AIMC Topic: Infant, Newborn

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Cardio-respiratory signal extraction from video camera data for continuous non-contact vital sign monitoring using deep learning.

Physiological measurement
UNLABELLED: Non-contact vital sign monitoring enables the estimation of vital signs, such as heart rate, respiratory rate and oxygen saturation (SpO), by measuring subtle color changes on the skin surface using a video camera. For patients in a hospi...

A novel hybrid atlas-free hierarchical graph-based segmentation of newborn brain MRI using wavelet filter banks.

The International journal of neuroscience
: The newborn brain MRI (magnetic resonance imaging) tissue segmentation plays a vital part in assessment of primary brain growth. In the newborn stage (nearly less than 28 days old), in T1- as well as T2-weighted MR images similar levels of intensit...

Neonatal seizure detection from raw multi-channel EEG using a fully convolutional architecture.

Neural networks : the official journal of the International Neural Network Society
A deep learning classifier for detecting seizures in neonates is proposed. This architecture is designed to detect seizure events from raw electroencephalogram (EEG) signals as opposed to the state-of-the-art hand engineered feature-based representat...

Prospective validation of a machine learning model that uses provider notes to identify candidates for resective epilepsy surgery.

Epilepsia
OBJECTIVE: Delay to resective epilepsy surgery results in avoidable disease burden and increased risk of mortality. The objective was to prospectively validate a natural language processing (NLP) application that uses provider notes to assign epileps...

[Hemoglobinuria in children hospitalized in Ouagadougou: short term inpatient care and prognosis].

The Pan African medical journal
INTRODUCTION: The purpose of this study was to analyze the epidemiological, diagnostic, therapeutic and evolutionary features of hemoglobinuria in children hospitalized in the Pediatric University Hospital Charles de Gaulle, Ouagadougou.

Mining multi-site clinical data to develop machine learning MRI biomarkers: application to neonatal hypoxic ischemic encephalopathy.

Journal of translational medicine
BACKGROUND: Secondary and retrospective use of hospital-hosted clinical data provides a time- and cost-efficient alternative to prospective clinical trials for biomarker development. This study aims to create a retrospective clinical dataset of Magne...

New grading criterion for retinal haemorrhages in term newborns based on deep convolutional neural networks.

Clinical & experimental ophthalmology
BACKGROUND: To define a new quantitative grading criterion for retinal haemorrhages in term newborns based on the segmentation results of a deep convolutional neural network.

Dysmorphology in a Genomic Era.

Clinics in perinatology
Dysmorphology is the practice of defining the morphologic phenotype of syndromic disorders. Genomic sequencing has advanced our understanding of human variation and molecular dysmorphology has evolved in response to the science of relating embryologi...

Deep learning predicts extreme preterm birth from electronic health records.

Journal of biomedical informatics
OBJECTIVE: Models for predicting preterm birth generally have focused on very preterm (28-32 weeks) and moderate to late preterm (32-37 weeks) settings. However, extreme preterm birth (EPB), before the 28th week of gestational age, accounts for the m...

Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis.

Medical hypotheses
The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonates with and without seizures. The study employed 10 different FS algorithms to reduce the classifica...