ILETIA: An AI-enhanced method for individualized trigger-oocyte pickup interval estimation of progestin-primed ovarian stimulation protocol
Journal:
arXiv
Published Date:
Jan 25, 2025
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.
