AI Medical Compendium Topic:
Child, Preschool

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Performance evaluation of DWT based sigmoid entropy in time and frequency domains for automated detection of epileptic seizures using SVM classifier.

Computers in biology and medicine
The electroencephalogram (EEG) signal contains useful information on physiological states of the brain and has proven to be a potential biomarker to realize the complex dynamic behavior of the brain. Epilepsy is a brain disorder described by recurren...

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...

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...

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...

Identifying Children With Clinical Language Disorder: An Application of Machine-Learning Classification.

Journal of learning disabilities
In this study, we identified child- and family-level characteristics most strongly associated with clinical identification of language disorder for preschool-aged children. We used machine learning to identify variables that best classified children ...

Precise diagnosis of intracranial hemorrhage and subtypes using a three-dimensional joint convolutional and recurrent neural network.

European radiology
OBJECTIVES: To evaluate the performance of a novel three-dimensional (3D) joint convolutional and recurrent neural network (CNN-RNN) for the detection of intracranial hemorrhage (ICH) and its five subtypes (cerebral parenchymal, intraventricular, sub...

Automated semantic labeling of pediatric musculoskeletal radiographs using deep learning.

Pediatric radiology
BACKGROUND: An automated method for identifying the anatomical region of an image independent of metadata labels could improve radiologist workflow (e.g., automated hanging protocols) and help facilitate the automated curation of large medical imagin...

Incorporated region detection and classification using deep convolutional networks for bone age assessment.

Artificial intelligence in medicine
Bone age assessment plays an important role in the endocrinology and genetic investigation of patients. In this paper, we proposed a deep learning-based approach for bone age assessment by integration of the Tanner-Whitehouse (TW3) methods and deep c...

Giving Voice to Vulnerable Children: Machine Learning Analysis of Speech Detects Anxiety and Depression in Early Childhood.

IEEE journal of biomedical and health informatics
Childhood anxiety and depression often go undiagnosed. If left untreated these conditions, collectively known as internalizing disorders, are associated with long-term negative outcomes including substance abuse and increased risk for suicide. This p...