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ORIGINAL RESEARCH article

Front. Digit. Health

Sec. Digital Mental Health

This article is part of the Research TopiceHealth and Personalized Medicine in Mental Health and Neurodevelopmental Disorders: Digital Innovation for Diagnosis, Care, and Clinical ManagementView all 12 articles

Akshar Mitra: A Multimodal Integrated Framework for Early Dyslexia Detection

Provisionally accepted
Vibha  TiwariVibha Tiwari1*Ocean  AgarwalOcean Agarwal1Manya  SharmaManya Sharma1Radhika  BabarRadhika Babar1Rebakah  GeddamRebakah Geddam2Muhammad  AwaisMuhammad Awais3Hemant  GhayvatHemant Ghayvat4*Rashi  SahuRashi Sahu1
  • 1Madhav Institute of Technology and Science, Gwalior, India
  • 2Unitedworld Institute of Technology, Karnavati University, Gandhinagar, , Gujarat, India, India
  • 3Memorial Sloan Kettering Cancer Center, New York, United States
  • 4Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University,, Vaxj¨o, 35244, Sweden., Sweden

The final, formatted version of the article will be published soon.

Developmental dyslexia is a prevalent neurobiological disorder affecting 10-15% of children globally, yet it remains largely undiagnosed due to the inaccessibility of conventional assessments in resource-limited settings. Existing screening methods are further constrained by their reliance on unimodal data streams and the need for large, clinically-labeled datasets. This paper presents Akshar Mitra, a Multimodal Integrated Framework (MMF), a novel computational methodology designed for accessible and early dyslexia screening. The framework pioneers the integration of three low-cost, high-yield digital biomarkers derived from eye-tracking, speech, and handwriting analysis.The MMF is implemented through three modules: webcam-based eye-tracking for fixation and saccadic analysis, automated speech assessment for fluency metrics, and optical character recognition for handwriting error detection. Each module extracts 4–6 interpretable features (e.g., fixation regressions, word-error rate, character reversals) that are standardized via a shared data schema. These objective measures are augmented by a concise behavioral questionnaire to generate a holistic risk profile. Beyond screening, the system incorporates support tools, including a dyslexia-friendly reading interface with syllable-level highlighting, to foster user engagement and confidence.By creating a scalable, language-agnostic, and explainable system, this work offers a viable pathway to bridge the global dyslexia diagnostic gap. The MMF provides a transformative tool for proactive screening, facilitating early intervention and improving educational outcomes.

Keywords: Dyslexia detection, multimodal framework, Early Screening, health technology, Neurodevelopmental disorders, digitalhealth, cognitive assessment

Received: 16 Oct 2025; Accepted: 10 Nov 2025.

Copyright: © 2025 Tiwari, Agarwal, Sharma, Babar, Geddam, Awais, Ghayvat and Sahu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Vibha Tiwari, vibhatiwari19@gmail.com
Hemant Ghayvat, hemant.ghayvat@lnu.se

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