This paper is in the following e-collection/theme issue:

Design and Usability of Medical Devices (141) mHealth in the Developing World/LMICs, Underserved Communities, and for Global Health (717) Pregnancy Information, Education and Lifestyle Interventions (400) Obstetrics, Gynecology, and Reproductive Medicine (329) Health Services in Resource-Poor Settings and LMICs (542) Digital Health, Telehealth and e-Innovation in Clinical Settings (556)

Published on in Vol 9, No 4 (2022): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/34823, first published .
Acceptability and Feasibility of a Low-Cost Device for Gestational Age Assessment in a Low-Resource Setting: Qualitative Study

Acceptability and Feasibility of a Low-Cost Device for Gestational Age Assessment in a Low-Resource Setting: Qualitative Study

Acceptability and Feasibility of a Low-Cost Device for Gestational Age Assessment in a Low-Resource Setting: Qualitative Study

Authors of this article:

Angela Koech1, 2 Author Orcid Image ;   Peris Muoga Musitia1, 3 Author Orcid Image ;   Grace Mkanjala Mwashigadi1 Author Orcid Image ;   Mai-Lei Woo Kinshella4 Author Orcid Image ;   Marianne Vidler4 Author Orcid Image ;   Marleen Temmerman1, 2 Author Orcid Image ;   Rachel Craik5, 6 Author Orcid Image ;   Peter von Dadelszen6 Author Orcid Image ;   J Alison Noble7 Author Orcid Image ;   Aris T Papageorghiou5, 8 Author Orcid Image ;   The PRECISE Network9
Article Authors Cited by (6) Tweetations (10) Metrics

Journals

  1. Hanson C, Annerstedt K, Alsina M, Abeid M, Kidanto H, Alvesson H, Pembe A, Waiswa P, Dossou J, Chipeta E, Straneo M, Benova L. Stillbirth mortality by Robson ten‐group classification system: A cross‐sectional registry of 80 663 births from 16 hospital in sub‐Saharan Africa. BJOG: An International Journal of Obstetrics & Gynaecology 2024;131(11):1465 View
  2. Self A, Schlussel M, Collins G, Dhombres F, Fries N, Haddad G, Salomon L, Massoud M, Papageorghiou A. External validation of models to estimate gestational age in the second and third trimester using ultrasound: A prospective multicentre observational study. BJOG: An International Journal of Obstetrics & Gynaecology 2024;131(13):1862 View
  3. Ginsburg A, Liddy Z, Alkan E, Matcheck K, May S. A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries. Scientific Reports 2025;15(1) View
  4. Craik R, Akuze J, Volvert M, Blencowe H, Mukhanya M, Makanga P, Tchavana C, Moore S, Vala A, Koech A, Tribe R, Noble A, Bah B, D’Alessandro U, Vidler M, Tu D, Maculuve S, Wanje O, Idris Y, Mwashigadi G, Ochieng M, Filippi V, Roca A, Magee L, Poston L, Mistry H, Bah Y, Li J, Temmerman M, Sevene E, Jah H, Mwadime E, Barratt B, Papageorghiou A, Makacha L, Quimice L, Touray F, Salisbury T, Kongira F, von Dadelszen P. PREgnancy Care Integrating translational Science, Everywhere (PRECISE): a prospective cohort study of African pregnant and non-pregnant women to investigate placental disorders – cohort profile. BMJ Open 2025;15(5):e091831 View
  5. Murray L, Montague E. Factors Influencing Trust in Artificial Intelligence Interventions in Healthcare: A Rapid Review. Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care 2025;14(1):44 View
  6. Ferrarini F, Leonelli S. Risks and benefits posed by artificial intelligence to workers’ mental health: a systematic literature review. Industrial Management & Data Systems 2025:1 View