Predicting Fetal Growth with Curve Fitting and Machine Learning

Predicting Fetal Growth with Curve Fitting and Machine Learning

Monitoring fetal growth throughout pregnancy is essential for early detection of developmental abnormalities. This study developed a Taiwan-specific fetal growth reference using a web-based data collection platform and polynomial regression modeling. We analyzed ultrasound data from 980 pregnant wom...

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Bibliographic Details
Main Authors: Huan Zhang, Chuan-Sheng Hung, Chun-Hung Richard Lin, Hong-Ren Yu, You-Cheng Zheng, Cheng-Han Yu, Chih-Min Tsai, Ting-Hsin Huang
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Bioengineering
Subjects:
curve fitting
machine learning
polynomial regression
fetal growth
prenatal ultrasound
Online Access:https://www.mdpi.com/2306-5354/12/7/730
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Summary:Monitoring fetal growth throughout pregnancy is essential for early detection of developmental abnormalities. This study developed a Taiwan-specific fetal growth reference using a web-based data collection platform and polynomial regression modeling. We analyzed ultrasound data from 980 pregnant women, encompassing 8350 prenatal scans, to model six key fetal biometric parameters: abdominal circumference, crown–rump length, estimated fetal weight, head circumference, biparietal diameter, and femur length. Quadratic regression was selected based on a balance of performance and simplicity, with R<sup>2</sup> values exceeding 0.95 for most parameters. Confidence intervals and real-time anomaly detection were implemented through the platform. The results demonstrate the potential for efficient, population-specific fetal growth monitoring in clinical settings.
ISSN:2306-5354

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