Empowering frontline maternal care with AI-assisted ultrasound

The World Health Organization (WHO) estimates that more than 250,000 preventable deaths occur worldwide each year due to pregnancy-related complications. While prenatal ultrasound can improve outcomes and reduce fetal and maternal mortality, two-thirds of people globally lack access to diagnostic medical imaging like ultrasound. Current methods that support maternal health rely heavily on specialized clinical training, leaving millions in low-resource settings without access to a single ultrasound during their entire pregnancy.

We saw an opportunity to conduct research towards addressing this real-world need, by developing machine learning systems that interpret "blind sweep" ultrasounds. This way of scanning allows frontline healthcare workers without specialized sonography training to perform scans by simply gliding low-cost, portable ultrasound probes across the abdomen in a standardized pattern.

Because many regions face critical shortages of experts, and traditional sonographer training is both expensive and requires a multi-year commitment, enabling these health workers to perform assessments with minimal training has the potential to significantly expand prenatal screening coverage.

Combining the minimal training requirement with AI interpretations could ultimately mean a successfully developed tool that has the potential to increase availability of ultrasound-based pregnancy assessment, and thereby improve access to higher quality maternal health.

“The gestational age is basically how far along a woman is in pregnancy. All of our screening, a lot of our treatments, a lot of our indications for delivery are all based on this master timeline that you get from the gestational age.”

- Nichole Young-Lin, OB-GYN Physician

Evaluating performance across diverse clinical settings

In this study, our original AI model was minimally tuned on only one of our target populations, and then prospectively tested across both target environments: