Together with academic & professional partners, we are researching new ways of improving the lives of individuals with dyslexia, dysgraphia & dyspraxia. We are not the only one to believe in the power of Artificial Intelligence.
If you would like to learn more about how our screening technology works, click here.
We hope you might find the following scientific literature useful. Below, we have put together a shortlist of references with their corresponding links.
- Detecting readers with dyslexia using machine learning with eye-tracking measures (L. Rello and M. Ballesteros – 2015)
- Machine learning and dyslexia: Classification of individual structural neuro-imaging scans of students with and without dyslexia (P. Tamboer et al. – 2016)
- Screening for Dyslexia Using Eye Tracking during Reading (M. Benfatto et al. – 2016)
- Features and machine learning for correlating and classifying between brain areas and dyslexia (A. Frid and L. Manevitz – 2018)
- Dyslexia and Dysgraphia prediction: A new machine learning approach (G. Richard and M. Serrurier – 2020) – Produced by the Dystech team.
- Ductus: A software package for the study of handwriting production (Guinet and Kandel – 2010)
- Identification and Rating of Developmental Dysgraphia by Handwriting Analysis (Mekyska et al. – 2013)
- Automated human-level diagnosis of dysgraphia using a consumer tablet (Asselborn et al. – 2018)
- The Dynamics of Handwriting Improves the Automated Diagnosis of Dysgraphia (Zolna et al. – 2019)
Research is an essential part of our work; we believe that the ordinary facts of today are the products of yesterday’s research.