AIMC Topic: Child

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State of the art and challenges for the classification of studies on electromechanical and robotic devices in neurorehabilitation: a scoping review.

European journal of physical and rehabilitation medicine
INTRODUCTION: The rapid development of electromechanical and robotic devices has profoundly influenced neurorehabilitation. Growth in the scientific and technological aspects thereof is crucial for increasing the number of newly developed devices, an...

Geometric deep learning on brain shape predicts sex and age.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The complex relationship between the shape and function of the human brain remains elusive despite extensive studies of cortical folding over many decades. The analysis of cortical gyrification presents an opportunity to advance our knowledge about t...

Robot-Assisted Autism Therapy (RAAT). Criteria and Types of Experiments Using Anthropomorphic and Zoomorphic Robots. Review of the Research.

Sensors (Basel, Switzerland)
Supporting the development of a child with autism is a multi-profile therapeutic work on disturbed areas, especially understanding and linguistic expression used in social communication and development of social contacts. Previous studies show that i...

Predicting youth diabetes risk using NHANES data and machine learning.

Scientific reports
Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step ...

Evaluating atypical language in autism using automated language measures.

Scientific reports
Measurement of language atypicalities in Autism Spectrum Disorder (ASD) is cumbersome and costly. Better language outcome measures are needed. Using language transcripts, we generated Automated Language Measures (ALMs) and tested their validity. 169 ...

Using deep learning convolutional neural networks to automatically perform cerebral aqueduct CSF flow analysis.

Journal of clinical neuroscience : official journal of the Neurosurgical Society of Australasia
Since the development of phase-contrast magnetic resonance imaging (PC-MRI), quantification of cerebrospinal fluid (CSF) flow across the cerebral aqueduct has been utilized for diagnosis of conditions such as normal pressure hydrocephalus (NPH). This...

Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.

The American journal of pathology
Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA ...

Analysing wideband absorbance immittance in normal and ears with otitis media with effusion using machine learning.

Scientific reports
Wideband Absorbance Immittance (WAI) has been available for more than a decade, however its clinical use still faces the challenges of limited understanding and poor interpretation of WAI results. This study aimed to develop Machine Learning (ML) too...

Probing an AI regression model for hand bone age determination using gradient-based saliency mapping.

Scientific reports
Understanding how a neural network makes decisions holds significant value for users. For this reason, gradient-based saliency mapping was tested on an artificial intelligence (AI) regression model for determining hand bone age from X-ray radiographs...