AIMC Topic: Infant, Newborn

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Predictive modeling for early detection of biliary atresia in infants with cholestasis: Insights from a machine learning study.

Computers in biology and medicine
Cholestasis, characterized by the obstruction of bile flow, poses a significant concern in neonates and infants. It can result in jaundice, inadequate weight gain, and liver dysfunction. However, distinguishing between biliary atresia (BA) and non-bi...

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures.

Talanta
Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead to delays in receiving appropriate treatment. Mid infrared (IR) spectroscopy is capable of measuring the concentrations of two diagnostic nRDS biomark...

Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms.

IEEE journal of biomedical and health informatics
One in every four newborns suffers from congenital heart disease (CHD) that causes defects in the heart structure. The current gold-standard assessment technique, echocardiography, causes delays in the diagnosis owing to the need for experts who vary...

Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching.

Pediatric cardiology
Total Cardiac Volume (TCV)-based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual ...

Machine learning prediction model of major adverse outcomes after pediatric congenital heart surgery: a retrospective cohort study.

International journal of surgery (London, England)
BACKGROUND: Major adverse postoperative outcomes (APOs) can greatly affect mortality, hospital stay, care management and planning, and quality of life. This study aimed to evaluate the performance of five machine learning (ML) algorithms for predicti...

Predicting Extubation Readiness in Preterm Infants Utilizing Machine Learning: A Diagnostic Utility Study.

The Journal of pediatrics
OBJECTIVE: The objective of this study was to predict extubation readiness in preterm infants using machine learning analysis of bedside pulse oximeter and ventilator data.

Machine learning-based analysis for prediction of surgical necrotizing enterocolitis in very low birth weight infants using perinatal factors: a nationwide cohort study.

European journal of pediatrics
Early prediction of surgical necrotizing enterocolitis (sNEC) in preterm infants is important. However, owing to the complexity of the disease, identifying infants with NEC at a high risk for surgical intervention is difficult. We developed a machine...

Mapping cell-to-tissue graphs across human placenta histology whole slide images using deep learning with HAPPY.

Nature communications
Accurate placenta pathology assessment is essential for managing maternal and newborn health, but the placenta's heterogeneity and temporal variability pose challenges for histology analysis. To address this issue, we developed the 'Histology Analysi...

Deep Learning Model for Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Chest Radiographs.

Journal of imaging informatics in medicine
Bronchopulmonary dysplasia (BPD) is common in preterm infants and may result in pulmonary vascular disease, compromising lung function. This study aimed to employ artificial intelligence (AI) techniques to help physicians accurately diagnose BPD in p...