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

Chace Lee; Angelica Willis; Christina Chen; Marcin Sieniek; Akib Uddin; Jonny Wong; Rory Pilgrim; Katherine Chou; Daniel Tse; Shravya Shetty; Ryan G. Gomes

arXiv · 2203.11903

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

Chace Lee·Angelica Willis·Christina Chen·Marcin Sieniek·Akib Uddin·Jonny Wong·Rory Pilgrim·Katherine Chou·Daniel Tse·Shravya Shetty·Ryan G. Gomes

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Abstract

Fetal ultrasounds are an essential part of prenatal care and can be used to estimate gestational age (GA). Accurate GA assessment is important for providing appropriate prenatal care throughout pregnancy and identifying complications such as fetal growth disorders. Since derivation of GA from manual fetal biometry measurements (head, abdomen, femur) are operator-dependent and time-consuming, there have been a number of research efforts focused on using artificial intelligence (AI) models to estimate GA using standard biometry images, but there is still room to improve the accuracy and reliability of these AI systems for widescale adoption. To improve GA estimates, without significant change to provider workflows, we leverage AI to interpret standard plane ultrasound images as well as 'fly-to' ultrasound videos, which are 5-10s videos automatically recorded as part of the standard of care before the still image is captured. We developed and validated three AI models: an image model using standard plane images, a video model using fly-to videos, and an ensemble model (combining both image and video). All three were statistically superior to standard fetal biometry-based GA estimates derived by expert 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 404 participants. We showed that our models outperform standard biometry by a more substantial margin on fetuses that were small for GA. Our AI models have the potential to empower trained operators to estimate GA with higher accuracy while reducing the amount of time required and user variability in measurement acquisition.

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Chace Lee, Angelica Willis, Christina Chen, Marcin Sieniek, Akib Uddin, Jonny Wong, Rory Pilgrim, Katherine Chou, Daniel Tse, Shravya Shetty, Ryan G. Gomes. 2022-03-22. Enabling faster and more reliable sonographic assessment of gestational age through machine learning. https://arxiv.org/abs/2203.11903

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