AIMC Topic: Infant, Newborn

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Application of artificial neural networks to evaluate femur development in the human fetus.

PloS one
The present article concentrates on an innovative analysis that was performed to assess the development of the femur in human fetuses using artificial intelligence. As a prerequisite, linear dimensions, cross-sectional surface areas and volumes of th...

The infant health effects of doulas: Leveraging big data and machine learning to inform cost-effective targeting.

Health economics
Doula services represent an underutilized maternal and child health intervention with the potential to improve outcomes through the provision of physical, emotional, and informational support. However, there is limited evidence of the infant health e...

The scope of artificial intelligence in retinopathy of prematurity (ROP) management.

Indian journal of ophthalmology
Artificial Intelligence (AI) is a revolutionary technology that has the potential to develop into a widely implemented system that could reduce the dependence on qualified professionals/experts for screening the large at-risk population, especially i...

Computational Approaches for Predicting Preterm Birth and Newborn Outcomes.

Clinics in perinatology
Preterm birth (PTB) and its associated morbidities are a leading cause of infant mortality and morbidity. Accurate predictive models and a better biological understanding of PTB-associated morbidities are critical in reducing their adverse effects. I...

Intelligence Sparse Sensor Network for Automatic Early Evaluation of General Movements in Infants.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
General movements (GMs) have been widely used for the early clinical evaluation of infant brain development, allowing immediate evaluation of potential development disorders and timely rehabilitation. The infants' general movements can be captured di...

Reassessing acquired neonatal intestinal diseases using unsupervised machine learning.

Pediatric research
BACKGROUND: Acquired neonatal intestinal diseases have an array of overlapping presentations and are often labeled under the dichotomous classification of necrotizing enterocolitis (which is poorly defined) or spontaneous intestinal perforation, hind...

Improving the second-tier classification of methylmalonic acidemia patients using a machine learning ensemble method.

World journal of pediatrics : WJP
INTRODUCTION: Methylmalonic acidemia (MMA) is a disorder of autosomal recessive inheritance, with an estimated prevalence of 1:50,000. First-tier clinical diagnostic tests often return many false positives [five false positive (FP): one true positive...

Information fusion for infant age estimation from deciduous teeth using machine learning.

American journal of biological anthropology
OBJECTIVES: Over the past few years, several methods have been proposed to improve the accuracy of age estimation in infants with a focus on dental development as a reliable marker. However, traditional approaches have limitations in efficiently comb...