## 5 Conclusions  
This study presented significant findings on using deep LSTM networks for dyslexia recognition by analyzing time series data depicting the attention trajectories of individual participants. Among various eye trackers, including Tobii, SMI, and Pupil, the team effectively utilized the Pupil Invisible and Pupil Core models, which facilitated seamless research in school environments.  
Based on the conducted experiments, the following conclusions can be drawn:  
* The Benton Test, employed to establish the expected values of the training set, proved to be an effective tool for use in intelligent systems aimed at recognizing developmental disorders in early school-age children.
* For dyslexia detection studies in children, non-invasive eye trackers that minimally disrupt the child's attention during measurements are recommended.
* Spatiotemporal measurements of attention trajectories can be effectively utilized to identify anomalies indicative of dyslexia risk.
* A high level of dispersion in attention trajectories correlates with high accuracy in task reproduction during BVRT tests, suggesting a lower risk of dyslexia.
* As with other systems that rely on expert knowledge, the arbitrary assessment method of BVRT test results for constructing a learning sequence is a limitation. It is advisable to involve a larger number of experts for independent result assessments to enhance reliability.
* The definite ease of implementation in a school setting (preferring a Pupil Invisible stand) of the proposed method is a significant advantage and superiority over methods using fMRI measurements [7].
* Analyzing attention trajectories using LSTM networks offers a robust alternative to methods utilizing CNNs for static graphical analysis of the BVRT test forms, as it accounts for the temporal and spatial strategies employed by humans in scene recognition.  
Based on the observations and conclusions from the conducted research, several promising directions for further studies on dyslexia recognition can be identified. In particular, it is anticipated that combining LSTM and CNN networks in future research will enhance the accuracy and confidence in dyslexia recognition results for early school-age children. Exploring the creation of a hybrid model that combines various methods of analyzing perception and scene recognition, as well as psychomotor reactions during performing specific tasks, will be a focus for the authors' future work.  
**Author Contributions:** Conceptualization, Z.G.; methodology, Z.G., E.Z. and B.C.; software, Z.G. and E.Z.; validation, Z.G., E.Z., B.C. and Y.K.; formal analysis, Z.G., E.Z., B.C. and Y.K.; investigation, Z.G. and E.Z.; resources, Z.G., E.Z. and B.C.; data curation, Z.G. and E.Z.; writing original draft preparation, Z.G. and E.Z.; writing--review and editing, Z.G. and E.Z.; visualization, Z.G. and E.Z.; supervision, Z.G.; project administration, Z.G.; funding acquisition, Z.G. and E.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding  
**Institutional Review Board Statement:** The study was approved by the Institutional Review Ethic Board of the FGKICPO, and the ethical approval was granted on 9 November 2022, under the reference number IRB-20221109. Informed consent was obtained from all subjects involved in the study and written informed consent has been obtained from the patients to publish this paper.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study. Written informed consent has been obtained from the patients to publish this paper.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.