Understanding dyslexia and the potential of artificial intelligence in detecting neurocognitive impairment in dyslexia - Siti Atiyah, Ali; Humaira, Nisar; Nurfaizatul Aisyah, Ab Aziz; Nor Asyikin, Fadzil; Nur Saida, Mohamad Zaber; Luthffi Idzhar, Ismail (Academic Press, 2025)
Dyslexia is a specific learning disorder that affects reading and writing abilities. Children with dyslexia are typically diagnosed during their primary school years, typically between the ages of 5 and 8, when their academic performance lags behind their peers. However, the diagnostic process can be lengthy, and due to the diverse range of characteristics exhibited by individuals with dyslexia, misdiagnosis as other learning disabilities is not uncommon. This delay in diagnosis can result in delayed intervention, further exacerbating their learning challenges. This chapter aims to provide an understanding of the clinical procedures involved in diagnosing dyslexia alongside current interventions, followed by a discussion of electrophysiological processing differences between children with dyslexia and typically developing children. This involves identifying significant abnormalities in neurocognitive processing activity in brain signals provided by electroencephalography (EEG) during the resting state and event-related potential (ERP) during different task stimulations. Taking significant abnormalities existing between dyslexia and healthy children into account, the current technology of artificial intelligence and machine learning as tools for diagnosing and intervening in dyslexia using multimodel of brain signals is considered beneficial to enable the development of methods for early diagnosis and tailored interventions for children with dyslexia as young as possible.