A pilot study to implement artificial intelligence-enabled, point-of-care obstetric ultrasound for gestational age estimation in Zambia: An evaluation protocol.
Authors
Affiliations (6)
Affiliations (6)
- Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America.
- Department of Health Promotion and Education, School of Public Health, University of Zambia, Lusaka, Zambia.
- UNC Global Projects Zambia, LLC, Lusaka, Zambia.
- Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
- Department of Public Health, Ministry of Health, Lusaka, Zambia.
- Department of Obstetrics and Gynaecology, Women and Newborn Hospital, University Teaching Hospital, Lusaka, Zambia.
Abstract
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. 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. 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.