MEG Detection of Attention and Memory Processes in Individuals with Dyslexia

Bowyer, Susan M.; Pawluk, Lesley; Olszewski, Amy K.; Gallaway, M. L.; Mansour, Alfred; Jacobson, Daniel; Erdődi, László A.; Lewandowski-Powley, P.; Moran, John; Tepley, N.; Lajiness-O’Neill, Renée

doi:10.1007/978-3-642-12197-5_81

Cited by 2 publications

(1 citation statement)

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“…as functional magnetic resonance imaging (fMRI) and magnetoencephalography. Genetic testing is one of the tools employed to establish that the probability of dyslexia is increased by certain genetic markers (Smith et al, 1991;Beneventi et al, 2010;Bowyer et al, 2010). Additionally, the identification of reading patterns and eye movements is facilitated by the use of computer-based assessments and eye-tracking technologies, thus pinpointing specific difficulties.…”

Section: Journal Of Disability Research 2024mentioning

confidence: 99%

Deep Learning for Dyslexia Detection: A Comprehensive CNN Approach with Handwriting Analysis and Benchmark Comparisons

Aldehim,

Rashid,

Alluhaidan

et al. 2024

Journal of Disability Research

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Dyslexia is a complex learning disorder that affects neurological nerves in the brain and makes reading and writing difficult; therefore, early diagnosis for effective interventions becomes important. This study demonstrates how quickly dyslexia can be identified by introducing an advanced convolutional neural network model developed for detecting dyslexia through image-based handwriting analysis. The need for early identification is informed by the fact that dyslexia may, in certain cases, lead to poor academic performance and emotional imbalance among learners. This method of using deep learning outperforms all other established conventional methods due to inherent sensitivity in classifying handwritings of dyslexics from those of normal individuals. The artificial intelligence (AI)-supported technology has the highest training accuracy of 99.5% proving its ability to capture subtle features related to the presence of dyslexic tendencies. Consequently, it records a testing accuracy of 96.4%, thereby confirming its efficacy under practical circumstances. In addition, the model also shows a good F1-score of 96 which indicates that it can achieve a balanced precision versus recall trade-off unlike other state-of-the-art approaches. The obtained results of the proposed methodology were compared with those of previous state–of-the-art approaches, and it has been observed that the proposed study provides better outcomes. These detailed performance indicators point toward the potential usefulness of AI-based methods in identifying dyslexia thus informing appropriate interventions on time and targeted assistance to the patients suffering from this disease.

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Section: Journal Of Disability Research 2024mentioning

confidence: 99%