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Updated: May 5, 2026

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

在受控卵巢刺激后检测到的MII卵细胞的可解释机器学习模型:对24,976次试管婴儿/ICSI周期的回顾性队列研究

Lu Zhang¹, Li-Juan Fu^1,2, Yu-Ling Hu³

¹Department of Obstetrics and Gynecology, Women and Children's Hospital of Chongqing Medical University, Department of Epidemiology, School of Public Health, Chongqing Medical University, Chongqing, 401147, China.

Journal of assisted reproduction and genetics

|August 23, 2025

概括

机器学习可以准确地预测在生育治疗中成熟的卵细胞. 一个新的网络计算器为体外受精 (IVF) 和细胞内精子注射 (ICSI) 提供了个性化的预测.

科学领域:

生殖内分泌学和不孕症
医学中的人工智能
医疗保健中的数据科学

背景情况:

预测成熟卵细胞产量对于优化体外受精 (IVF) 和细胞内精子注射 (ICSI) 的结果至关重要.
准确的预测可以个性化治疗方案,
现有的预测方法往往缺乏准确性和通用性.

研究的目的:

评估机器学习模型在控制卵巢刺激后检测到的成熟卵细胞数量的有效性.
在接受辅助生殖技术的患者中确定影响卵细胞产量的关键预测因素.
开发一种临床上适用于个性化卵细胞产量预测的工具.

主要方法:

对24,976名不孕症患者周期 (2018-2022) 的回顾性分析.
使用相关性分析,LASSO回归和随机森林的特征选择.
使用RMSE,MAE和R-squared的八个机器学习模型的开发和评估.
使用独立数据集进行临床验证,并使用SHAP值进行解释.

主要成果:

确定了六个关键预测因素:毛囊数量,基线荷尔蒙水平 (FSH,雌激素),触发日雌激素,大毛囊数量和患者年龄.
在临床验证中,多层感知子模型表现出最高的准确性 (R^2 = 0. 714).
雌激素水平和触发日的大毛囊数量是最强的预测因素.

结论:

一个机器学习模型成功地开发出准确预测成熟卵细胞产量.
该模型为个性化生育治疗规划提供了宝贵的工具.
一个用户友好的基于网络的计算器提高了临床可用性,并支持了明智的决策.

关键词:

机器学习卵细胞检索卵巢刺激辅助生殖技术

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