Journal article
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
- Abstract:
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Background: The World Health Organization recommends a package of pregnancy care that includes obstetric ultrasound scans. There are significant barriers to universal access to antenatal ultrasound, particularly because of the cost and need for maintenance of ultrasound equipment and a lack of trained personnel. As low-cost, handheld ultrasound devices have become widely available, the current roadblock is the global shortage of health care providers trained in obstetric scanning.
Objective: The aim of this study is to improve pregnancy and risk assessment for women in underserved regions. Therefore, we are undertaking the Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) project, bringing together experts in machine learning and clinical obstetric ultrasound.
Methods: In this prospective study conducted in two clinical centers (United Kingdom and India), participating pregnant women were scanned and full-length ultrasounds were performed. Each woman underwent 2 consecutive ultrasound scans. The first was a series of simple, standardized ultrasound sweeps (the CALOPUS protocol), immediately followed by a routine, full clinical ultrasound examination that served as the comparator. We describe the development of a simple-to-use clinical protocol designed for nonexpert users to assess fetal viability, detect the presence of multiple pregnancies, evaluate placental location, assess amniotic fluid volume, determine fetal presentation, and perform basic fetal biometry. The CALOPUS protocol was designed using the smallest number of steps to minimize redundant information, while maximizing diagnostic information. Here, we describe how ultrasound videos and annotations are captured for machine learning.
Results: Over 5571 scans have been acquired, from which 1,541,751 label annotations have been performed. An adapted protocol, including a low pelvic brim sweep and a well-filled maternal bladder, improved visualization of the cervix from 28% to 91% and classification of placental location from 82% to 94%. Excellent levels of intra- and interannotator agreement are achievable following training and standardization.
Conclusions: The CALOPUS study is a unique study that uses obstetric ultrasound videos and annotations from pregnancies dated from 11 weeks and followed up until birth using novel ultrasound and annotation protocols. The data from this study are being used to develop and test several different machine learning algorithms to address key clinical diagnostic questions pertaining to obstetric risk management. We also highlight some of the challenges and potential solutions to interdisciplinary multinational imaging collaboration.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 1.0MB, Terms of use)
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- Publisher copy:
- 10.2196/37374
- Publisher:
- JMIR Publications
- Journal:
- JMIR Research Protocols More from this journal
- Volume:
- 11
- Issue:
- 9
- Pages:
- e37374-
- Publication date:
- 2022-09-01
- Acceptance date:
- 2022-06-21
- DOI:
- EISSN:
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1929-0748
- ISSN:
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1929-0748
- Pmid:
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36048518
- Language:
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English
- Keywords:
- Pubs id:
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1277279
- Local pid:
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pubs:1277279
- Deposit date:
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2022-09-06
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- Copyright holder:
- Self et al
- Copyright date:
- 2022
- Rights statement:
- © Alice Self, Qingchao Chen, Bapu Koundinya Desiraju, Sumeet Dhariwal, Alexander D Gleed, Divyanshu Mishra, Ramachandran Thiruvengadam, Varun Chandramohan, Rachel Craik, Elizabeth Wilden, Ashok Khurana, The CALOPUS Study Group, Shinjini Bhatnagar, Aris T Papageorghiou, J Alison Noble. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 01.09.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is properly cited. The complete bibliographic information, a link to the original publication on https://www.researchprotocols.org, as well as this copyright and license information must be included.
- Licence:
- CC Attribution (CC BY)
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