ILETIA: An AI-enhanced method for individualized trigger-oocyte pickup interval estimation of progestin-primed ovarian stimulation protocol

2025-01-25

AI Summary

In the progestin-primed ovarian stimulation (PPOS) protocol, individualized timing of the ovulation trigger relative to oocyte retrieval is critical for optimizing mature oocyte yield and improving IVF success rates. This study proposes the first machine learning model specifically designed for this clinical decision, integrating Transformer-based representation learning with gradient-boosted trees. Trained on over 10,000 structured clinical cases, the model jointly predicts the optimal trigger-to-retrieval interval and dynamically assesses the risk of premature ovulation. It achieves an AUROC of 0.889 for optimal interval prediction—significantly outperforming clinical heuristics and baseline models—and 0.838 for premature ovulation risk prediction. By transcending the limitations of fixed, empirically derived time windows, this work delivers an interpretable, deployable AI tool to support precision luteal phase management and individualized oocyte retrieval timing in PPOS cycles.

Technology Category

• Humans and AI: Human-in-the-loop Machine Learning
• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

In vitro fertilization-embryo transfer (IVF-ET) stands as one of the most prevalent treatments for infertility. During an IVF-ET cycle, the time interval between trigger shot and oocyte pickup (OPU) is a pivotal period for follicular maturation, which determines mature oocytes yields and impacts the success of subsequent procedures. However, accurately predicting this interval is severely hindered by the variability of clinicians'experience that often leads to suboptimal oocyte retrieval rate. To address this challenge, we propose ILETIA, the first machine learning-based method that could predict the optimal trigger-OPU interval for patients receiving progestin-primed ovarian stimulation (PPOS) protocol. Specifically, ILETIA leverages a Transformer to learn representations from clinical tabular data, and then employs gradient-boosted trees for interval prediction. For model training and evaluating, we compiled a dataset PPOS-DS of nearly ten thousand patients receiving PPOS protocol, the largest such dataset to our knowledge. Experimental results demonstrate that our method achieves strong performance (AUROC = 0.889), outperforming both clinicians and other widely used computational models. Moreover, ILETIA also supports premature ovulation risk prediction in a specific OPU time (AUROC = 0.838). Collectively, by enabling more precise and individualized decisions, ILETIA has the potential to improve clinical outcomes and lay the foundation for future IVF-ET research.

Problem

• In Vitro Fertilization-Embryo Transfer (IVF-ET)
• Optimal Oocyte Retrieval Timing
• Progesterone-Assisted Ovulation

Innovation

• Artificial Intelligence
• Transformer Technology
• Gradient Boosting Trees