O-219 Explainable artificial intelligence (XAI) to determine the follicle sizes on trigger day that maximize mature oocyte yield

Abstract

Abstract Study question Which follicle sizes on trigger day (TD) are key to obtaining mature oocytes, embryos, and blastocysts, thereby enhancing live birth rates? Summary answer Follicles sized 13-18mm are most likely to yield mature oocytes. Live birth rate (LBR) was increased with a greater proportion of follicles in this range. What is known already At the time of oocyte retrieval, follicles that are either too small or too large are less likely to yield mature oocytes. However, there is only limited data examining which follicle sizes on TD administration are optimally sized to yield mature oocytes, nor any data examining whether live birth rates are impacted. Explainable artificial intelligence (XAI) offers a valuable opportunity to leverage clinical data to determine the range of follicle sizes that are most likely to yield mature oocytes and lead to improved downstream outcomes. Study design, size, duration A retrospective study incorporating 19,082 patients undergoing their first IVF/ICSI cycle (2005-2023) across 11 European clinics. Follicle sizes on TD were related to oocyte maturity (14,140 patients), fertilization (17,822 patients), high-quality blastocyst development (17,488 patients), and pregnancy outcomes (12,672 patients). Secondary analyses included stratification by age (>35 years: 4,717 patients; ≤35 years: 5,707), and suppressant protocol (GnRH agonist: n = 5,420 patients; GnRH antagonist: n = 3,982). Participants/materials, setting, methods An ensemble-based XAI model, developed through ‘internal-external validation’, was trained, Bayesian optimized, and independently iteratively tested on hold-out clinic datasets. The model determined follicle sizes contributing most to successful laboratory outcomes by harnessing permutation importance and ‘SHAP’ values. Model performance was evaluated and optimized for mean absolute error (MAE). The top 50% contiguous cohort of influential follicle sizes on TD were identified, and further validated through analysis of follicle sizes on preceding days. Main results and the role of chance XAI analysis revealed that follicles sized 13-18mm on TD were most influential in yielding mature oocytes, especially those sized 15-18mm (MAE: 3.60 ±0.35). Predictive follicle sizes from scans 1-2 days preceding TD administration were consistent with expected mean follicle growth rates. For high-quality blastocysts, follicles sized 14-20mm were contributory, narrowing to 15-18mm in the subset of patients where oocytes had been confirmed to be mature. In hCG-triggered cycles, follicles sized 12-19mm were most likely to yield mature oocytes in GnRH antagonist cycles, whereas follicles sized 14-20mm were most contributory in GnRH agonist cycles. In patients ≤35 years, follicles 13-18mm were most contributory, whereas a wider range of follicles (11-20mm) were contributory in patients >35 years. In line with current practice, having three lead follicles ≥17mm on TD was associated with a median improvement in mature oocyte yield of 10% versus those that did not (p < 0.0001). By comparison, maximizing the proportion of follicles on TD within the optimal size range could better improve mature oocyte yield, for example, by 42% when at least 70% of follicles were sized 15-18mm (p < 0.0001). Likewise, LBR improved as the proportion of follicles sized 13-18mm was increased (<10%: 23.3%; 10-20%: 25.7%; 20-30%: 28.8%; 30-40%: 31.6%). Limitations, reasons for caution These data could help guide when to administer the trigger to optimize clinical outcomes, however prospective assessment of different TD strategies based on the proportion of follicles within the optimal follicle size ranges identified in comparison to current practice based on lead follicle size is required prior to clinical adoption. Wider implications of the findings These findings provide a data-driven approach to personalizing ovarian stimulation in IVF. The findings generated by applying an XAI model could assist clinicians in optimizing TD timing, potentially enhancing clinical outcomes by moving beyond the conventional focus on lead follicle size. Trial registration number not appicable