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

Non-randomized Protocols and Methods (ehealth) (598) Artificial Intelligence (1646) Obstetrics, Gynecology, and Reproductive Medicine (269)

Published on in Vol 11, No 9 (2022): September

This is a member publication of University of Oxford (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/37374, first published .
Developing Clinical Artificial Intelligence for Obstetric Ultrasound to Improve Access in Underserved Regions: Protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) Study

Developing Clinical Artificial Intelligence for Obstetric Ultrasound to Improve Access in Underserved Regions: Protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) Study

Developing Clinical Artificial Intelligence for Obstetric Ultrasound to Improve Access in Underserved Regions: Protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) Study

Authors of this article:

Alice Self1 Author Orcid Image ;   Qingchao Chen2 Author Orcid Image ;   Bapu Koundinya Desiraju3 Author Orcid Image ;   Sumeet Dhariwal2 Author Orcid Image ;   Alexander D Gleed2 Author Orcid Image ;   Divyanshu Mishra3 Author Orcid Image ;   Ramachandran Thiruvengadam3 Author Orcid Image ;   Varun Chandramohan3 Author Orcid Image ;   Rachel Craik1 Author Orcid Image ;   Elizabeth Wilden2 Author Orcid Image ;   Ashok Khurana4 Author Orcid Image ;   The CALOPUS Study Group5 ;   Shinjini Bhatnagar3 Author Orcid Image ;   Aris T Papageorghiou1, 6 Author Orcid Image ;   J Alison Noble2 Author Orcid Image
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