Predicting Autism Using Machine Learning and Eye-Tracking
Autism Spectrum Disorder (ASD) is a pervasive developmental disorder characterized by a set of impairments including social communication problems.
Abnormalities of eye gaze have been consistently recognized as the hallmark of ASD.
Our research applies eye-tracking techniques along with Machine Learning to support the diagnosis ASD.
Key Idea: Learning the Visual Patterns of Eye-Tracking Scanpaths
Resources
Data
Python Code
Experiments on Azure ML
Demo Application
A practical illustration of our approach. The application makes use of Azure predictive web services. It can be accessed via [URL]
Publications
- Cilia, F., Carette, R., Elbattah, M., Guérin, J. L., & Dequen, G. (2022). Eye-Tracking Dataset to Support the Research on Autism Spectrum Disorder. In Proceedings of the IJCAI–ECAI Workshop on Scarce Data in Artificial Intelligence for Healthcare (SDAIH). [URL]
- Elbattah, M., Carette, R., Cilia, F., Guérin, J., & Dequen, G. (2023). Applications of machine learning methods to assist the diagnosis of autism spectrum disorder. In Suri, J.S. & El-baz, A.S. (Eds.). Neural Engineering Techniques for Autism Spectrum Disorder, Vol.2. Elsevier. [URL]
- Elbattah, M., Guérin, J., Carette, R., Cilia, F. & Dequen, G. (2022). Vision-based approach for autism diagnosis using transfer learning and eye-tracking. In Proceedings of the 15th International Conference on Health Informatics (HEALTHINF). [URL]
- Cilia, F., Carette, R., Elbattah, M., Dequen, G., Guérin, J. L., Bosche, J., ... & Le Driant, B. (2021). Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning. JMIR Human Factors, 8(4), e27706. [URL]
- Elbattah, M., Guérin, J., Carette, R., Cilia, F. & Dequen, G. (2020). NLP-based approach to detect autism spectrum disorder in saccadic eye movement. In Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. [URL]
- Elbattah, M., Carette, R. ,Dequen, G., Guérin, J, & Cilia, F. (2019). Learning Clusters in Autism Spectrum Disorder: Image-Based Clustering of Eye-Tracking Scanpaths with Deep Autoencoder. In Proceedings of the 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. [URL]
- Carette, R., Elbattah, M., Dequen, G., Guérin, J, & Cilia, F. (2019). Learning to predict autism spectrum disorder based on the visual patterns of eye-tracking scanpaths. In Proceedings of the 12th International Conference on Health Informatics (HEALTHINF 2019). [URL]
- Carette, R., Elbattah, M., Dequen, G., Guérin J.L., & Cilia F. (2018). Visualization of eye-tracking patterns in autism spectrum disorder: method and dataset. In Proceedings of the 13th International Conference on Digital Information Management (ICDIM 2018).IEEE. [URL]
- Carette, R., Cilia, F., Dequen, G., Bosche, J., Guerin, J. L., & Vandromme, L. (2017). Automatic autism spectrum disorder detection thanks to eye-tracking and neural network-based approach. In Proceedings of the International Conference on IoT Technologies for HealthCare.Springer. [URL]
Team
Laboratoire MIS, Université de Picardie Jules Verne
- Romuald Carrette
- Mahmoud Elbattah
- Gilles Dequen
- Jean-Luc Guérin
Contact: mahmoud.elbattah@uwe.ac.uk
CRP-CPO, Université de Picardie Jules Verne
- Federica Cilia
- Luc Vandromme
Industry Partner
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