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Machine learning models for estimating fetal weight based on ultrasonographic biometry: Development and validation study
1
Zitationen
4
Autoren
2025
Jahr
Abstract
The TabPFN model outperformed traditional formulas, including Hadlock and Shepard, and other evaluated machine learning methods in estimating fetal weight. Its high predictive accuracy, robustness across temporally distinct cohorts, and independence from hyperparameter tuning support its potential as a reliable clinical decision-support tool in obstetric care.
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