Work

Fetal gestational age estimation using artificial intelligence on non-targeted ultrasound images and video

Year: 2025

Type: article

Abstract: Background Accurate gestational age (GA) estimation is essential for prenatal care, guiding fetal growth assessment and medical interventions. Ultrasound-based biometric measurements, though more reliable than last menstrual period, require high sonographic expertise and are time-consuming, posing challenges, especially in resource-limited settings. This study aimed to develop an Artificial Intelligence (AI) model that estimates GA from any fetal ultrasound images regardless of orientation or standard plane, reducing reliance on sonographic skill, to increase accessibility. Methods: We trained a deep learning model on a large, diverse dataset of over 2 million ultrasound images from three continents (Australia, India, and the UK). The model was trained to estimate GA from ultrasound images without requiring specific biometric planes or measurements. It outputs a GA estimate alongside an uncertainty level, based on image quality and type. Validation was performed using independent datasets of ultrasound images and videos, with comparison against standard biometric measurements across all trimesters. Findings: The AI model consistently produced GA estimates that were at least as accurate as those derived from traditional biometry, with a mean absolute error (MAE) significantly lower than biometry in the second trimester (p < 0.001) and comparable in the third trimester. Subanalysis by country and maternal BMI demonstrated the model's robustness across different sub-populations. The model also accurately estimated GA on video datasets, producing a confident estimate after a median of 24 seconds of video. Interpretation: This AI-based GA estimation method, trained from retrospective clinical data, is at least accurate and gold-standard fetal biometry. By significantly reducing the skill level required by sonologists, this approach holds potential to improve prenatal care in resource-limited settings and democratize access to ultrasound-based GA estimation globally.

Source: npj Digital Medicine

DOI: 10.1038/s41746-025-02024-z