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Dyslexia

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An Efficient Machine Learning-Based Feature Optimization Model for the Detection of Dyslexia.

Computational intelligence and neuroscience
Dyslexia is among the most common neurological disorders in children. Detection of dyslexia therefore remains an important pursuit for the research works across various domains which is illustrated by the plethora of work presented in diverse scienti...

Clustering analysis of factors affecting academic career of university students with dyslexia in Italy.

Scientific reports
This study was designed to explore learning experiences of university students with dyslexia and factors that could contribute to their success in the university career. Although, great efforts have been made to diagnose dyslexia and to mitigate its ...

A novel approach for detection of dyslexia using convolutional neural network with EOG signals.

Medical & biological engineering & computing
Dyslexia is a learning disability in acquiring reading skills, even though the individual has the appropriate learning opportunity, adequate education, and appropriate sociocultural environment. Dyslexia negatively affects children's educational deve...

Deep learning classification of reading disability with regional brain volume features.

NeuroImage
Developmental reading disability is a prevalent and often enduring problem with varied mechanisms that contribute to its phenotypic heterogeneity. This mechanistic and phenotypic variation, as well as relatively modest sample sizes, may have limited ...

An abbreviated Chinese dyslexia screening behavior checklist for primary school students using a machine learning approach.

Behavior research methods
To increase early identification and intervention of dyslexia, a prescreening instrument is critical to identifying children at risk. The present work sought to shorten and validate the 30-item Mandarin Dyslexia Screening Behavior Checklist for Prima...

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis.

Interdisciplinary sciences, computational life sciences
The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integration and coordination of neuronal oscillations across multiple spatiotemporal scales. We have proposed ...

Distinct connectivity patterns between perception and attention-related brain networks characterize dyslexia: Machine learning applied to resting-state fMRI.

Cortex; a journal devoted to the study of the nervous system and behavior
Diagnosis of dyslexia often occurs in late schooling years, leading to academic and psychological challenges. Furthermore, diagnosis is time-consuming, costly, and reliant on arbitrary cutoffs. On the other hand, automated algorithms hold great poten...

INSIGHT: Combining Fixation Visualisations and Residual Neural Networks for Dyslexia Classification From Eye-Tracking Data.

Dyslexia (Chichester, England)
Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge...

Exploring and Identifying Key Factors in Predicting Dyslexia in Children: Advanced Machine Learning Algorithms From Screening to Diagnosis.

Clinical psychology & psychotherapy
INTRODUCTION: The current study aimed to develop and validate a machine learning (ML)-based predictive models for early dyslexia detection in children by integrating neurocognitive, linguistic and behavioural predictors.