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

Journals

  1. Toscano M, Marini T, Lennon C, Erlick M, Silva H, Crofton K, Serratelli W, Rana N, Dozier A, Castaneda B, Baran T, Drennan K. Diagnosis of Pregnancy Complications Using Blind Ultrasound Sweeps Performed by Individuals Without Prior Formal Ultrasound Training. Obstetrics & Gynecology 2023;141(5):937 View
  2. Venkatayogi N, Gupta M, Gupta A, Nallaparaju S, Cheemalamarri N, Gilari K, Pathak S, Vishwanath K, Soney C, Bhattacharya T, Maleki N, Purkayastha S, Gichoya J. From Seeing to Knowing with Artificial Intelligence: A Scoping Review of Point-of-Care Ultrasound in Low-Resource Settings. Applied Sciences 2023;13(14):8427 View
  3. Ranger B, Bradburn E, Chen Q, Kim M, Noble J, Papageorghiou A. Portable ultrasound devices for obstetric care in resource-constrained environments: mapping the landscape. Gates Open Research 2023;7:133 View

Books/Policy Documents

  1. Kwon J, Jiao J, Self A, Alison Noble J, Papageorghiou A. Trustworthy Machine Learning for Healthcare. View
  2. Zhao H, Men Q, Gleed A, Papageorghiou A, Noble J. Simplifying Medical Ultrasound. View