Publication details
INSIGHT: Combining Fixation Visualizations and Residual Neural Networks for Dyslexia Classification from Eye-Tracking Data
| Authors | |
|---|---|
| Year of publication | 2025 |
| Type | Article in Periodical |
| Magazine / Source | DYSLEXIA |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1002/dys.1801 |
| Keywords | dyslexia, eye tracking, eye movement, fixation data classification, deep learning, ResNet18, AI-based diagnosis |
| Description | 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 visualization phase and a neural network-based classification phase. The first phase involves transforming eye-tracking fixation data into 2D visualizations called Fix-images, which clearly depict reading difficulties. The second phase utilizes 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 personalized and effective interventions. |
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