Predicting Fetal Growth with Curve Fitting and Machine Learning

TL;DR

This study creates a fetal growth reference for Taiwan using machine learning and ultrasound data to detect developmental issues early.

Contribution

A Taiwan-specific fetal growth reference using polynomial regression and real-time anomaly detection is developed.

Findings

Quadratic regression achieved R2 values over 0.95 for most fetal biometric parameters.

A web-based platform was used to collect and analyze data from 8350 prenatal scans.

The model enables efficient and population-specific fetal growth monitoring in clinical settings.

Abstract

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 R2 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.

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