This paper is in the following e-collection/theme issue:

Tools and Questionnaires in Human Factors Evaluation (51) Autism Spectrum Disorder (ASD) (109) New Methods (944) Machine Learning (1465) Diagnostic Tools in Mental Health (242) Artificial Intelligence (1371) Imaging Informatics (193) Methods and New Tools in Mental Health Research (454)

Published on in Vol 8, No 4 (2021): Oct-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27706, first published .
Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning

Computer-Aided Screening of Autism Spectrum Disorder: Eye-Tracking Study Using Data Visualization and Deep Learning

Authors of this article:

Federica Cilia1 Author Orcid Image ;   Romuald Carette2 Author Orcid Image ;   Mahmoud Elbattah2 Author Orcid Image ;   Gilles Dequen2 Author Orcid Image ;   Jean-Luc Guérin2 Author Orcid Image ;   Jérôme Bosche2 Author Orcid Image ;   Luc Vandromme3 Author Orcid Image ;   Barbara Le Driant1 Author Orcid Image
Article Authors Cited by (34) Tweetations (3) Metrics

Journals

  1. Martínez-Lorca M, Gómez Fernández D. Rendimiento de los estímulos visuales en el diagnóstico del TEA por Eye Tracking: Revisión Sistemática. Revista de Investigación en Logopedia 2023;13(1):e83937 View
  2. Wei Q, Cao H, Shi Y, Xu X, Li T. Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis. Journal of Biomedical Informatics 2023;137:104254 View
  3. Hendr A, Ozgunalp U, Erbilek Kaya M. Diagnosis of Autism Spectrum Disorder Using Convolutional Neural Networks. Electronics 2023;12(3):612 View
  4. Sturner R, Howard B, Bergmann P, Attar S, Stewart-Artz L, Bet K, Allison C, Baron-Cohen S. Autism screening at 18 months of age: a comparison of the Q-CHAT-10 and M-CHAT screeners. Molecular Autism 2022;13(1) View
  5. Iwauchi K, Tanaka H, Okazaki K, Matsuda Y, Uratani M, Morimoto T, Nakamura S. Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders. Frontiers in Digital Health 2023;5 View
  6. Cilia F, Brisson J, Vandromme L, Garry C, Le Driant B. Multiple deictic cues allow ASD children to direct their visual attention. Current Psychology 2023;42(33):29549 View
  7. Khalaji E, Eraslan S, Yesilada Y, Yaneva V. Effects of data preprocessing on detecting autism in adults using web-based eye-tracking data. Behaviour & Information Technology 2023;42(14):2476 View
  8. Abdullah Mengash H, Alqahtani H, Maray M, K. Nour M, Marzouk R, Abdullah Al-Hagery M, Mohsen H, Al Duhayyim M. Automated Autism Spectral Disorder Classification Using Optimal Machine Learning Model. Computers, Materials & Continua 2023;74(3):5251 View
  9. Wu X, Deng H, Jian S, Chen H, Li Q, Gong R, Wu J. Global trends and hotspots in the digital therapeutics of autism spectrum disorders: a bibliometric analysis from 2002 to 2022. Frontiers in Psychiatry 2023;14 View
  10. Balasubramanian J, Gururaj B, Gayatri N. An effective autism spectrum disorder screening method using machine learning classification techniques. Concurrency and Computation: Practice and Experience 2024;36(2) View
  11. Asmetha Jeyarani R, Senthilkumar R. Eye Tracking Biomarkers for Autism Spectrum Disorder Detection using Machine Learning and Deep Learning Techniques: Review. Research in Autism Spectrum Disorders 2023;108:102228 View
  12. Awaji B, Senan E, Olayah F, Alshari E, Alsulami M, Abosaq H, Alqahtani J, Janrao P. Hybrid Techniques of Facial Feature Image Analysis for Early Detection of Autism Spectrum Disorder Based on Combined CNN Features. Diagnostics 2023;13(18):2948 View
  13. Feng M, Xu J. Detection of ASD Children through Deep-Learning Application of fMRI. Children 2023;10(10):1654 View
  14. Wang H, Zhao X, Yu D. Nonlinear features of gaze behavior during joint attention in children with autism spectrum disorder. Autism Research 2023;16(9):1786 View
  15. Uddin M, Shahriar M, Mahamood M, Alnajjar F, Pramanik M, Ahad M. Deep learning with image-based autism spectrum disorder analysis: A systematic review. Engineering Applications of Artificial Intelligence 2024;127:107185 View
  16. Ahmed Z, Albalawi E, Aldhyani T, Jadhav M, Janrao P, Obeidat M. Applying Eye Tracking with Deep Learning Techniques for Early-Stage Detection of Autism Spectrum Disorders. Data 2023;8(11):168 View
  17. Çetintaş D, Tuncer T, Çınar A. Detection of autism spectrum disorder from changing of pupil diameter using multi-modal feature fusion based hybrid CNN model. Journal of Ambient Intelligence and Humanized Computing 2023;14(8):11273 View
  18. Li Y, Huang W, Song P. A face image classification method of autistic children based on the two-phase transfer learning. Frontiers in Psychology 2023;14 View
  19. Mumenin N, Yousuf M, Nashiry M, Azad A, Alyami S, Lio' P, Moni M. ASDNet: A robust involution‐based architecture for diagnosis of autism spectrum disorder utilising eye‐tracking technology. IET Computer Vision 2024;18(5):666 View
  20. de Belen R, Eapen V, Bednarz T, Sowmya A, Coutrot A. Using visual attention estimation on videos for automated prediction of autism spectrum disorder and symptom severity in preschool children. PLOS ONE 2024;19(2):e0282818 View
  21. Simeoli R, Rega A, Cerasuolo M, Nappo R, Marocco D. Using Machine Learning for Motion Analysis to Early Detect Autism Spectrum Disorder: A Systematic Review. Review Journal of Autism and Developmental Disorders 2024 View
  22. Duvivier V, Derobertmasure A, Demeuse M. Eye tracking in a teaching context: comparative study of the professional vision of university supervisor trainers and pre-service teachers in initial training for secondary education in French-speaking Belgium. Frontiers in Education 2024;9 View
  23. Davis J, Harrington M, Howie F, Mohammed K, Gunderson J. Reducing Time to Diagnosis of Autism Spectrum Disorder Using an Integrated Community Specialty Care Model: A Retrospective Study. The Journal of Pediatrics 2024;270:114009 View
  24. Alsharif N, Al-Adhaileh M, Al-Yaari M, Farhah N, Khan Z. Utilizing deep learning models in an intelligent eye-tracking system for autism spectrum disorder diagnosis. Frontiers in Medicine 2024;11 View
  25. Benabderrahmane B, Gharzouli M, Benlecheb A. A novel multi-modal model to assist the diagnosis of autism spectrum disorder using eye-tracking data. Health Information Science and Systems 2024;12(1) View
  26. Islam M, Manab M, Mondal J, Zabeen S, Rahman F, Hasan M, Sadeque F, Noor J. Involution fused convolution for classifying eye-tracking patterns of children with Autism Spectrum Disorder. Engineering Applications of Artificial Intelligence 2025;139:109475 View
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Books/Policy Documents

  1. López De Luise D, Hertzulis F, Peralta J, Pescio P, Saad B, Ibacache T. Artificial Intelligence and Machine Learning for Healthcare. View