标题： ILETIA：一种用于孕激素预处理卵巢刺激方案的个体化触发-取卵间隔估计的AI增强方法 显示英文标题

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

摘要： 体外受精-胚胎移植（IVF-ET）是治疗不孕症最普遍的治疗方法之一。 在IVF-ET周期中，触发注射与卵子采集（OPU）之间的时间间隔是卵泡成熟的关键时期，这决定了成熟卵子的产量并影响后续程序的成功率。 然而，由于临床医生经验的差异，准确预测这一间隔受到严重阻碍，这通常导致卵子获取率不理想。 为了解决这一挑战，我们提出了ILETIA，这是第一个基于机器学习的方法，可以预测接受孕酮预处理卵巢刺激（PPOS）方案患者的最优触发-OPU间隔。 具体而言，ILETIA利用Transformer从临床表格数据中学习表示，然后使用梯度提升树进行间隔预测。 在模型训练和评估方面，我们整理了一个包含近万名接受PPOS方案患者的PPOS-DS数据集，据我们所知这是最大的此类数据集。 实验结果表明，我们的方法表现出色（AUROC = 0.889），优于临床医生和其他广泛使用的计算模型。 此外，ILETIA还能在特定的OPU时间预测提前排卵的风险（AUROC = 0.838）。 总体而言，通过实现更精确和个性化的决策，ILETIA有潜力改善临床结果，并为未来的IVF-ET研究奠定基础。 显示英文摘要

摘要： 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.