Automatic detection of dyslexia based on eye movements during reading in Russian

P. 59–66.

Laurinavichyute A., Lopukhina A., Reich D.

Dyslexia, a common learning disability, requires an early diagnosis. However, current screening tests are very time- and resourceconsuming. We present an LSTM that aims to automatically classify dyslexia based on eye movements recorded during natural reading combined with basic demographic information and linguistic features. The proposed model reaches an AUC of 0.93 and outperforms the state-of-the-art model by 7 %. We report several ablation studies demonstrating that the fixation features matter the most for classification.

Keywords: машинное обучение, reading, движения глаз, eye movements, дислексия, dyslexia, machine learning, чтение

Publication based on the results of: Neural foundations of language and its impairments: From basic research to clinical practice (2025)

In book

Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics

Vol. 2: Short papers. , Wien: Association for Computational Linguistics, 2025.