# Deep Learning Applications for Dyslexia Prediction
###### Abstract  
Dyslexia is a neurological problem that leads to obstacles and difficulties in the learning process, especially in reading. Generally, people with dyslexia suffer from weak reading, writing, spelling, and fluency abilities. However, these difficulties are not related to their intelligence. An early diagnosis of this disorder will help dyslexic children improve their abilities using appropriate tools and specialized software. Machine learning and deep learning methods have been implemented to recognize dyslexia with various datasets related to dyslexia acquired from medical and educational organizations. This review paper analyzed the prediction performance of deep learning models for dyslexia and summarizes the challenges researchers face when they use deep learning models for classification and diagnosis. Using the PRISMA protocol, 19 articles were reviewed and analyzed, with a focus on data acquisition, preprocessing, feature extraction, and the prediction model performance. The purpose of this review was to aid researchers in building a predictive model for dyslexia based on available dyslexia-related datasets. The paper demonstrated some challenges that researchers encounter in this field and must overcome. dyslexia detection; dyslexia classification; feature extraction; diagnosing dyslexia; machine learning; deep learning +
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resources and technological tools utilized to create, transfer, store, interchange, or share information. All these tests are available for subscription under specific criteria. Neurological aspects have also been considered in modern technologies for diagnosing dyslexia, helping to increase detection accuracy and reliability. Medical devices enable the observation of the brain structure of dyslexics [6]. [PERSON] [7] proposed three triggering mechanisms of dyslexia: attentiveness disturbance (visual-attentional dyslexia), language disturbance (phonological dyslexia), and motor disturbance (a dyspraxic form of dyslexia). In \"phonological dyslexia\", during the reading, fMRI displays paralysis of three regions that are concerned with language production and grasping ([PERSON]'s, [PERSON]'s, and [PERSON]'s territory) [8]. Additionally, compared to controls, DTI reveals white matter variation in the language area [9]. In visual attentional dyslexia, when dyslexic participants are tasked to recognize congruous stimuli pairs, fMRI displays a separation between the temporal visual system and parietal attentional system as well as a disconnection, in the left hemisphere, of the temporal and occipital zones [9]. In the \"dyspraxic form of dyslexia\", there is inactivation in the cerebellum-ventral frontotemporal and cerebellum-dorsal frontoparietal pathway [10]. Besides fMRI, the surface measurement of brain potential, known as EEG, assists in identifying brain activation patterns. During spelling tests, phoneme deletion, the rapid naming of letters, and articulation, increased vigor is observed in the theta and delta EEG frequency bands in the frontal and right temporal zones in dyslexics [11]. Researchers have suggested different ML methods for predicting dyslexia in children utilizing datasets related to dyslexics. Such datasets can be acquired from medical and educational institutions as well as through special games constructed specially for them. For example, eye tracking, brain imaging, EEG, test scores, and handwriting have been used in dyslexia prediction. AI refers to the development of machines and systems to enable them to implement functions and tasks that demand human intelligence, such as translation, decision making, visual perception, and speech recognition. ML is considered a part of AI, which concentrates on the evolution of computer programs that use different datasets to learn for themselves. DL is a subset of ML that seeks to simulate the human brain, allowing DL systems to cluster data and make incredibly accurate predictions.