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Infant, Extremely Premature

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The possible role of artificial intelligence in deciding postnatal steroid management in extremely premature ventilated infants.

Journal of neonatal-perinatal medicine
Clinical decision support (CDS) has shown a positive effect on physicians. There is variability among physicians about using postnatal steroids (PNS) in preterm (PT) infants. It is, therefore, essential to develop tools supporting the decision to use...

Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.

Artificial intelligence in medicine
OBJECTIVE: The neonatal period of a child is considered the most crucial phase of its physical development and future health. As per the World Health Organization, India has the highest number of pre-term births [1], with over 3.5 million babies born...

Deep learning predicts extreme preterm birth from electronic health records.

Journal of biomedical informatics
OBJECTIVE: Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the m...

Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.

PloS one
Intracerebral hemorrhage in preterm infants is a major cause of brain damage and cerebral palsy. The pathogenesis of cerebral hemorrhage is multifactorial. Among the risk factors are impaired cerebral autoregulation, infections, and coagulation disor...

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

European journal of pediatrics
There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed to identify distinct phenotypes in extremely low birth weight infants. We performed an agglomerativ...

Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.

Scientific reports
This study aimed to develop quantitative assessments of spontaneous movements in high-risk preterm infants based on a deep learning algorithm. Video images of spontaneous movements were recorded in very preterm infants at the term-equivalent age. The...

A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants.

Respiratory research
BACKGROUND: Bronchopulmonary dysplasia-associated pulmonary hypertension (BPD-PH) remains a devastating clinical complication seriously affecting the therapeutic outcome of preterm infants. Hence, early prevention and timely diagnosis prior to pathol...

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...

Combining artificial intelligence and conventional statistics to predict bronchopulmonary dysplasia in very preterm infants using routinely collected clinical variables.

Pediatric pulmonology
BACKGROUND: Prematurity is the strongest predictor of bronchopulmonary dysplasia (BPD). Most previous studies investigated additional risk factors by conventional statistics, while the few studies applying artificial intelligence, and specifically ma...

Development of a simplified model and nomogram for the prediction of pulmonary hemorrhage in respiratory distress syndrome in extremely preterm infants.

BMC pediatrics
BACKGROUND: Pulmonary hemorrhage (PH) in respiratory distress syndrome (RDS) in extremely preterm infants exhibits a high mortality rate and poor long-term outcomes. The aim of the present study was to develop a machine learning (ML) predictive model...