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

Tools and Questionnaires in Human Factors Evaluation (75) Autism Spectrum Disorder (ASD) (141) New Methods (1134) Machine Learning (2076) Diagnostic Tools in Mental Health (326) Artificial Intelligence (2129) Imaging Informatics (302) Methods and New Tools in Mental Health Research (558)

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 (59) Tweetations (2) 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
  27. Liu Z, Li J, Zhang Y, Wu D, Huo Y, Yang J, Zhang M, Dong C, Jiang L, Sun R, Zhou R, Li F, Yu X, Zhu D, Guo Y, Chen J. Auxiliary Diagnosis of Children With Attention-Deficit/Hyperactivity Disorder Using Eye-Tracking and Digital Biomarkers: Case-Control Study. JMIR mHealth and uHealth 2024;12:e58927 View
  28. Su W, Mutersbaugh J, Huang W, Bhat A, Gandjbakhche A. Using deep learning to classify developmental differences in reaching and placing movements in children with and without autism spectrum disorder. Scientific Reports 2024;14(1) View
  29. Eraslan S, Yesilada Y, Shafique A, Yaneva V, Ha L. A systematic evaluation of autism spectrum disorder identification with Scanpath Trend Analysis (STA). Biomedical Signal Processing and Control 2025;103:107414 View
  30. Jaradat A, Wedyan M, Alomari S, Barhoush M. Using Machine Learning to Diagnose Autism Based on Eye Tracking Technology. Diagnostics 2024;15(1):66 View
  31. Zhang B, Jiang Q, Yan C, Tao R. Interpersonal synchronization and eye-tracking in children with autism spectrum disorder: A systematic review. Displays 2025;87:102950 View
  32. Zhang S. AI-assisted early screening, diagnosis, and intervention for autism in young children. Frontiers in Psychiatry 2025;16 View
  33. Deng S, Kosloski E, Patel S, Barnett Z, Nan Y, Kaplan A, Aarukapalli S, Doan W, Wang M, Singh H, Rollins P, Tian Y. Hear Me, See Me, Understand Me: Audio-Visual Autism Behavior Recognition. IEEE Transactions on Multimedia 2025;27:2335 View
  34. Chawla V, Rana B. Early fusion for Autism classification with biomarker detection using rs-fMRI and phenotypic data. Biomedical Signal Processing and Control 2025;109:108020 View
  35. Hao S, Pan H, Zhang D. A Process-Oriented Approach to Assessing High School Students’ Mathematical Problem-Solving Competence: Insights from Multidimensional Eye-Tracking Analysis. Education Sciences 2025;15(6):761 View
  36. El Hmimdi A, Kapoula Z. Distinguishing Dyslexia, Attention Deficit, and Learning Disorders: Insights from AI and Eye Movements. Bioengineering 2025;12(7):737 View
  37. Aldhyani T, Al-Nefaie A. DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms. Frontiers in Physiology 2025;16 View
  38. Ahmed M, Hussain S, Ali F, Gárate-Escamilla A, Amaya I, Ochoa-Ruiz G, Ortiz-Bayliss J. Summarizing Recent Developments on Autism Spectrum Disorder Detection and Classification Through Machine Learning and Deep Learning Techniques. Applied Sciences 2025;15(14):8056 View
  39. Nguyen T, Nguyen T, Le T, Ngo T. Eye-tracking technology applications for supporting individuals with autism spectrum disorder: insights, challenges, and opportunities. Disability and Rehabilitation: Assistive Technology 2025;20(8):2630 View
  40. Mousli S, Taheri S, He E. ConASD: Contrastive Few Shot Learning for Detecting Autism Spectrum Disorder via Eye Tracking Scanpath. Multimedia Systems 2025;31(4) View
  41. Gawish A, Sarah M. Ayyad , Sabry F. Saraya , Ahmed I. Saleh . Autism Spectrum Disorder Classification in Children Using Eye-tracking Technology and Convolutional Neural Networks. International Journal of Computational and Experimental Science and Engineering 2025;11(3) View
  42. Al-Adhaileh M, Alsubari S, Al-Nefaie A, Ahmad S, Alhamadi A. Diagnosing autism spectrum disorder based on eye tracking technology using deep learning models. Frontiers in Medicine 2025;12 View
  43. Rakotomanana H, Rouhafzay G. A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data. Bioengineering 2025;12(11):1136 View
  44. Reilly A, Walsh N, O’Reilly D, Smyth M, Gorman K, Ostadabbas S, Power C. The role of machine learning in autism spectrum disorder assessment and management. Pediatric Research 2025 View
  45. Aldakhil A, Alasim K. Diagnostic accuracy of AI-based models for autism spectrum disorder: A systematic review and meta-analysis with a focus on Arab populations. Research in Developmental Disabilities 2025;167:105166 View
  46. Soloh R, Orm L, Dabdoub D. An image-based analytics framework for early autism detection using eye movements. Healthcare Analytics 2025;8:100439 View

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
  2. Vitanidi A, Nanos A. Empowering Innovations in Advanced Autism Research and Management. View
  3. Simeoli R, Marocco D. Bridging Biology and Behavior in Autism - Innovations in Research and Practice. View
  4. Jasim D, Kadhem S. Data Processing and Networking. View

Conference Proceedings

  1. Kabir Mehedi M, Arafin I, Md Hasib K, Rahman F, Alam M, Tasin R, Alim Rasel A. Proceedings of the 2023 9th International Conference on Computer Technology Applications. Early Autism Disorder Detection Through Visualizing Eye-Tracking Patterns Using Compact Convolutional Transformers View
  2. Reddy P, J A. RAiSE-2023. Diagnosis of Autism in Children Using Deep Learning Techniques by Analyzing Facial Features View
  3. Kumar R, Bordoloi D, Shrivastava A, Kumar C, Kumari V, Kumar A. 2023 10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). Behavioral and Clinical Data Analysis for Autism Spectrum Disorder Screening with Machine Learning View
  4. Kavitha V, Siva R, Suresh K. 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS). ESOA:Classification of Autism Spectrum Disorder based on Gaze Tracking Imaging using Egret Swarm Optimization Algorithm View
  5. Mathew J, Asha V, S P, Kumbhar O, T R, Rohit R. 2024 International Conference on Integrated Circuits and Communication Systems (ICICACS). Autism Spectrum Disorder Using Convolutional Neural Networks View
  6. Benabderrahmane B, Gharzouli M, Benlecheb A. 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS). Machine Learning-Based Detection of Autism Spectrum Disorder Using Attention Mechanisms in Eye-Tracking Data View
  7. Patil S, Shimpi R, Rode R, Chakrabarti S, Pawar R. 2025 4th OPJU International Technology Conference (OTCON) on Smart Computing for Innovation and Advancement in Industry 5.0. Autism Detection Using Eye Tracking View
  8. Copiaco A, Ritz C, Himeur Y, Eapen V, Albanna A, Mansoor W. 2025 International Conference on Control, Automation and Diagnosis (ICCAD). Exploring Image Transforms derived from Eye Gaze Variables for Progressive Autism Diagnosis View
  9. Lencastre P, Devkota A, Bhandari S, Kuriakose B, Lind P. 2025 International Joint Conference on Neural Networks (IJCNN). Comparing eye-tracking time series imaging methods in ADHD screening View