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Fetal ultrasounds are an essential part of prenatal care and can be used toestimate gestational age (GA). Accurate GA assessment is important forproviding appropriate prenatal care throughout pregnancy and identifyingcomplications such as fetal growth disorders. Since derivation of GA frommanual fetal biometry measurements (head, abdomen, femur) areoperator-dependent and time-consuming, there have been a number of researchefforts focused on using artificial intelligence (AI) models to estimate GAusing standard biometry images, but there is still room to improve the accuracyand reliability of these AI systems for widescale adoption. To improve GAestimates, without significant change to provider workflows, we leverage AI tointerpret standard plane ultrasound images as well as 'fly-to' ultrasoundvideos, which are 5-10s videos automatically recorded as part of the standardof care before the still image is captured. We developed and validated three AImodels: an image model using standard plane images, a video model using fly-tovideos, and an ensemble model (combining both image and video). All three werestatistically superior to standard fetal biometry-based GA estimates derived byexpert sonographers, the ensemble model has the lowest mean absolute error(MAE) compared to the clinical standard fetal biometry (mean difference: -1.51$\pm$ 3.96 days, 95% CI [-1.9, -1.1]) on a test set that consisted of 404participants. We showed that our models outperform standard biometry by a moresubstantial margin on fetuses that were small for GA. Our AI models have thepotential to empower trained operators to estimate GA with higher accuracywhile reducing the amount of time required and user variability in measurementacquisition.

Enabling faster and more reliable sonographic assessment of gestational age through machine learning