AIMC Topic: Infant, Newborn

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A Demonstration of Machine Learning in Detecting Frequency Following Responses in American Neonates.

Perceptual and motor skills Sep 22, 2020
In this study, we sought to evaluate the efficiencies of multiple machine learning algorithms in detecting neonates' Frequency Following Responses (FFRs). We recorded continuous brainwaves from 43 American neonates in response to a pre-recorded monos...
United States Humans Machine Learning Infant, Newborn
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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...
Infant, Premature Brain Male Developmental Disabilities Female Humans Middle Aged Infant, Newborn Infant, Premature, Diseases Machine Learning Child Adult Databases, Factual Neurodevelopmental Disorders Models, Neurological
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Deep learning for neonatal seizure detection: a friend rather than foe.

The Lancet. Child & adolescent health Aug 27, 2020
Infant, Newborn Deep Learning Seizures Friends Humans Machine Learning Algorithms
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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).
Birth Weight Infant, Premature Infant, Low Birth Weight Gestational Age Male Diagnosis, Computer-Assisted ROC Curve Image Processing, Computer-Assisted Retinopathy of Prematurity Humans Neural Networks, Computer Algorithms Retrospective Studies Female Infant, Newborn Sensitivity and Specificity Cross-Sectional Studies
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Deep learning protocol for improved photoacoustic brain imaging.

Journal of biophotonics Aug 17, 2020
One of the key limitations for the clinical translation of photoacoustic imaging is penetration depth that is linked to the tissue maximum permissible exposures (MPE) recommended by the American National Standards Institute (ANSI). Here, we propose a...
Brain Infant, Newborn Animals Deep Learning Sheep Humans Spectrum Analysis Neuroimaging Photoacoustic Techniques
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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.
Infant, Newborn Reproducibility of Results Diagnosis, Computer-Assisted Predictive Value of Tests Humans Machine Learning Brain Gestational Age Electroencephalography Neonatology Brain Diseases Infant, Premature
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Accuracy of Trained Physicians is Inferior to Deep Learning-Based Algorithm for Determining Angles in Ultrasound of the Newborn Hip.

Ultraschall in der Medizin (Stuttgart, Germany : 1980) Aug 6, 2020
PURPOSE:  Sonographic diagnosis of developmental dysplasia of the hip allows treatment with a flexion-abduction orthosis preventing hip luxation. Accurate determination of alpha and beta angles according to Graf is crucial for correct diagnosis. It i...
Infant, Newborn Deep Learning Ultrasonography Automation Hip Dislocation, Congenital Humans Physicians
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Using deep learning to predict the hand-foot-and-mouth disease of enterovirus A71 subtype in Beijing from 2011 to 2018.

Scientific reports Jul 22, 2020
Hand-foot-and-month disease (HFMD), especially the enterovirus A71 (EV-A71) subtype, is a major health problem in Beijing, China. Previous studies mainly used regressive models to forecast the prevalence of HFMD, ignoring its intrinsic age groups. Th...
Deep Learning Infant, Newborn Seasons Humans Animals Beijing Child, Preschool Databases, Factual Infant Hand, Foot and Mouth Disease Child Female Disease Outbreaks Models, Theoretical Enterovirus A, Human
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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...
Prognosis Infant, Premature, Diseases Female Infant, Premature Male Humans Infant, Newborn Developmental Disabilities Infant Neural Networks, Computer Sensitivity and Specificity Magnetic Resonance Spectroscopy Predictive Value of Tests Prospective Studies Diffusion Tensor Imaging Image Interpretation, Computer-Assisted Brain
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Identifying scenarios of benefit or harm from kidney transplantation during the COVID-19 pandemic: A stochastic simulation and machine learning study.

American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons Jul 15, 2020
Clinical decision-making in kidney transplant (KT) during the coronavirus disease 2019 (COVID-19) pandemic is understandably a conundrum: both candidates and recipients may face increased acquisition risks and case fatality rates (CFRs). Given our po...
Infant, Newborn SARS-CoV-2 Aged Young Adult Adult Pandemics Male United States Adolescent Middle Aged COVID-19 Humans Kidney Failure, Chronic Child, Preschool Infant Machine Learning Waiting Lists Kidney Transplantation Child Female Tissue Donors
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