AI Insight
This study describes an evaluation protocol for implementing AI-enabled point-of-care ultrasound (POCUS) for pregnancy dating in six antenatal care facilities in Zambia's Lusaka Province. The multi-year PIKABU pilot program will assess the acceptability, feasibility, and fidelity of integrating this technology into routine care through mixed methods including focus groups, interviews, patient surveys, and time-motion studies. Data will be collected at baseline and every four months to measure outcomes longitudinally among patients, community members, and healthcare providers.
Why it matters
Accurate gestational age dating is critical for maternal healthcare but traditional ultrasound implementation faces barriers of cost, infrastructure, and training in low-resource settings. AI-enabled portable ultrasound could overcome these obstacles and improve antenatal care delivery, with findings potentially informing broader adoption of this technology across similar healthcare settings globally.
Understand the Science
by Erika Gazzetta, Tulani Francis L. Matenga, Stephanie Martin, Nelly Mandona, Rassil Barada, Modesta Chileshe, Elizabeth Stringer, Manze Chinyama, Beene Chembo, Harmony Chi, Angel Mwiche, Selia Ng’anjo, Jeffrey S.A. Stringer, Benjamin H. Chi, Margaret Kasaro
Background
Ultrasound is essential for accurate pregnancy dating, but its implementation in low- and middle-income countries is hindered by cost, infrastructure, and training barriers. The implementation of point-of-care ultrasound (POCUS) with artificial intelligence (AI) technology to accurately estimate gestational age can potentially address these barriers. We describe a protocol to evaluate the acceptability, feasibility, and fidelity of integrating AI-enabled POCUS for gestational age dating into routine antenatal care (ANC) in Zambia’s Lusaka Province.
Methods
PIKABU (Piloting Integration, Knowledge and Acceptability of Baby Ultrasounds) is a multi-year pilot program to introduce and maintain AI-enabled POCUS in six ANC facilities across three districts. To evaluate these activities, we designed a prospective, mixed methods evaluation to assess acceptability, feasibility, and fidelity. Informed by the Consolidated Framework for Implementation Research, the evaluation comprises six separate components: focus-group discussions, in-depth interviews, patient register reviews, time motion studies, implementation strategy assessments, and patient exit surveys. Participants include patients, community members, and healthcare providers. By collecting baseline and follow-up data every four months, we are able to measure these outcomes in longitudinal fashion.
Discussion
Integrating portable, AI-enabled POCUS into routine ANC can improve gestational age dating and improve maternal health services in resource limited settings. Through its assessment of the acceptability, feasibility, and fidelity, this study provides novel insights about service implementation. Our findings are expected to inform policy and programs considering AI-enabled POCUS and support broader adoption across a range of healthcare settings.