Machine learning models for estimating fetal weight based on ultrasonographic biometry: Development and validation study

Abstract

Background: Advances in machine learning (ML) offer an innovative approach to accurate fetal weight estimation by integrating multiple biometric and clinical variables.

Objective: To develop and validate ML models for estimating fetal weight using biometric data obtained via ultrasonography, evaluating their accuracy and comparing them with traditional formulas, such as Hadlock and Shepard.

Methods: A retrospective observational study was conducted at the National Maternal Perinatal Institute of Peru from 2009 to 2022, including 3525 low-risk pregnancies with singleton gestations. ML models, including Gradient Boosting, Support Vector Machine (SVM), Random Forest and TabPFN (Tabular Prior-data Fitted Network), were trained and validated using ultrasonographic measurements such as biparietal diameter, abdominal circumference, head circumference, femur length, and gestational age. Accuracy was assessed using the coefficient of determination (R²) and mean squared error (MSE), comparing the ML models to the Hadlock and Shepard formulas.

Results: Data from the first study stage (2009-2018) indicated that the TabPFN model was the most accurate (R² = 0.856; MSE = 0.146), outperforming the Hadlock (R² = 0.807; MSE = 0.195) and Shepard (R² = 0.801; MSE = 0.201) formulas. In the independent validation sample (2019-2022), TabPFN consistently outperformed other methods (R² = 0.873; MSE = 0.144). Model consistency was evaluated through cross-validation and randomization of samples.

Conclusions: 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.

Keywords: Fetal weight; artificial intelligence; machine learning; perinatal care; pregnancy; prenatal; ultrasonography.

Figure 1.

Description of techniques applied in the ML modeling process.

Figure 2.

Contribution of features and their interactions to the mean estimated fetal weight using the TabPFN model, based on faithful Shapley interaction Index values in the test and validation datasets.

Figure 3.

Trend graph of MSE and R² obtained from replications of the fetal weight predictive model.