AIMC Topic: Infant, Extremely Premature
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Development of a simplified model and nomogram for the prediction of pulmonary hemorrhage in respiratory distress syndrome in extremely preterm infants.
BMC pediatrics
Nov 21, 2024
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...
Can deep learning classify cerebral ultrasound images for the detection of brain injury in very preterm infants?
European radiology
Aug 30, 2024
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
Aug 16, 2024
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...
A comprehensive study on machine learning models combining with oversampling for bronchopulmonary dysplasia-associated pulmonary hypertension in very preterm infants.
Respiratory research
May 8, 2024
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...
Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.
Scientific reports
Feb 24, 2022
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...
Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.
European journal of pediatrics
Nov 3, 2021
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...
Machine learning models for identifying preterm infants at risk of cerebral hemorrhage.
PloS one
Jan 15, 2020
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...
Deep learning predicts extreme preterm birth from electronic health records.
Journal of biomedical informatics
Oct 31, 2019
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...
Optimizing neural networks for medical data sets: A case study on neonatal apnea prediction.
Artificial intelligence in medicine
Jul 25, 2019
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...
Early prediction of cognitive deficits in very preterm infants using functional connectome data in an artificial neural network framework.
NeuroImage. Clinical
Jan 31, 2018
Investigation of the brain's functional connectome can improve our understanding of how an individual brain's organizational changes influence cognitive function and could result in improved individual risk stratification. Brain connectome studies in...
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