A portable AI tool can estimate the age of an unborn baby from scans as well as trained sonographers, potentially extending access to vital prenatal ultrasonography where it might not otherwise be available.
The findings, in JAMA Network Open, reveal the potential of artificial intelligence to expand access to diagnostic tasks such as ultrasonography using novice operators in low-resource settings.
An AI model trained on blind sweep ultrasonography scans performed as well as traditional sonographers on estimating gestational age when used by novice operators across diverse geographical and infrastructural contexts, with minimal fine tuning.
“This system could mitigate disproportionate maternal or fetal comorbidity in low-resource areas by providing access to an essential clinical tool, serving as a template for automating and democratizing other ultrasonography-based diagnostics in future obstetric work,” reported Ryan Gomes, PhD, from Google in California, and co-workers.
Determining gestational age is a fundamental component of prenatal care, allowing obstetric care that can include life-saving interventions to prevent pre- and post-term births and manage high-risk conditions.
But while traditional ultrasonography is recommended by the World Health Organization, it requires skilled sonographers and expensive equipment that may not be available, particularly in low-resource settings.
AI using low-cost portable devices offer a potentially accessibly alternative, with blind sweep ultrasonography—a set of protocolized sweeps that does not rely on real-time imaging interpretation—emerging as a particularly promising approach.
However, clinical sites vary significantly in workflow, staffing, patient demographics, and equipment, which could affect the accuracy of AI-based assessment.
Gomes and team therefore examined the value of an AI tool to estimate gestational age from blind sweep ultrasonography scans across a variety of settings.
The AI-based system was originally trained on data from suburban North Carolina and urban Zambia and validated in a Chicago urban academic center and a Kenyan urban clinic using a different portable probe.
The broader cohort included 2043 participants—consisting of 1008 in Chicago and 1035 in Nairobi.
Fine-tuning using 180 examinations from 120 Chicago participants—approximately 6% of original training size, split evenly for training and validation—targeted generalization to new hardware and gestational-age distributions.
The primary evaluation set of 385 participants—192 in Chicago and 193 in Nairobi—had gestational ages from 16 to 36 weeks.
The researchers found that the AI model effectively generalized to new clinical environments and institutions, achieving a mean absolute error of 4.2 days that was noninferior to the clinical standard.
Its robust performance in Nairobi, with a mean absolute error of 4.3 days without local-tuning mirrored results in Chicago, where this mean error was 4.1 days and underscored the model’s inherent adaptability and transfer-learning efficacy.
These mean absolute errors with the adapted model were similar to the standard of care.
“The lower sweep rejection rate in the Nairobi setting (1.8% vs 7.9% in Chicago) may suggest that approximately six hours of formal, interactive, hands-on training improves acquisition quality compared with informal and written instruction,” the authors noted.
Nonetheless, they conclude overall: “This generalizable accuracy, achieved with low-cost probes, represents an important step toward World Health Organization–recommended scalable prenatal implementation.”
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