A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

Abstract

In-utero progress of fetal development is normally assessed through manual measurements taken from ultrasound images, requiring relatively expensive equipment and well-trained personnel. Such monitoring is therefore unavailable in low-and middle-income countries (LMICs), where most of the perinatal mortality and morbidity exists. The work presented here attempts to identify a proxy for IUGR, which is a significant contributor to perinatal death in LMICs, by determining gestational age (GA) from data derived from simple-to-use, low-cost one-dimensional Doppler ultrasound (1D-DUS) and blood pressure devices. A total of 114 paired 1D-DUS recordings and maternal blood pressure recordings were selected, based on previously described signal quality measures. The average length of 1D-DUS recording was 10.43 ± 1.41 min. The min/median/max systolic and diastolic maternal blood pressures were 79/102/121 and 50.5/63.5/78.5 mmHg, respectively. GA was estimated using features derived from the 1D-DUS and maternal blood pressure using a support vector regression (SVR) approach and GA based on the last menstrual period as a reference target. A total of 50 trials of 5-fold cross-validation were performed for feature selection. The final SVR model was retrained on the training data and then tested on a held-out set comprising 28 normal weight and 25 low birth weight (LBW) newborns. The mean absolute GA error with respect to the last menstrual period was found to be 0.72 and 1.01 months for the normal and LBW newborns, respectively. The mean error in the GA estimate was shown to be negatively correlated with the birth weight. Thus, if the estimated GA is lower than the (remembered) GA calculated from last menstruation, then this could be interpreted as a potential sign of IUGR associated with LBW, and referral and intervention may be necessary. The assessment system may, therefore, have an immediate impact if coupled with suitable intervention, such as nutritional supplementation. However, a prospective clinical trial is required to show the efficacy of such a metric in the detection of IUGR and the impact of the intervention.

Deep Sequence Learning for Assessing Hypertension in Pregnancy from Doppler Signals

“…We demonstrate the utility in a population of 653 pregnant women, with blood pressures in the range of 71 to 169 mmHg SBP and 41 to 110 mmHg DBP, indicating that hypertension is predictable. Interestingly, we noticed differences based on gestational age, which we have also shown can be evaluated from the Doppler device used in this study(31). A dual age-blood pressure prediction therefore can improve performance, and provide a more useful diagnostic.…”

A review of fetal cardiac monitoring, with a focus on low- and middle-income countries

“…In the event a risk factor was identified, the app connected the TBA to appropriate (local or remote) medical care through a voice call, to provide decisional support and onward referral to appropriate healthcare, if needed. The Doppler signal and maternal blood pressure recorded with the mHealth system has allowed the development of different modules for providing estimates of fHRV, gestational age, and hypertension (Valderrama et al 2017, Valderrama et al 2018, Valderrama et al 2019, Valderrama et al 2020a, Valderrama et al 2020b, Katebi et al 2020.…”

Development and external validation of Indian population-specific Garbhini-GA2 model for estimating gestational age in second and third trimesters

“… 19 , 20 , 21 , 22 , 23 Several attempts have been made to develop population-specific GA estimation models in LMICs. 24 , 25 , 26 Many of these attempts utilised the data from the Alliance for Maternal and Newborn Health Improvement (AMANHI) cohorts, which covered study sites in Bangladesh, Pakistan, and Tanzania. 27 These efforts implemented machine learning approaches and utilised data such as one-dimensional Doppler ultrasound, maternal blood pressure, birth weight, metabolite screening data, ultrasound blind sweep cineloop videos, etc.…”