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通过超声波菌株分析和机器学习预测体外受精结果:一项多中心研究

Anyi Cheng¹, Yizhou Huang², Connie Rees³

¹School of Information Science and Technology, ShanghaiTech University, Shanghai, China; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands; Shanghai Advanced Research Institute, Chinese Academy of Sciences, Shanghai, China.

The ultrasound journal

|February 25, 2026

概括

通过超声波和机器学习评估子宫周平静脉可以预测体外受精 (IVF) 的成功. 该方法确定了子宫运动的关键特征,以提高胚胎植入率并支持临床决策.

科学领域:

生殖医学是一种生殖医学.
医学成像分析分析医学成像分析
机器学习在医疗保健中的应用

背景情况:

在体外受精 (IVF) 的成功率受到高失败率的阻碍,通常是由于未知的原因.
受到子宫周平静的影响的子宫受容性是成功植入胚胎的关键因素.
精确评估子宫周平静止对于改善试管婴儿结果至关重要.

研究的目的:

开发和验证用于预测试管婴儿成功的机器学习框架.
为了确定与临床怀孕相关的子宫周平静的关键特征.
加强胚胎移植 (ET) 之前的临床决策.

主要方法:

一项前性研究涉及62名多个生育中心的试管婴儿患者.
用斑点追踪分析了四分钟的经阴道超声波 (TVUS) 扫描,以提取25个子宫周静止特征.
使用支持矢量机 (SVM),K-最近邻居 (KNN) 和自适应提升 (AdaBoost) 分类器,进行特征选择和嵌套的8倍交叉验证进行评估.

主要成果:

与子宫周立体协调和频率相关的特征与临床怀孕有很强的相关性.
SVM模型实现了最高的分类性能,ROC曲线下的平均面积为0.81.1.
开发的框架证明了可靠和可概括的分类性能.

结论:

使用TVUS数据的新型机器学习框架可以有效预测试管婴儿的结果.
该SVM模型准确地识别出可预测怀孕的显著子宫运动特征.
这种方法为临床医生提供了一种有价值的工具,可以提高试管婴儿成功率.

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In Vitro Fertilization

In Vitro Fertilization

In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...