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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. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR1-10.2196/37374.
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Background: Body mass index (BMI) is the most popular anthropometric marker to define obesity and cardiometabolic risk. BMI is limited in its ability to discriminate central adiposity and other indices such as waist circumference (WC), and waist to height ratio (WHtR) could be a better choice. In this study, we aimed to evaluate the relative accuracy of these indices for the prediction of hypertension in Indian children and young adults. Methods: Anthropometric indices and blood pressure measurements were obtained in 2609 adolescent children and young adults (10-20 years) across a national residential school system. Z-scores were calculated for anthropometric parameters using the Box-Cox-Cole-Green method and World Health Organization (WHO) growth charts. Hypertension was defined using the sex, age and height specific cutoffs for systolic blood pressure. Receiver operator curve (ROC) analysis was performed to examine the predictive ability. Results: Girls had higher BMI for age in our dataset (p < 0.001), along with higher odds for stunting (95% CI: 1.21 - 1.88) as well as central obesity (95% CI: 2.44 - 3.99). Hypertension was seen in 10.6% of the subjects, with higher age, and higher BMI or WHtR as the predictors. Prehypertension was higher in males (p <0.001). WHtR had acceptable but modest discrimination ability for hypertension (AUC > 0.6) in boys (AUC=0.62) and girls (AUC=0.66). Performance of BMI was better in boys (AUC = 0.67) but poor in girls (AUC = 0.55) Conclusion: WHtR was a better predictor of hypertension in Indian adolescent girls and could be used as an augmented parameter to BMI for a better assessment of cardiovascular risk.
