## 1 Introduction

Table 1: _Cont._related to psycho-tests and the measurement of the attention of the subject will allow for the establishment of individualized therapeutic procedures for each patient, minimizing adverse effects. The proposed hybrid technology for conducting psycho-tests, allowing for result assessment through the use of observer attention, represents an original and innovative approach, adding value to this study. In this article, we propose an innovative approach to diagnosing dyslexia by integrating the Benton Visual Retention Test (BVRT) with advanced analysis of visual attention trajectories using eye-tracking technology and deep neural networks (DNN). This approach not only enhances the effectiveness of dyslexia diagnosis but also makes it more accessible and efficient, especially for children who are not yet able to read. The Benton test is a widely recognized tool for assessing visual memory and perception. Integrating this test with eye-tracking technology allows for detailed analysis of a child's eye movement patterns while performing tasks. Recording the spatiotemporal trajectories of visual attention using mobile devices like Pupil Invisible or Pupil Core provides objective and precise data. The recorded spatiotemporal data, extracted from sessions, are analyzed by an LSTM network, enabling the detection of subtle visual anomalies characteristic of dyslexia. One of the main advantages of the proposed approach is its ability to diagnose dyslexia in children who have not yet mastered reading skills. Traditional diagnostic methods often rely on reading tests, which can be a barrier for younger children or those with severe reading difficulties. The method based on visual perception and eye movement analysis bypasses this obstacle, allowing for the early detection of dyslexia. The proposed solution offers several key advantages over traditional dyslexia diagnostic methods:  
* Objectivity and Precision: Utilizing eye tracking for the accurate and objective collection of data related to eye movements and subjecting the gathered data to analysis using LSTM networks allows for the identification of subtle eye movement patterns characteristic of dyslexia. The high correlation coefficient R (\(\sim\)0.992) achieved in the proposed model indicates its high accuracy and reliability. * Speed and Efficiency: Traditional diagnostic methods can be time-consuming and require multiple sessions with the child. The proposed approach allows for rapid data collection and immediate analysis of results, significantly reducing the time needed for diagnosis. * Stress-Free Environment: The BVRT, which does not require reading skills, is less stressful and more natural for younger children. Eye tracking allows for administering the test in a friendly and engaging manner, which can lead to more reliable results. * Early Intervention: The ability to diagnose dyslexia in children who are not yet able to read enables the early implementation of appropriate educational and therapeutic interventions. Early recognition of dyslexia-related issues allows for the prompt introduction of effective support strategies, which can significantly improve the child's educational and emotional outcomes. A key added value of this work is the combination of the Benton Visual Retention Test with eye-tracking technology and LSTM deep neural networks, presenting a novel approach to diagnosing dyslexia. The ease of implementing this approach in school settings and its stress-free nature make it suitable for use with children of various ages and developmental stages, including those who have not yet learned to read. This enables early intervention, which is crucial for effective educational and emotional support for children with dyslexia.