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<p>We developed a deep learning model trained on over two million ultrasound images from 78,531 pregnancies from Australia, India, and the UK to estimate gestational age (GA) directly from any fetal ultrasound image, regardless of orientation. The model outputs both a GA estimate and an uncertainty value based on image quality. Independent validation on 36,762 ultrasound images from 742 fetuses showed a mean absolute error (MAE) of 1.7 days at 14–18 weeks and 2.8 days at 18–24 weeks, significantly outperforming traditional biometry (p &lt; 0.001). In video analysis, the model achieved a median prediction time of 24 s and an MAE below 3 days across all trimesters. Performance was consistent across maternal body mass index (BMI) categories and geographic settings. This AI-based GA estimation method matches or exceeds gold-standard fetal biometry, reduces reliance on highly skilled sonologists, and offers the potential to improve access to prenatal care in resource-limited and underserved settings globally.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月20日

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  • Martin Benson,
  • Sacha Walton,
  • Tom Hartley,
  • Simon Meagher,
  • Suresh Seshadri,
  • Nicholas Sleep,
  • Aris T. Papageorghiou

摘要

We developed a deep learning model trained on over two million ultrasound images from 78,531 pregnancies from Australia, India, and the UK to estimate gestational age (GA) directly from any fetal ultrasound image, regardless of orientation. The model outputs both a GA estimate and an uncertainty value based on image quality. Independent validation on 36,762 ultrasound images from 742 fetuses showed a mean absolute error (MAE) of 1.7 days at 14–18 weeks and 2.8 days at 18–24 weeks, significantly outperforming traditional biometry (p < 0.001). In video analysis, the model achieved a median prediction time of 24 s and an MAE below 3 days across all trimesters. Performance was consistent across maternal body mass index (BMI) categories and geographic settings. This AI-based GA estimation method matches or exceeds gold-standard fetal biometry, reduces reliance on highly skilled sonologists, and offers the potential to improve access to prenatal care in resource-limited and underserved settings globally.