基于机器学习的试管婴儿结果预测:女性手术前因素的核心作用

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Kristóf Bereczki¹, Mátyás Bukva^2,3,4, Viktor Vedelek⁵

¹Department of Obstetrics and Gynecology, Albert Szent-Györgyi Medical School, University of Szeged, H-6725 Szeged, Hungary.

|November 27, 2025

概括

一个新的模型仅使用初始临床数据来预测体外受精 (IVF) 的成功. 这个工具有助于早期咨询和治疗计划,以获得更好的试管婴儿结果.

科学领域:

生殖医学生殖医学
生物统计学生物统计学
医疗保健中的人工智能

背景情况:

预测体外受精 (IVF) 成功对于患者咨询和治疗计划至关重要.
当前的预测模型往往需要大量的数据,或者在初始咨询中实施起来很复杂.

研究的目的:

开发和验证一种用于试管婴儿成功的预测模型,仅使用在首次患者访问时可用的程序前临床变量.
在独立的验证队列中评估模型的性能和通用性.

主要方法:

对1243个试管婴儿/ICSI周期进行了回顾性分析.
开发一个使用14个基线预测器的极端梯度增强 (XGBoost) 分类器.
推导一个节的9变量模型和验证在一个独立的队列.

主要成果:

在内部测试组中,9变量模型在81.70%的准确度下达到0.876的AUC.
外部验证显示了强的性能,准确率为78.30%.
女人的年龄,AMH和BMI是关键预测因素,男性因素增加了价值.

结论:

在使用例行收集的手术前数据,可以在第一次咨询时可靠地预测试管婴儿的结果.
该模型表现出一致的歧视,支持其用于早期,个性化的患者咨询和治疗规划的实用性.
这种方法可以优化患者管理,并可能提高试管婴儿成功率.

关键词:

临床怀孕临床怀孕在体外受精体外受精.活生生的出生是活生生的出生.机器学习是机器学习.程序前的因素程序前的因素

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