AIMC Topic: Child

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HypernasalityNet: Deep recurrent neural network for automatic hypernasality detection.

International journal of medical informatics
BACKGROUND: Cleft palate patients have inability to produce adequate velopharyngeal closure, which results in hypernasal speech. In clinic, hypernasal speech is assessed through subject assessment by speech language pathologists. Automatic hypernasal...

Machine Learning Prediction of Liver Stiffness Using Clinical and T2-Weighted MRI Radiomic Data.

AJR. American journal of roentgenology
The purpose of this study is to develop a machine learning model to categorically classify MR elastography (MRE)-derived liver stiffness using clinical and nonelastographic MRI radiomic features in pediatric and young adult patients with known or su...

Deep residual network for off-resonance artifact correction with application to pediatric body MRA with 3D cones.

Magnetic resonance in medicine
PURPOSE: To enable rapid imaging with a scan time-efficient 3D cones trajectory with a deep-learning off-resonance artifact correction technique.

Cerebral microbleed detection using Susceptibility Weighted Imaging and deep learning.

NeuroImage
Detecting cerebral microbleeds (CMBs) is important in diagnosing a variety of diseases including dementia, stroke and traumatic brain injury. However, manual detection of CMBs can be time-consuming and prone to errors, whereas the current automatic a...

nCREANN: Nonlinear Causal Relationship Estimation by Artificial Neural Network; Applied for Autism Connectivity Study.

IEEE transactions on medical imaging
Quantifying causal (effective) interactions between different brain regions are very important in neuroscience research. Many conventional methods estimate effective connectivity based on linear models. However, using linear connectivity models may o...

Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning.

International journal of medical informatics
BACKGROUND AND OBJECTIVE: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, resear...

Psychosocial Health Interventions by Social Robots: Systematic Review of Randomized Controlled Trials.

Journal of medical Internet research
BACKGROUND: Social robots that can communicate and interact with people offer exciting opportunities for improved health care access and outcomes. However, evidence from randomized controlled trials (RCTs) on health or well-being outcomes has not yet...

Statistical Approaches Based on Deep Learning Regression for Verification of Normality of Blood Pressure Estimates.

Sensors (Basel, Switzerland)
Oscillometric blood pressure (BP) monitors currently estimate a single point but do not identify variations in response to physiological characteristics. In this paper, to analyze BP's normality based on oscillometric measurements, we use statistical...

Testing a deep convolutional neural network for automated hippocampus segmentation in a longitudinal sample of healthy participants.

NeuroImage
Subtle changes in hippocampal volumes may occur during both physiological and pathophysiological processes in the human brain. Assessing hippocampal volumes manually is a time-consuming procedure, however, creating a need for automated segmentation m...

Deep learning to automate Brasfield chest radiographic scoring for cystic fibrosis.

Journal of cystic fibrosis : official journal of the European Cystic Fibrosis Society
BACKGROUND: The aim of this study was to evaluate the hypothesis that a deep convolutional neural network (DCNN) model could facilitate automated Brasfield scoring of chest radiographs (CXRs) for patients with cystic fibrosis (CF), performing similar...