AIMC Topic: Infant, Premature

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Fast body part segmentation and tracking of neonatal video data using deep learning.

Medical & biological engineering & computing Oct 23, 2020
Photoplethysmography imaging (PPGI) for non-contact monitoring of preterm infants in the neonatal intensive care unit (NICU) is a promising technology, as it could reduce medical adhesive-related skin injuries and associated complications. For practi...
Humans Photoplethysmography Video Recording Infant, Premature Human Body Infant, Newborn Infant Deep Learning Image Processing, Computer-Assisted
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A multi-task, multi-stage deep transfer learning model for early prediction of neurodevelopment in very preterm infants.

Scientific reports Sep 15, 2020
Survivors following very premature birth (i.e., ≤ 32 weeks gestational age) remain at high risk for neurodevelopmental impairments. Recent advances in deep learning techniques have made it possible to aid the early diagnosis and prognosis of neurodev...
Developmental Disabilities Infant, Premature, Diseases Neurodevelopmental Disorders Middle Aged Child Models, Neurological Infant, Premature Humans Machine Learning Databases, Factual Brain Female Infant, Newborn Adult Male
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Automated detection of early-stage ROP using a deep convolutional neural network.

The British journal of ophthalmology Aug 23, 2020
BACKGROUND/AIM: To automatically detect and classify the early stages of retinopathy of prematurity (ROP) using a deep convolutional neural network (CNN).
Diagnosis, Computer-Assisted ROC Curve Image Processing, Computer-Assisted Birth Weight Infant, Newborn Sensitivity and Specificity Cross-Sectional Studies Male Infant, Premature Infant, Low Birth Weight Gestational Age Retinopathy of Prematurity Humans Neural Networks, Computer Algorithms Retrospective Studies Female
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Reliability and accuracy of EEG interpretation for estimating age in preterm infants.

Annals of clinical and translational neurology Aug 7, 2020
OBJECTIVES: To determine the accuracy of, and agreement among, EEG and aEEG readers' estimation of maturity and a novel computational measure of functional brain age (FBA) in preterm infants.
Humans Machine Learning Brain Infant, Newborn Brain Diseases Infant, Premature Gestational Age Neonatology Reproducibility of Results Diagnosis, Computer-Assisted Predictive Value of Tests Electroencephalography
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Feed-forward neural networks using cerebral MR spectroscopy and DTI might predict neurodevelopmental outcome in preterm neonates.

European radiology Jul 18, 2020
OBJECTIVES: We aimed to evaluate the ability of feed-forward neural networks (fNNs) to predict the neurodevelopmental outcome (NDO) of very preterm neonates (VPIs) at 12 months corrected age by using biomarkers of cerebral MR proton spectroscopy (H-M...
Humans Neural Networks, Computer Prospective Studies Brain Prognosis Female Magnetic Resonance Spectroscopy Diffusion Tensor Imaging Infant, Premature, Diseases Predictive Value of Tests Image Interpretation, Computer-Assisted Infant, Premature Developmental Disabilities Infant, Newborn Sensitivity and Specificity Male Infant
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Integration of an interpretable machine learning algorithm to identify early life risk factors of childhood obesity among preterm infants: a prospective birth cohort.

BMC medicine Jul 10, 2020
BACKGROUND: The early life risk factors of childhood obesity among preterm infants are unclear and little is known about the influence of the feeding practices. We aimed to identify early life risk factors for childhood overweight/obesity among prete...
Child Pediatric Obesity Infant, Premature Infant China Cohort Studies Child, Preschool Infant, Newborn Risk Factors Male Humans Machine Learning Algorithms Prospective Studies Female
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Machine Learning to Support Hemodynamic Intervention in the Neonatal Intensive Care Unit.

Clinics in perinatology May 20, 2020
Hemodynamic support in neonatal intensive care is directed at maintaining cardiovascular wellbeing. At present, monitoring of vital signs plays an essential role in augmenting care in a reactive manner. By applying machine learning techniques, a mode...
Intensive Care Units, Neonatal Cardiovascular Physiological Phenomena Diagnostic Techniques, Cardiovascular Homeostasis Cerebrovascular Circulation Hemodynamic Monitoring Cardiovascular Diseases Shock, Septic Infant, Premature Neonatal Sepsis Humans Machine Learning Infant, Newborn
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Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model.

NeuroImage Apr 9, 2020
BACKGROUND AND AIMS: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 ...
Brain Infant Deep Learning Longitudinal Studies Humans Neural Networks, Computer Prospective Studies Neurodevelopmental Disorders Models, Neurological Infant, Premature Motor Disorders Diffusion Magnetic Resonance Imaging
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A convolutional neural network outperforming state-of-the-art sleep staging algorithms for both preterm and term infants.

Journal of neural engineering Jan 14, 2020
OBJECTIVE: To classify sleep states using electroencephalogram (EEG) that reliably works over a wide range of preterm ages, as well as term age.
Sleep Stages Algorithms Databases, Factual Brain Infant, Newborn Electroencephalography Markov Chains Infant, Premature Normal Distribution Humans Neural Networks, Computer
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Preterm Infants' Pose Estimation With Spatio-Temporal Features.

IEEE transactions on bio-medical engineering Dec 23, 2019
OBJECTIVE: Preterm infants' limb monitoring in neonatal intensive care units (NICUs) is of primary importance for assessing infants' health status and motor/cognitive development. Herein, we propose a new approach to preterm infants' limb pose estima...
Reproducibility of Results Infant, Premature Humans Neural Networks, Computer Infant, Newborn
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