Improving preterm newborn identification in low-resource settings with machine learning

Abstract

Background Globally, preterm birth is the leading cause of neonatal death with estimated prevalence and associated mortality highest in low- and middle-income countries (LMICs). Accurate identification of preterm infants is important at the individual level for appropriate clinical intervention as well as at the population level for informed policy decisions and resource allocation. As early prenatal ultrasound is commonly not available in these settings, gestational age (GA) is often estimated using newborn assessment at birth. This approach assumes last menstrual period to be unreliable and birthweight to be unable to distinguish preterm infants from those that are small for gestational age (SGA). We sought to leverage machine learning algorithms incorporating maternal factors associated with SGA to improve accuracy of preterm newborn identification in LMIC settings. Methods and findings This study uses data from an ongoing obstetrical cohort in Lusaka, Zambia that uses early pregnancy ultrasound to estimate GA. Our intent was to identify the best set of parameters commonly available at delivery to correctly categorize births as either preterm (<37 weeks) or term, compared to GA assigned by early ultrasound as the gold standard. Trained midwives conducted a newborn assessment (<72 hours) and collected maternal and neonatal data at the time of delivery or shortly thereafter. New Ballard Score (NBS), last menstrual period (LMP), and birth weight were used individually to assign GA at delivery and categorize each birth as either preterm or term. Additionally, machine learning techniques incorporated combinations of these measures with several maternal and newborn characteristics associated with prematurity and SGA to develop GA at delivery and preterm birth prediction models. The distribution and accuracy of all models were compared to early ultrasound dating. Within our live-born cohort to date (n = 862), the median GA at delivery by early ultrasound was 39.4 weeks (IQR: 38.3–40.3). Among assessed newborns with complete data included in this analysis (n = 468), the median GA by ultrasound was 39.6 weeks (IQR: 38.4–40.3). Using machine learning, we identified a combination of six accessible parameters (LMP, birth weight, twin delivery, maternal height, hypertension in labor, and HIV serostatus) that can be used by machine learning to outperform current GA prediction methods. For preterm birth prediction, this combination of covariates correctly classified >94% of newborns and achieved an area under the curve (AUC) of 0.9796. Conclusions We identified a parsimonious list of variables that can be used by machine learning approaches to improve accuracy of preterm newborn identification. Our best-performing model included LMP, birth weight, twin delivery, HIV serostatus, and maternal factors associated with SGA. These variables are all easily collected at delivery, reducing the skill and time required by the frontline health worker to assess ...

Artificial intelligence and the future of global health

“…Signal processing and machine learning have also been used to estimate perinatal risk factors-eg, to automatically estimate gestational age using data from ultrasound images and other patient variables. [47][48][49] Studies reported high accuracy (>85%) relative to trained experts and other standard gestational age estimation techniques.…”

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“…Prenatal diagnosis of fetal abnormalities has greatly benefited from advances in US technology and, in the last years, also from the advances in ML. ML algorithms have been used in different applications within fetal US medicine such as to predict preterm births [43,44], the risk of euploidy, trisomy 21, and other chromosomal aneuploidies [45] or prediction of perinatal outcomes on asymptomatic short cervical length [46] among others. Regarding fetal cardiology, one of the subfields in which ML has been extensively applied in the last decades is improvement of the diagnosis of fetal hypoxia or acidemia based on the analysis of CTG.…”

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“…Few studies elaborating ML-tools for prediction of survival of preterm neonates are currently available; see, for example, [11] describing ML-models for estimation of mortality risk of very preterm neonates. Recent works [12] elaborated ML-models for more precise assessment of gestational age and to improve identification of preterm neonates.…”