Self Alice, Chen Qingchao, Desiraju Bapu Koundinya, Dhariwal Sumeet, Gleed Alexander D, Mishra Divyanshu, Thiruvengadam Ramachandran, Chandramohan Varun, Craik Rachel, Wilden Elizabeth, Khurana Ashok, Bhatnagar Shinjini, Papageorghiou Aris T, Noble J Alison
Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, United Kingdom.
Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom.
JMIR Res Protoc. 2022 Sep 1;11(9):e37374. doi: 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.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/37374.
世界卫生组织推荐了一套包含产科超声扫描的孕期护理方案。普遍获得产前超声检查存在重大障碍，特别是由于超声设备的成本和维护需求以及缺乏训练有素的人员。随着低成本的手持式超声设备广泛可得，当前的障碍是全球缺乏接受过产科扫描培训的医疗服务提供者。
本研究的目的是改善服务不足地区妇女的妊娠和风险评估。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，对参与的孕妇进行扫描并进行全程超声检查。每位妇女连续接受2次超声扫描。第一次是一系列简单、标准化的超声扫查（CALOPUS方案），随后立即进行常规的全面临床超声检查作为对照。我们描述了一种为非专业用户设计的简单易用临床方案的开发，以评估胎儿活力、检测多胎妊娠、评估胎盘位置、评估羊水量、确定胎儿胎位并进行基本的胎儿生物测量。CALOPUS方案的设计采用了最少的步骤，以尽量减少冗余信息，同时最大化诊断信息。在此，我们描述了如何为机器学习捕获超声视频和标注。
已采集超过5571次扫描，从中进行了1541751次标签标注。一种经过调整的方案，包括低位骨盆边缘扫查和充盈良好的孕妇膀胱，将宫颈可视化率从28%提高到91%，胎盘位置分类准确率从82%提高到94%。经过培训和标准化后，标注者内部和标注者之间的一致性水平很高。
CALOPUS研究是一项独特的研究，它使用了从11周开始直至出生的妊娠产科超声视频和标注，采用了新颖的超声和标注方案。本研究的数据正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相关的关键临床诊断问题。我们还强调了跨学科跨国成像合作的一些挑战和潜在解决方案。
国际注册报告识别码（IRRID）：RR1-10.2196/37374。
