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.
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DOI：
10.2196/37374
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发表时间：
2022-09-01
影响因子：
1.7
通讯作者：
Noble, J. Alison
中科院分区：
文献类型：
--
作者：
Self, Alice;Chen, Qingchao;Desiraju, Bapu Koundinya;Dhariwal, Sumeet;Gleed, Alexander;Mishra, Divyanshu;Thiruvengadam, Ramachandran;Chandramohan, Varun;Craik, Rachel;Wilden, Elizabeth;Khurana, Ashok;CALOPUS Study Grp, Shinjini;Bhatnagar, Shinjini;Papageorghiou, Aris T.;Noble, J. Alison
关键词：
世界卫生组织推荐了一套包括产科超声波扫描在内的孕期护理方案。普及产前超声波检查存在重大障碍，特别是因为超声波设备的费用和维护需求以及缺乏训练有素的人员。随着低成本、手持超声设备的普及，目前的障碍是全球缺乏受过产科扫描培训的保健提供者。这项研究的目的是改善服务不足地区妇女的怀孕和风险评估。因此，我们正在开展计算机辅助低成本床旁超声（CALOPUS）项目，汇集了机器学习和临床产科超声方面的专家。在两个临床中心（英国和印度）进行的这项前瞻性研究中，对参与的孕妇进行了扫描，并进行了全长超声检查。每名妇女接受2次连续超声扫描。首先是一系列简单的标准化超声扫描（CALOPUS方案），然后立即进行常规的全面临床超声检查，作为对照。我们描述了一个简单易用的临床协议的发展，旨在为非专家用户评估胎儿的生存能力，检测多胎妊娠的存在，评估胎盘的位置，评估羊水量，确定胎儿介绍，并进行基本的胎儿生物测定。CALOPUS协议设计使用最少的步骤，以最大限度地减少冗余信息，同时最大限度地增加诊断信息。在这里，我们描述了如何捕获超声视频和注释用于机器学习。已采集超过5571次扫描，其中已执行1，541，751次标签注释。一个适应的协议，包括一个低骨盆边缘扫描和一个充满产妇膀胱，提高了可视化的子宫颈从28%到91%，胎盘位置的分类从82%到94%。经过培训和标准化之后，注释者内部和注释者之间的一致性达到了很好的水平。CALOPUS研究是一项独特的研究，使用产科超声视频和妊娠11周的注释，并使用新型超声和注释协议随访至出生。这项研究的数据被用于开发和测试几种不同的机器学习算法，以解决与产科风险管理有关的关键临床诊断问题。我们还强调了跨学科多国成像合作的一些挑战和潜在的解决方案。RR1-10.2196/37374
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. 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. 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. 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. 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. RR1-10.2196/37374
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DOI：
10.1111/1471-0528.12313
发表时间：
2013-09-01
影响因子：
5.8
作者：
Papageorghiou, A. T.;Sarris, I.;Salomon, L. J.
通讯作者：
Salomon, L. J.
DOI：
10.1016/0002-9378(92)91347-d
发表时间：
1992-03-01
影响因子：
9.8
作者：
HICKOK, DE;GORDON, DC;DALING, JR
通讯作者：
DALING, JR
影响因子：
--
作者：
Shah S;Bellows BA;Adedipe AA;Totten JE;Backlund BH;Sajed D
通讯作者：
Sajed D
影响因子：
7.1
作者：
Malinger, G.;Paladini, D.;Timor-Tritsch, I. E.
通讯作者：
Timor-Tritsch, I. E.
影响因子：
5
作者：
Bhatnagar, Shinjini;Majumder, Partha P.;Bal, Vineeta
通讯作者：
Bal, Vineeta