Deep learning to estimate gestational age from fly-to cineloop videos: A novel approach to ultrasound quality control.

Ambika V Viswanathan Teeranan Pokaprakarn Margaret P Kasaro Hina R Shah Juan Carlos PrietoChiraz BenabdelkaderYuri V Sebastião Ntazana Sindano Elizabeth M Stringer Jeffrey S A Stringer

Published in: International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics (2024)

Our AI model estimated GA more accurately than expert biometry. Because fly-to cineloop videos can be obtained without any change to sonographer workflow, the model represents a no-cost guardrail that could be incorporated into both low-cost and commercial ultrasound devices to prevent reporting of most gross GA estimation errors.

Keyphrases

gestational age
low cost
quality control
pet ct
deep learning
magnetic resonance imaging
birth weight
preterm birth
artificial intelligence
adverse drug
ultrasound guided
patient safety
drosophila melanogaster
contrast enhanced ultrasound
body mass index
clinical practice
quality improvement