AIMC Topic: Infant, Newborn

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Computer Vision Identification of Trachomatous Inflammation-Follicular Using Deep Learning.

Cornea
PURPOSE: Trachoma surveys are used to estimate the prevalence of trachomatous inflammation-follicular (TF) to guide mass antibiotic distribution. These surveys currently rely on human graders, introducing a significant resource burden and potential f...

Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches.

Scientific reports
To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). Th...

Geographic inequities in neonatal survival in Nigeria: a cross-sectional evidence from spatial and artificial neural network analyses.

Journal of biosocial science
This study was conducted to provide empirical evidence of geographical variations of neonatal mortality and its associated social determinants with a view to improving neonatal survival at the subnational level in Nigeria. With a combination of spati...

Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data.

BMC pregnancy and childbirth
BACKGROUND: Newborns are shaped by prenatal maternal experiences. These include a pregnant person's physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a series of machine learning models ...

From bytes to bedside: a systematic review on the use and readiness of artificial intelligence in the neonatal and pediatric intensive care unit.

Intensive care medicine
PURPOSE: Despite its promise to enhance patient outcomes and support clinical decision making, clinical use of artificial intelligence (AI) models at the bedside remains limited. Translation of advancements in AI research into tangible clinical benef...

Comparative analysis of artificial intelligence and expert assessments in detecting neonatal procedural pain.

Scientific reports
Assessing pain in newborns in the NICU is crucial due to their frequent exposure to painful stimuli, yet it's challenging due to the subjective nature of current methods. This study aimed to evaluate the effectiveness of an AI system designed for aut...

Prediction of delayed breastfeeding initiation among mothers having children less than 2 months of age in East Africa: application of machine learning algorithms.

Frontiers in public health
BACKGROUND: Delayed breastfeeding initiation is a significant public health concern, and reducing the proportion of delayed breastfeeding initiation in East Africa is a key strategy for lowering the Child Mortality rate. However, there is limited evi...

Can deep learning classify cerebral ultrasound images for the detection of brain injury in very preterm infants?

European radiology
OBJECTIVES: Cerebral ultrasound (CUS) is the main imaging screening tool in preterm infants. The aim of this work is to develop deep learning (DL) models that classify normal vs abnormal CUS to serve as a computer-aided detection tool providing timel...

Uncovering early predictors of cerebral palsy through the application of machine learning: a case-control study.

BMJ paediatrics open
OBJECTIVE: Cerebral palsy (CP) is a group of neurological disorders with profound implications for children's development. The identification of perinatal risk factors for CP may lead to improved preventive and therapeutic strategies. This study aime...

Next-generation pediatric care: nanotechnology-based and AI-driven solutions for cardiovascular, respiratory, and gastrointestinal disorders.

World journal of pediatrics : WJP
BACKGROUND: Global pediatric healthcare reveals significant morbidity and mortality rates linked to respiratory, cardiac, and gastrointestinal disorders in children and newborns, mostly due to the complexity of therapeutic management in pediatrics an...