About The Study: Between 14 and 27 weeks’ gestation, novice users with no prior training in ultrasonography estimated gestational age as accurately with the low-cost, point-of-care artificial intelligence (AI) tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of gestational age for all pregnant people.
About The Study: Between 14 and 27 weeks’ gestation, novice users with no prior training in ultrasonography estimated gestational age as accurately with the low-cost, point-of-care artificial intelligence (AI) tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of gestational age for all pregnant people.
Quote from corresponding author Jeffrey S. A. Stringer, MD:
“Our study demonstrates that an AI-enabled, portable ultrasound device can estimate gestational age as accurately as an expert sonographer using an expensive, high-specification machine. This high degree of accuracy was obtained even though the users of the device had no formal training in sonography.
“The most important takeaway is the potential democratization of a critical prenatal diagnostic tool. By enabling accurate gestational age estimation without the need for expensive equipment or specialized training, this technology could significantly expand access to quality prenatal care in resource-limited settings worldwide.
“This research could transform prenatal care delivery globally. In areas where ultrasound was previously unavailable, patients can now receive timely, accurate gestational age estimates, crucial for identifying high-risk pregnancies and guiding appropriate care. Health care providers in remote or underserved areas can offer expert-level diagnostics without extensive training, potentially leading to improved maternal and neonatal outcomes on a large scale.”
Contact information for Jeffrey S. A. Stringer, MD: email jeffrey_stringer@med.unc.edu.
To access the embargoed study: Visit our For The Media website at this link
(doi:10.1001/jama.2024.10770)
Editor’s Note: Please see the article for additional information, including other authors, author contributions and affiliations, conflict of interest and financial disclosures, and funding and support.
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JAMA
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