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La dislexia es un trastorno del aprendizaje de origen neurológico que afecta significativamente a las habilidades de lectura y escritura de los niños, a pesar de que su inteligencia sea normal, y su identificación tardía puede acarrear consecuencias académicas y psicosociales a largo plazo. Los métodos actuales de detección de la dislexia se basan en gran medida en evaluaciones realizadas por expertos que requieren mucho tiempo, son subjetivas y difíciles de aplicar a gran escala en entornos no clínicos. Aunque estudios recientes han explorado enfoques basados en la inteligencia artificial (IA) para la detección de la dislexia, muchos siguen limitándose a datos de una sola modalidad, análisis sin conexión o implementaciones no móviles, lo que restringe su aplicabilidad práctica para la detección precoz. El objetivo de este estudio fue desarrollar una aplicación móvil basada en IA para la detección precoz de la dislexia, aprovechando datos secuenciales de texto y voz mediante una arquitectura de red neuronal recurrente (RNN), concretamente la unidad recurrente con compuerta (GRU). Se empleó una metodología de investigación y desarrollo (I+D) que abarcó el análisis de requisitos, el diseño del sistema, el entrenamiento del modelo GRU, el desarrollo de la aplicación móvil con Flutter y la integración del sistema con un backend RESTful y una base de datos MySQL. El modelo GRU se entrenó con texto de lectura preprocesado y grabaciones de voz para captar los patrones temporales asociados a los comportamientos de lectura relacionados con la dislexia. Los resultados experimentales indican que el modelo propuesto alcanzó un rendimiento de clasificación fiable en la identificación de patrones relacionados con la dislexia, mientras que la aplicación móvil proporcionó con éxito resultados de detección en tiempo real y mantuvo registros de evaluación longitudinales. Los resultados demuestran que la integración de modelos ligeros de aprendizaje profundo secuencial en plataformas móviles ofrece una solución escalable y accesible para la detección precoz de la dislexia, lo que permite su uso independiente por parte de padres y educadores fuera de entornos clínicos.
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Declaraciones recogidas en el artículo publicado
Author Contributions
Muhamad Fathur Rahman: Conceptualization, Methodology, Software, Data Curation, Investigation, Writing – Original Draft, Writing – Review & Editing. Resmi Darni: Supervision, Resources, Validation, Writing – Review & Editing. Dony Novaliendry: Validation, Writing – Review & Editing. Khairi Budayawan: Supervision, Validation, Writing – Review & Editing. All authors have read and approved the final version of this manuscript.
Acknowledgments
The authors would like to thank all individuals, institutions, and external collaborators who provided invaluable guidance, technical support, and insights throughout the completion of this research. Their expertise significantly contributed to the rigor, quality, and practical relevance of this study.
View statement in PDF · PDF page 15Ethics Approval
This study did not involve direct experimental interventions on human or animal subjects. The research focused on application development and system testing. Data were collected through interviews with relevant participants after obtaining informed consent from parents or teachers. All data were processed anonymously to ensure privacy and confidentiality, and the study was conducted in accordance with applicable ethical research standards.
View statement in PDF · PDF page 15Informed Consent
Informed consent was obtained verbally from all participants involved in this study. For participants who were minors, consent was obtained from their parents, teachers, and the school authorities. All data collected was anonymized to protect the privacy and confidentiality of the participants.
View statement in PDF · PDF page 16Financiación
The authors declare that this research was conducted independently and did not receive any external funding or financial support. All stages of the study, including system design, development, testing, and manuscript preparation, were carried out without external grants or sponsorships.
View statement in PDF · PDF page 16Disponibilidad de Datos
The data supporting the findings of this study are not publicly available due to privacy and confidentiality concerns related to the participants. Summary data are included in the manuscript, and additional information may be provided by the corresponding author upon reasonable request under strict confidentiality conditions.
View statement in PDF · PDF page 16Conflictos de Intereses
The authors confirm that there are no conflicts of interest, financial or otherwise, that could have influenced the research or the outcomes reported in this study.
View statement in PDF · PDF page 16Generative AI Disclosure
During the preparation of this manuscript, the author used generative AI and AI-assisted tools, including ChatGPT and Grammarly, to support language editing and proofreading. All content generated or assisted by these tools was carefully reviewed, revised, and validated by the author to ensure accuracy, originality, and academic integrity. The author takes full responsibility for the content of the manuscript and confirms that the use of these tools did not affect the study's scientific validity.
View statement in PDF · PDF page 16Citas
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Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2026 Muhamad Fathur Rahman, Resmi Darni, Dony Novaliendry, Khairi Budayawan