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
BackgroundThe 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 equipme...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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JMIR Publications
2022-09-01
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| Series: | JMIR Research Protocols |
| Online Access: | https://www.researchprotocols.org/2022/9/e37374 |
| _version_ | 1827857994906337280 |
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| author | Alice Self Qingchao Chen Bapu Koundinya Desiraju Sumeet Dhariwal Alexander D Gleed Divyanshu Mishra Ramachandran Thiruvengadam Varun Chandramohan Rachel Craik Elizabeth Wilden Ashok Khurana Shinjini Bhatnagar Aris T Papageorghiou J Alison Noble |
| author_facet | Alice Self Qingchao Chen Bapu Koundinya Desiraju Sumeet Dhariwal Alexander D Gleed Divyanshu Mishra Ramachandran Thiruvengadam Varun Chandramohan Rachel Craik Elizabeth Wilden Ashok Khurana Shinjini Bhatnagar Aris T Papageorghiou J Alison Noble |
| author_sort | Alice Self |
| collection | DOAJ |
| description |
BackgroundThe 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.
ObjectiveThe 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.
MethodsIn 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.
ResultsOver 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.
ConclusionsThe 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.
International Registered Report Identifier (IRRID)RR1-10.2196/37374 |
| first_indexed | 2024-03-12T12:49:32Z |
| format | Article |
| id | doaj.art-617e2cfd58f0408f8b60d23e740a7a64 |
| institution | Directory Open Access Journal |
| issn | 1929-0748 |
| language | English |
| last_indexed | 2024-03-12T12:49:32Z |
| publishDate | 2022-09-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Research Protocols |
| spelling | doaj.art-617e2cfd58f0408f8b60d23e740a7a642023-08-28T23:00:19ZengJMIR PublicationsJMIR Research Protocols1929-07482022-09-01119e3737410.2196/37374Developing Clinical Artificial Intelligence for Obstetric Ultrasound to Improve Access in Underserved Regions: Protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) StudyAlice Selfhttps://orcid.org/0000-0002-8846-9916Qingchao Chenhttps://orcid.org/0000-0002-1216-5609Bapu Koundinya Desirajuhttps://orcid.org/0000-0002-5955-5364Sumeet Dhariwalhttps://orcid.org/0000-0001-5757-7757Alexander D Gleedhttps://orcid.org/0000-0002-6492-075XDivyanshu Mishrahttps://orcid.org/0000-0002-3264-8739Ramachandran Thiruvengadamhttps://orcid.org/0000-0002-9195-2730Varun Chandramohanhttps://orcid.org/0000-0001-5810-0176Rachel Craikhttps://orcid.org/0000-0001-7885-0775Elizabeth Wildenhttps://orcid.org/0000-0003-0415-7649Ashok Khuranahttps://orcid.org/0000-0002-9993-377XShinjini Bhatnagarhttps://orcid.org/0000-0003-1703-5296Aris T Papageorghiouhttps://orcid.org/0000-0001-8143-2232J Alison Noblehttps://orcid.org/0000-0002-3060-3772 BackgroundThe 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. ObjectiveThe 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. MethodsIn 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. ResultsOver 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. ConclusionsThe 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. International Registered Report Identifier (IRRID)RR1-10.2196/37374https://www.researchprotocols.org/2022/9/e37374 |
| spellingShingle | Alice Self Qingchao Chen Bapu Koundinya Desiraju Sumeet Dhariwal Alexander D Gleed Divyanshu Mishra Ramachandran Thiruvengadam Varun Chandramohan Rachel Craik Elizabeth Wilden Ashok Khurana Shinjini Bhatnagar Aris T Papageorghiou J Alison Noble 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 JMIR Research Protocols |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| url | https://www.researchprotocols.org/2022/9/e37374 |
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