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在男性因子不孕症中基于机器学习的IVF/ICSI结果的预测,突出了夫妇水平的BMI

Hu Li¹, Jie Gao¹, Yiran Li²

¹Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.

Frontiers in endocrinology

|February 26, 2026

概括

一个新的机器学习模型预测了IVF/ICSI在男性因子不孕症中的成功,将代谢因素与卵巢储备标志物结合在一起,以获得更好的结果.

科学领域:

生殖医学生殖医学
医疗保健中的人工智能
临床预测建模临床预测建模

背景情况:

现有的辅助生殖技术 (ART) 临床预测模型经常忽视男性因素和伴侣代谢状态.
传统的参数模型与生殖数据中常见的非线性模式作斗争.
这项研究解决了在进行体外受精/细胞内质精子注射 (IVF/ICSI) 的男性因子不孕症中需要综合模型的需求.

研究的目的:

开发和验证基于机器学习 (ML) 的模型,用于预测经过IVF/ICSI的男性因子不孕症夫妇的临床妊娠结果.
为了提高模型的可解读性,使用Shapley添加式扩展 (SHAP).
探索代谢状态和卵巢储备标记在预测试管婴儿/ICSI成功中的作用.

主要方法:

对2565对夫妇进行了回顾性分析,他们接受了第一个IVF/ICSI周期的男性因子不孕症.
使用LASSO回归的特征选择,通过5倍交叉验证开发和优化7个ML模型 (包括LightGBM,物流回归).
使用AUC,准确性,布里尔得分,决策曲线分析和SHAP进行解释性的性能评估.

主要成果:

确定了五个关键预测因素:女性BMI,男性BMI,基底FSH,AMH和女性年龄.
轻GBM模型在验证集中实现了0.857的AUC (95%CI:0.830-0.882) 和0.775的准确性.
所有测试的ML模型都显示了可比的区分性能 (AUC范围:0.840-0.857).

结论:

一个基于LightGBM的模型显示了预测男性因子不孕症中IVF/ICSI结果的前景.
情侣级代谢特征,以及卵巢储备标记,与模型预测密切相关.
代谢特征需要在风险分层和对不育夫妇的咨询中考虑.

关键词:

身体质量指数 (BMI) 是什么?临床怀孕临床怀孕灯光GBMMGBMM 的时间机器学习是机器学习.男人不孕不育的原因

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