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

New Methods (944) Machine Learning (1465) Artificial Intelligence (1371) Autism Spectrum Disorder (ASD) (109) Methods and New Tools in Mental Health Research (454) Diagnostic Tools in Mental Health (242)

Published on in Vol 23, No 8 (2021): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29328, first published .
Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation

Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation

Classification of Children With Autism and Typical Development Using Eye-Tracking Data From Face-to-Face Conversations: Machine Learning Model Development and Performance Evaluation

Authors of this article:

Zhong Zhao1 Author Orcid Image ;   Haiming Tang1 Author Orcid Image ;   Xiaobin Zhang2 Author Orcid Image ;   Xingda Qu1 Author Orcid Image ;   Xinyao Hu1 Author Orcid Image ;   Jianping Lu3 Author Orcid Image
Article Authors Cited by (41) Tweetations (9) Metrics

Journals

  1. (previously Marzena Szkodo) M, Micai M, Caruso A, Fulceri F, Fazio M, Scattoni M. Technologies to support the diagnosis and/or treatment of neurodevelopmental disorders: A systematic review. Neuroscience & Biobehavioral Reviews 2023;145:105021 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. Putra P, Shima K, Alvarez S, Shimatani K. Identifying autism spectrum disorder symptoms using response and gaze behavior during the Go/NoGo game CatChicken. Scientific Reports 2021;11(1) View
  4. Alcañiz M, Chicchi‐Giglioli I, Carrasco‐Ribelles L, Marín‐Morales J, Minissi M, Teruel‐García G, Sirera M, Abad L. Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis. Autism Research 2022;15(1):131 View
  5. Zhao Z, Wei J, Xing J, Zhang X, Qu X, Hu X, Lu J. Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach. Journal of Autism and Developmental Disorders 2023;53(3):934 View
  6. Kollias K, Syriopoulou-Delli C, Sarigiannidis P, Fragulis G. The Contribution of Machine Learning and Eye-Tracking Technology in Autism Spectrum Disorder Research: A Systematic Review. Electronics 2021;10(23):2982 View
  7. Ma Z, Xu L, Li Q, Li X, Shi Y, Zhang X, Yang Y, Wang J, Fan L, Wu L. Prediction Model for Sensory Perception Abnormality in Autism Spectrum Disorder. International Journal of Molecular Sciences 2023;24(3):2367 View
  8. Ahmed I, Senan E, Rassem T, Ali M, Shatnawi H, Alwazer S, Alshahrani M. Eye Tracking-Based Diagnosis and Early Detection of Autism Spectrum Disorder Using Machine Learning and Deep Learning Techniques. Electronics 2022;11(4):530 View
  9. Milano N, Simeoli R, Rega A, Marocco D. A deep learning latent variable model to identify children with autism through motor abnormalities. Frontiers in Psychology 2023;14 View
  10. 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
  11. 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
  12. Zheng Y, Liu C, Lai N, Wang Q, Xia Q, Sun X, Zhang S. Current development of biosensing technologies towards diagnosis of mental diseases. Frontiers in Bioengineering and Biotechnology 2023;11 View
  13. Lestarevic S, Kalanj M, Milutinovic L, Grujicic R, Vasic J, Maslak J, Mitkovic-Voncina M, Ljubomirovic N, Pejovic-Milovancevic M. Internal Consistency of the Serbian Translation of the Stanford Social Dimensions Scale and Association to Strengths and Difficulties Questionnaire Scores in Male and Female Individuals on the Autism Spectrum and Non-autistic Individuals. Journal of Autism and Developmental Disorders 2024;54(9):3423 View
  14. Cheng M, Zhang Y, Xie Y, Pan Y, Li X, Liu W, Yu C, Zhang D, Xing Y, Huang X, Wang F, You C, Zou Y, Liu Y, Liang F, Zhu H, Tang C, Deng H, Zou X, Li M. Computer-Aided Autism Spectrum Disorder Diagnosis With Behavior Signal Processing. IEEE Transactions on Affective Computing 2023;14(4):2982 View
  15. Sha M, Alqahtani A, Alsubai S, Dutta A. Modified Meta Heuristic BAT with ML Classifiers for Detection of Autism Spectrum Disorder. Biomolecules 2023;14(1):48 View
  16. Ziv I, Avni I, Dinstein I, Meiri G, Bonneh Y. Oculomotor randomness is higher in autistic children and increases with the severity of symptoms. Autism Research 2024;17(2):249 View
  17. 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
  18. 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
  19. Zhou W, Yang M, Tang J, Wang J, Hu B. Gaze Patterns in Children With Autism Spectrum Disorder to Emotional Faces: Scanpath and Similarity. IEEE Transactions on Neural Systems and Rehabilitation Engineering 2024;32:865 View
  20. Mohd Salah Aljabiri S, Hamdan M. Analyzing lower body movements using machine learning to classify autistic children. Biomedical Signal Processing and Control 2024;94:106288 View
  21. Wei Q, Dong W, Yu D, Wang K, Yang T, Xiao Y, Long D, Xiong H, Chen J, Xu X, Li T. Early identification of autism spectrum disorder based on machine learning with eye-tracking data. Journal of Affective Disorders 2024;358:326 View
  22. Jia Q, Wang X, Li X, Xie C, Zhang Q, Mu J, Yang W. Analysis of research hotspots and trends in pediatric ophthalmopathy based on 10 years of WoSCC literature. Frontiers in Pediatrics 2024;12 View
  23. Zhao Z, Zhang X, Zhang X, Qu X, Hu X, Lu J. Interpersonal Motor Coordination in Children with Autism and the Establishment of Machine Learning Models to Objectively Classify Children with Autism and Typical Development. IRBM 2024;45(5):100838 View
  24. Jia S, Jing J, Yang C. A Review on Autism Spectrum Disorder Screening by Artificial Intelligence Methods. Journal of Autism and Developmental Disorders 2024 View
  25. Nie W, Zhou B, Wang Z, Chen B, Wang X, Hu C, Li H, Xu Q, Xu X, Liu H. Computational Interpersonal Communication Model for Screening Autistic Toddlers: A Case Study of Response-to-Name. IEEE Journal of Biomedical and Health Informatics 2024;28(6):3683 View
  26. Liu Z, Yeh W, Lin K, Lin C, Chang C. Machine learning based approach for exploring online shopping behavior and preferences with eye tracking. Computer Science and Information Systems 2024;21(2):593 View
  27. Cerasuolo M, De Marco S, Nappo R, Simeoli R, Rega A. The Potential of Virtual Reality to Improve Diagnostic Assessment by Boosting Autism Spectrum Disorder Traits: A Systematic Review. Advances in Neurodevelopmental Disorders 2024 View
  28. 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: An Eye-Tracking Study with Novel Digital Biomarkers (Preprint). JMIR mHealth and uHealth 2024 View

Books/Policy Documents

  1. Rabbi M, Zohra F, Hossain F, Akhi N, Khan S, Mahbub K, Biswas M. Recent Trends in Image Processing and Pattern Recognition. View
  2. Magboo V, Magboo M. Artificial Intelligence and Data Science. View
  3. Singh A, Laroia M, Rawat A, Seeja K. International Conference on Innovative Computing and Communications. View
  4. Mitra S, Srinath K, Gowri Manohari V, Poornima D, Karunya K. ICT Analysis and Applications. View
  5. Lee S, Mun J, Kim S, Park H, Yang S, Kim H, Noh S, Kim W, Chung M. Computers Helping People with Special Needs. View