Machine learning for accurate estimation of fetal gestational age based on ultrasound images

Abstract

Accurate estimation of gestational age is an essential component of good obstetric care and informs clinical decision-making throughout pregnancy. As the date of the last menstrual period is often unknown or uncertain, ultrasound measurement of fetal size is currently the best method for estimating gestational age. The calculation assumes an average fetal size at each gestational age. The method is accurate in the first trimester, but less so in the second and third trimesters as growth deviates from the average and variation in fetal size increases. Consequently, fetal ultrasound late in pregnancy has a wide margin of error of at least ±2 weeks’ gestation. Here, we utilise state-of-the-art machine learning methods to estimate gestational age using only image analysis of standard ultrasound planes, without any measurement information. The machine learning model is based on ultrasound images from two independent datasets: one for training and internal validation, and another for external validation. During validation, the model was blinded to the ground truth of gestational age (based on a reliable last menstrual period date and confirmatory first-trimester fetal crown rump length). We show that this approach compensates for increases in size variation and is even accurate in cases of intrauterine growth restriction. Our best machine-learning based model estimates gestational age with a mean absolute error of 3.0 (95% CI, 2.9–3.2) and 4.3 (95% CI, 4.1–4.5) days in the second and third trimesters, respectively, which outperforms current ultrasound-based clinical biometry at these gestational ages. Our method for dating the pregnancy in the second and third trimesters is, therefore, more accurate than published methods.

Design of a targeted blood transcriptional panel for monitoring immunological changes accompanying pregnancy

“…Recently the potential of machine learning in predicting gestational age has been demonstrated, with high degrees of correlation between actual and predicted weeks of gestation being reported. These findings underscore the importance of dynamic changes in maternal physiology over the course of pregnancy and the potential utility of omic profiles in understanding these changes and harnessing them for diagnostic and predictive purposes (8)(9)(10).…”

Application and Progress of Artificial Intelligence in Fetal Ultrasound

“…Currently, ultrasound measurements of fetal anatomical landmarks have been well established for GA estimation, especially in early gestational states. However, with time, the error in ultrasound-estimated GA becomes more pronounced in late pregnancy due to the neglect of variability in fetal growth and development, and in some studies, the error is greater than 2 weeks [54,55]. Therefore, the development of an accurate and reliable model for mid-and late-stage GA assessment is worth exploring.…”

“…This method [57] showed a lower error in late pregnancy than simply measuring fetal biometric parameters. Unlike single image analysis [56,57], Lee et al [55] used CNN to analyze images from multiple standard ultrasound views for GA estimation without utilizing biometric information. The best model has a MAE of 3.0 days and 4.3 days in the middle and late stages of pregnancy, respectively.…”

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

“…In addition to this, the studies of Namburete et al and Alzubaidi et al similarly used the anatomy and growth of the fetal head for GA estimation [147,148]. Dan et al developed a DeepGA model that used the three main factors of fetal head, abdomen, and femur [149], while Lee et al proposed a machine learning method to accurately estimate GA with standard US planes [150]. The recent topic of point-of-care-US was addressed in the research of Maraci et al, who successfully showed automated head plane detection and GA estimation with point-of-care devices [151].…”