ArticleDyslexia (Chichester, England)2025
INSIGHT: Combining Fixation Visualisations and Residual Neural Networks for Dyslexia Classification From Eye-Tracking Data.
Article in Dyslexia (Chichester, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-enabled eye-movement and emerging multimodal frameworks for precision dyslexia screening and reading pattern analysis.Frontiers in medicine · 2026Pooled it
- Enhancing the detection of visual-cognitive disruptions in automated dyslexia diagnosis using viscoelastic constitutive artificial neural networks for eye movement analysis.Documenta ophthalmologica. Advances in ophthalmology · 2026Article
- From "quantifying the child" to "supporting the caregiver": a paradigm evaluation and ethical pathway selection for AI applications in child development.Frontiers in psychology · 2026Review
- An In-Depth Investigation of Eye Movement Profile of Dyslexic Readers Using a Standardized Text-Reading Aloud Task in French.Behavioral sciences (Basel, Switzerland) · 2025Article
- Distinguishing Dyslexia, Attention Deficit, and Learning Disorders: Insights from AI and Eye Movements.Bioengineering (Basel, Switzerland) · 2025Article
- Enhancing particle swarm optimization based on optical computing mechanism: application to dyslexia detection.Frontiers in artificial intelligence · 2025Article
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4 authors.
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Abstract
Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge the gap between scientific and diagnostic concepts by proposing a novel dyslexia detection method, called INSIGHT, which combines a visualisation phase and a neural network-based classification phase. The first phase involves transforming eye-tracking fixation data into 2D visualisations called Fix-images, which clearly depict reading difficulties. The second phase utilises the ResNet18 convolutional neural network for classifying these images. The INSIGHT method was tested on 35 child participants (13 dyslexic and 22 control readers) using three text-reading tasks, achieving a highest accuracy of 86.65%. Additionally, we cross-tested the method on an independent dataset of Danish readers, confirming the robustness and generalizability of our approach with a notable accuracy of 86.11%. This innovative approach not only provides detailed insight into eye movement patterns when reading but also offers a robust framework for the early and accurate diagnosis of dyslexia, supporting the potential for more personalised and effective interventions.
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