Assessment of Twin Gestation with AI-Assisted Ultrasound

About This Grant

Assessment of Twin Gestation with AI-Assisted Ultrasound ABSTRACT This research aims to address the significant challenge of diagnosing and managing twin pregnancies in resource-constrained settings, where access to obstetric ultrasound services is often limited. Despite the clear benefit to both mothers and babies, many low- and middle-income countries and rural areas of the United States face substantial barriers to routine obstetric ultrasound, owing to the high costs of equipment and the need for trained sonographers to perform the scans. Consequently, twin pregnancies frequently go undetected until late in gestation, or even delivery, limiting the opportunities for effective intervention and management. In this proposal, we outline an innovative solution to twin assessment in resource-constrained settings. Point-of- care ultrasound (POCUS) devices offer a cost-effective alternative to traditional ultrasound machines. A "blind sweep" protocol enables providers without formal sonography training to conduct basic scans. And deep learning AI models analyze blind sweeps to make diagnoses. The overarching goal of this R01 proposal is to produce a suite of five essential tools that will allow obstetric providers working in resource-constrained settings to better diagnose and manage twin gestations. These tools include (a) diagnosis of twins, (b) estimating twin gestational age, (c) assessing chorionicity, (d) monitoring individual growth and growth concordance, and (e) establishing fetal presentation. The twin-specific AI models that underly tools a and b have already been developed and are ready to be tested in prospective diagnostic accuracy studies (Aims 1 and 2). Tools c, d, and e require additional data collection and model development (Aim 3). Our objectives will be supported by a common protocol in Chapel Hill, North Carolina, and Lusaka, Zambia, where our team has long-standing clinical research sites. We will enroll 700 women (350 twin and 350 singleton pregnancies) in a shared clinical cohort serving our diagnostic studies (Aims 1 and 2) and an additional 100 twin pregnancies (complementing 350 from our shared clinical cohort) to aid in new model development (Aim 3). The successful execution of our project holds significant potential for expanding care access to an exceedingly vulnerable population.