Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 1 h ago
Peer-reviewedCC BY 4.0PLOS One

A pilot study to implement artificial intelligence-enabled, point-of-care obstetric ultrasound for gestational age estimation in Zambia: An evaluation protocol

  • Erika Gazzetta1
  • Tulani Francis L. Matenga2
  • Stephanie Martin3
  • Nelly Mandona4
  • Rassil Barada5
  • Modesta Chileshe6
  • Elizabeth Stringer
  • Manze Chinyama
  • Beene Chembo
  • Harmony Chi
  • Angel Mwiche
  • Selia Ng’anjo
3 more authors
  • Jeffrey S.A. Stringer
  • Benjamin H. Chi
  • Margaret Kasaro
6 affiliations
  1. 1Department of Obstetrics and Gynecology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, United States of America
  2. 2Department of Health Promotion and Education, School of Public Health, University of Zambia, Lusaka, Zambia
  3. 3UNC Global Projects Zambia, LLC, Lusaka, Zambia
  4. 4Department of Nutrition, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
  5. 5Department of Public Health, Ministry of Health, Lusaka, Zambia
  6. 6Department of Obstetrics and Gynaecology, Women and Newborn Hospital, University Teaching Hospital, Lusaka, Zambia
Open at sourcePDFDOICite

Abstract

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.

Related

  1. Artificial intelligence-based prostate cancer risk stratification via large-scale ultrasound images paired with histopathology
    Qiong Zhang, Qiang Lu, Yu Liang +3
    Quantitative imaging in medicine and surgeryPeer-reviewedCC BY-NC-NDFull textMedical imaging & radiologyEurope PMC
  2. Validating Artificial Intelligence Guidance for Ultrasound Acquisition and Remote Interpretation
    T. Maldonado, S. Muluk, P. Rali +14
    medRxivPreprintAll rights reservedFull textMedical imaging & radiologymedRxiv
  3. Ensuring Safe and Responsible Use of Artificial Intelligence in Medical Imaging: Pitfalls and Practical Solutions through the Lens of Obstetric Ultrasound.
    Rishika Chanian, Ramachandran Thiruvengadam, Bapu Koundinya Desiraju
    The Indian journal of radiology & imagingPeer-reviewedCC BY-NC-NDFull textEpidemiology & public healthEurope PMC
  4. Artificial intelligence for ultrasound-based differentiation of soft tissue liposarcoma from benign masses.
    YuKun Deng, Chengjia Qu, Yi Cao +8
    Frontiers in medicinePeer-reviewedCC BYFull textGeneral medicineEurope PMC
  5. OP–249 A comparative evaluation of artificial intelligence models for adolescent-centered scoliosis questions
    Esra Rabia Taşpolat, Ebru Kübra Taşpolat, Tuba Sarıkaya +2
    BMJ Paediatrics OpenPeer-reviewedCC BYObstetrics, gynaecology & paediatricsDOAJ

Cite

BibTeXRIS
BibTeX
@article{gazzetta2026pilot,
  title = {{A pilot study to implement artificial intelligence-enabled, point-of-care obstetric ultrasound for gestational age estimation in Zambia: An evaluation protocol}},
  author = {Erika Gazzetta and Tulani Francis L. Matenga and Stephanie Martin and Nelly Mandona and Rassil Barada and Modesta Chileshe and Elizabeth Stringer and Manze Chinyama and Beene Chembo and Harmony Chi and Angel Mwiche and Selia Ng’anjo and Jeffrey S.A. Stringer and Benjamin H. Chi and Margaret Kasaro},
  journal = {PLOS One},
  year = {2026},
  month = sep,
  doi = {10.1371/journal.pone.0357962},
  url = {https://doi.org/10.1371/journal.pone.0357962},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - A pilot study to implement artificial intelligence-enabled, point-of-care obstetric ultrasound for gestational age estimation in Zambia: An evaluation protocol
AU  - Erika Gazzetta
AU  - Tulani Francis L. Matenga
AU  - Stephanie Martin
AU  - Nelly Mandona
AU  - Rassil Barada
AU  - Modesta Chileshe
AU  - Elizabeth Stringer
AU  - Manze Chinyama
AU  - Beene Chembo
AU  - Harmony Chi
AU  - Angel Mwiche
AU  - Selia Ng’anjo
AU  - Jeffrey S.A. Stringer
AU  - Benjamin H. Chi
AU  - Margaret Kasaro
PY  - 2026
DA  - 2026/09/11
JO  - PLOS One
DO  - 10.1371/journal.pone.0357962
UR  - https://doi.org/10.1371/journal.pone.0357962
AB  - 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.
SN  - 1932-6203
ER  -