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Related Experiment Video

Updated: May 8, 2026

A Novel Use of Three-dimensional High-frequency Ultrasonography for Early Pregnancy Characterization in the Mouse
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Machine learning models for estimating fetal weight based on ultrasonographic biometry: Development and validation

Marcos Espinola-Sánchez1,2, Antonio Limay-Rios3, Andrés Campaña-Acuña3

  • 1Facultad de Ciencias de la Salud, Universidad Privada del Norte, Lima, Peru.

Digital Health
|May 19, 2025
PubMed
Summary

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Ultrasonography01:17

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called a...

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Machine learning models accurately estimate fetal weight. The Tabular Prior-data Fitted Network (TabPFN) model demonstrated superior predictive accuracy compared to traditional formulas, offering a promising tool for obstetric care.

Area of Science:

  • Medical Imaging and Diagnostics
  • Artificial Intelligence in Healthcare
  • Perinatal Medicine

Background:

  • Machine learning (ML) presents an innovative method for fetal weight estimation.
  • Integrating multiple biometric and clinical variables enhances accuracy.

Purpose of the Study:

  • Develop and validate ML models for fetal weight estimation using ultrasonographic biometric data.
  • Evaluate the accuracy of ML models and compare them against traditional formulas like Hadlock and Shepard.

Main Methods:

  • A retrospective observational study included 3525 low-risk singleton pregnancies (2009-2022).
  • ML models (Gradient Boosting, SVM, Random Forest, TabPFN) were trained and validated using fetal biometry (biparietal diameter, abdominal circumference, head circumference, femur length) and gestational age.
Keywords:
Fetal weightartificial intelligencemachine learningperinatal carepregnancyprenatalultrasonography

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  • Accuracy was assessed using R² and Mean Squared Error (MSE).

    • Main Results:

    • The TabPFN model achieved the highest accuracy in the initial study stage (R²=0.856, MSE=0.146), surpassing Hadlock (R²=0.807, MSE=0.195) and Shepard (R²=0.801, MSE=0.201) formulas.
    • TabPFN maintained superior performance in the independent validation sample (2019-2022) (R²=0.873, MSE=0.144).
    • Model consistency was confirmed through cross-validation and sample randomization.

    Conclusions:

    • The TabPFN model significantly outperformed traditional formulas and other ML methods for fetal weight estimation.
    • Its high predictive accuracy and robustness make it a potential clinical decision-support tool in obstetrics.
    • TabPFN's independence from hyperparameter tuning enhances its clinical utility.