AIMC Topic: Infant, Premature

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Predictive connectome subnetwork extraction with anatomical and connectivity priors.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Aug 25, 2018
We present a new method to identify anatomical subnetworks of the human connectome that are optimally predictive of targeted clinical variables, developmental outcomes or disease states. Given a training set of structural or functional brain networks...
Magnetic Resonance Imaging Predictive Value of Tests Anatomic Landmarks Autistic Disorder Humans Machine Learning Infant, Newborn Infant, Premature Connectome
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Quiet sleep detection in preterm infants using deep convolutional neural networks.

Journal of neural engineering Aug 22, 2018
OBJECTIVE: Neonates spend most of their time asleep. Sleep of preterm infants evolves rapidly throughout maturation and plays an important role in brain development. Since visual labelling of the sleep stages is a time consuming task, automated analy...
Algorithms Brain Female Infant, Newborn Humans Neural Networks, Computer Male Automation Electroencephalography Infant, Premature Wakefulness Sleep Stages Sleep
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17-hydroxiprogesterone values in healthy preterm infants.

Colombia medica (Cali, Colombia) Dec 30, 2017
INTRODUCTION: In preterm newborn, problems with the interpretation of 17-OHP may occur.
Follow-Up Studies Gestational Age Male Humans 17-alpha-Hydroxyprogesterone Cohort Studies Female Adrenal Hyperplasia, Congenital Enzyme-Linked Immunosorbent Assay Birth Weight Infant, Newborn Longitudinal Studies Infant, Premature Neonatal Screening
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Machine learning shows association between genetic variability in and cerebral connectivity in preterm infants.

Proceedings of the National Academy of Sciences of the United States of America Dec 11, 2017
Preterm infants show abnormal structural and functional brain development, and have a high risk of long-term neurocognitive problems. The molecular and cellular mechanisms involved are poorly understood, but novel methods now make it possible to addr...
Integrin alpha6 Infant, Newborn Male Polymorphism, Single Nucleotide RNA-Binding Proteins Infant, Premature Connectome PPAR gamma Diffusion Tensor Imaging Humans Machine Learning Brain Female
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BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

NeuroImage Sep 28, 2016
We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is compo...
Diffusion Tensor Imaging Infant, Premature, Diseases Neurodevelopmental Disorders Neural Networks, Computer Brain Female Infant, Newborn Male Brain Mapping Infant, Premature Neural Pathways Humans
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Prediction of brain maturity in infants using machine-learning algorithms.

NeuroImage May 11, 2016
Recent resting-state functional MRI investigations have demonstrated that much of the large-scale functional network architecture supporting motor, sensory and cognitive functions in older pediatric and adult populations is present in term- and prema...
Prognosis Female Infant, Newborn Reproducibility of Results Sensitivity and Specificity Male Support Vector Machine Magnetic Resonance Imaging Humans Algorithms Brain Infant, Premature Regression Analysis Image Enhancement Brain Mapping Gestational Age Image Interpretation, Computer-Assisted Pattern Recognition, Automated
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Automatic Segmentation of MR Brain Images With a Convolutional Neural Network.

IEEE transactions on medical imaging Mar 30, 2016
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages. This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes u...
Infant, Premature Adult Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Infant, Newborn Aged Humans Young Adult Neural Networks, Computer Machine Learning
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Machine-learning to characterise neonatal functional connectivity in the preterm brain.

NeuroImage Sep 2, 2015
Brain development is adversely affected by preterm birth. Magnetic resonance image analysis has revealed a complex fusion of structural alterations across all tissue compartments that are apparent by term-equivalent age, persistent into adolescence a...
Male Magnetic Resonance Imaging Brain Mapping Infant, Premature Machine Learning Humans Brain Female Connectome Infant, Newborn
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Evaluating Prevalence of Preterm Postnatal Growth Faltering Using Fenton 2013 and INTERGROWTH-21st Growth Charts with Logistic and Machine Learning Models.

Nutrients May 20, 2025
Postnatal growth faltering (PGF) significantly affects premature neonates, leading to compromised neurodevelopment and an increased risk of long-term health complications. This retrospective study at a level III NICU of a tertiary hospital analyzed...
Machine Learning Retrospective Studies Female Infant, Newborn Humans Infant, Premature Male Gestational Age Prevalence Infant, Small for Gestational Age Growth Disorders Growth Charts Logistic Models
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Improving Neonatal Care with AI: Class Weight Optimization for Respiratory Distress Syndrome Prediction in Very Low Birth Weight Infants.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
In this study, we developed an AI model to predict Respiratory Distress Syndrome (RDS) in premature infants, aiming to reduce unnecessary treatment with artificial pulmonary surfactant. We analyzed data from 13,120 infants in 76 hospitals, considerin...
Infant, Premature Neural Networks, Computer Infant, Very Low Birth Weight Artificial Intelligence Female Respiratory Distress Syndrome, Newborn Humans Machine Learning Infant, Newborn Algorithms Male Support Vector Machine
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