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

• Authors: Camilo E Valderrama, Emory University; Faezeh Marzbanrad, Monash University, Melbourne; Rachel Hall-Clifford, Emory University; Peter Rohloff, Wuqu’ Kawoq Maya Health Alliance; Gari Clifford, Emory University
• Journal Title: Frontiers in Artificial Intelligence
• Volume: Volume 3
• Publisher: Frontiers Media | 2020-08-07, Pages 56-56
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.3389/frai.2020.00056
• Subjects: Engineering, Biomedical; Engineering, Electronics and Electrical
• Keywords: fetal heart rate (FHR); gestational age estimation; intra-uterine growth restriction (IUGR); low-and middle-income countries (LMICs); maternal blood pressure; one-dimension Doppler ultrasound (1D-DUS); signal processing; supervised machine learning

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.