AI-derived oocyte morphology and follicular fluid biomarkers in donors

Why clinicians should know about it

• Picked for Embryology (paper of the day, 30 July 2026).

Abstract

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.

Abstract as published, via PubMed.