AIMC Topic: Infant, Premature

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Construction and SHAP interpretability analysis of a risk prediction model for feeding intolerance in preterm newborns based on machine learning.

BMC medical informatics and decision making Nov 18, 2024
OBJECTIVE: To construct a highly accurate and interpretable feeding intolerance (FI) risk prediction model for preterm newborns based on machine learning (ML) to assist medical staff in clinical diagnosis.
Machine Learning Retrospective Studies Female Infant, Newborn Humans Food Intolerance Male Risk Assessment Infant, Premature
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Deep Learning Approaches for the Assessment of Germinal Matrix Hemorrhage Using Neonatal Head Ultrasound.

Sensors (Basel, Switzerland) Oct 31, 2024
Germinal matrix hemorrhage (GMH) is a critical condition affecting premature infants, commonly diagnosed through cranial ultrasound imaging. This study presents an advanced deep learning approach for automated GMH grading using the YOLOv8 model. By a...
Infant, Newborn Male Deep Learning Ultrasonography Humans Female Cerebral Hemorrhage Head Infant, Premature
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Early prediction of mortality and morbidities in VLBW preterm neonates using machine learning.

Pediatric research Oct 8, 2024
BACKGROUND: Predicting mortality and specific morbidities before they occur may allow for interventions that may improve health trajectories.
Retrospective Studies Female Infant, Newborn Infant, Premature Male Infant, Premature, Diseases Bronchopulmonary Dysplasia Enterocolitis, Necrotizing ROC Curve Humans Infant, Very Low Birth Weight Morbidity Machine Learning Infant Mortality Algorithms
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Machine-learning-based evaluation of the usefulness of lactate for predicting neonatal mortality in preterm infants.

Pediatrics and neonatology Sep 25, 2024
BACKGROUND: Unlike in adult and pediatric patients, the usefulness of lactate in preterm infants has not been thoroughly discussed. This study aimed to evaluate whether the lactate level in the first hours of life is an important factor associated wi...
Humans Machine Learning Retrospective Studies Infant Mortality Republic of Korea Lactic Acid Female Infant, Premature Infant, Newborn Infant, Very Low Birth Weight Male Hospital Mortality
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Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage.

International journal of molecular sciences Sep 25, 2024
Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical on...
Cerebral Hemorrhage Infant, Premature Prospective Studies Biomarkers Prognosis Female Infant, Newborn Humans Male Proteomics Machine Learning Longitudinal Studies
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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...
Infant, Premature Nova Scotia Humans Deep Learning Female Infant, Newborn Ultrasonography Male Brain Injuries Infant, Extremely Premature Image Interpretation, Computer-Assisted
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Risk factors for the time to development of retinopathy of prematurity in premature infants in Iran: a machine learning approach.

BMC ophthalmology Aug 23, 2024
BACKGROUND: Retinopathy of prematurity (ROP), is a preventable leading cause of blindness in infants and is a condition in which the immature retina experiences abnormal blood vessel growth. The development of ROP is multifactorial; nevertheless, the...
Humans Machine Learning Retrospective Studies Female Infant, Newborn Risk Factors Iran Male Infant, Premature Infant Gestational Age Time Factors Retinopathy of Prematurity Birth Weight
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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...
Infant, Premature Humans Gestational Age Infant, Extremely Premature Artificial Intelligence Machine Learning Bronchopulmonary Dysplasia Algorithms Respiration, Artificial Female Infant, Newborn Risk Factors Male
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Host-derived protein profiles of human neonatal meconium across gestational ages.

Nature communications Jul 17, 2024
Meconium, a non-invasive biomaterial reflecting prenatal substance accumulation, could provide valuable insights into neonatal health. However, the comprehensive protein profile of meconium across gestational ages remains unclear. Here, we conducted ...
Heart Defects, Congenital Proteome Female Meconium Humans Infant, Newborn Infant, Premature Male Machine Learning Gestational Age Proteomics Pregnancy Gastrointestinal Diseases
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Automated detection of type 1 ROP, type 2 ROP and A-ROP based on deep learning.

Eye (London, England) Jun 25, 2024
PURPOSE: To provide automatic detection of Type 1 retinopathy of prematurity (ROP), Type 2 ROP, and A-ROP by deep learning-based analysis of fundus images obtained by clinical examination using convolutional neural networks.
Retrospective Studies Humans Male Fundus Oculi Neural Networks, Computer Deep Learning Female Infant, Premature ROC Curve Algorithms Infant, Newborn Gestational Age Area Under Curve Sensitivity and Specificity Retinopathy of Prematurity
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