted reproduction.