AIMC Topic: Infant, Newborn

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Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.

PloS one
BACKGROUND: Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentation. Our objective was to develop a model using readily available electronic health record (EHR) data...

Pilot Testing a Robot for Reducing Pain in Hospitalized Preterm Infants.

OTJR : occupation, participation and health
Optimizing neurodevelopment is a key goal of neonatal occupational therapy. In preterm infants, repeated procedural pain is associated with adverse effects on neurodevelopment long term. Calmer is a robot designed to reduce infant pain. The objective...

Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence.

Nature medicine
Artificial intelligence (AI)-based methods have emerged as powerful tools to transform medical care. Although machine learning classifiers (MLCs) have already demonstrated strong performance in image-based diagnoses, analysis of diverse and massive e...

Automated Fundus Image Quality Assessment in Retinopathy of Prematurity Using Deep Convolutional Neural Networks.

Ophthalmology. Retina
PURPOSE: Accurate image-based ophthalmic diagnosis relies on fundus image clarity. This has important implications for the quality of ophthalmic diagnoses and for emerging methods such as telemedicine and computer-based image analysis. The purpose of...

Association between prenatal exposure to multiple persistent organic pollutants (POPs) and growth indicators in newborns.

Environmental research
Despite the fact that many of persistent organic pollutants (POPs) have been banned for decades, they still constitute a group of harmful substances to human health. Prenatal exposure can have adverse effects on one's health as well as on their newbo...

A machine learning approach to investigate potential risk factors for gastroschisis in California.

Birth defects research
BACKGROUND: To generate new leads about risk factors for gastroschisis, a birth defect that has been increasing in prevalence over time, we performed an untargeted data mining statistical approach.

Application of a deep convolutional neural network in the diagnosis of neonatal ocular fundus hemorrhage.

Bioscience reports
There is a disparity between the increasing application of digital retinal imaging to neonatal ocular screening and slowly growing number of pediatric ophthalmologists. Assistant tools that can automatically detect ocular disorders may be needed. In ...

Deep Learning for Image Quality Assessment of Fundus Images in Retinopathy of Prematurity.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Accurate image-based medical diagnosis relies upon adequate image quality and clarity. This has important implications for clinical diagnosis, and for emerging methods such as telemedicine and computer-based image analysis. In this study, we trained ...

Supervised machine learning quality control for magnetic resonance artifacts in neonatal data sets.

Human brain mapping
Quality control (QC) of brain magnetic resonance images (MRI) is an important process requiring a significant amount of manual inspection. Major artifacts, such as severe subject motion, are easy to identify to naïve observers but lack automated iden...