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机器学习与胎儿体重估计的传统公式相比:一项国际多中心研究,评估出生体重百分点的预测准确性

Omer Dor¹, Eran Ashwal², May Cohen¹

¹Gray Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics

|November 11, 2025

概括

与哈德洛克公式相比,机器学习 (ML) 模型,特别是LightGBM和XGBoost,显示出与哈德洛克公式相比,整体出生体重预测准确度有所提高. 然而,ML在识别百分位基胎儿风险方面提供了有限的收益.

科学领域:

围产儿医学围产儿医学
医疗信息学医疗信息学
计算生物学计算生物学

背景情况:

哈德洛克公式是目前用于估计胎儿体重的临床标准.
尽管有现有模型,但需要提高出生体重预测的准确性.

研究的目的:

评估机器学习 (ML) 模型是否可以提高出生体重预测的准确性.
将各种ML模型的性能与已确定的哈德洛克公式进行比较.

主要方法:

一个多中心的回顾性研究包括9674个单子怀孕.
使用超声波和母体数据训练了ML模型 (线性回归,决策树,随机森林,LightGBM,XGBoost,神经网络).
性能指标包括平均绝对百分比误差 (MAPE),RMSE,MAE,准确性,精度,回忆和F1分数.

主要成果:

与哈德洛克公式 (MAPE ~0.065) 相比,LightGBM和XGBoost的整体重量估计准确度更高.
ML模型显示出出生体重百分点 (<3,<10,>90,>97) 的边际或可比改善.
轻GBM在极端百分位数中获得了更高的准确性和F1得分,而Hadlock在特定情况下的回忆略好一些.

结论:

机器学习模型,特别是LightGBM和XGBoost,显著改善了整体胎儿体重预测.
使用ML识别百分位基胎儿风险的收益有限.
哈德洛克公式仍然是一个有价值的工具来分类风险胎儿.

关键词:

哈德洛克公式哈德洛克公式出生体重预测和预测胎儿体重估计的估计.对于妊娠年龄来说很大.机器学习是机器学习.百分位数预测的预测对于妊娠年龄来说小.

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