Tonge BJ, Bull K, Brereton A, Wilson R. A review of evidence-based early intervention for behavioural problems in children with autism spectrum disorder. Curr Opin Psychiatry. 2014;27(2):158–65. https://doi.org/10.1097/yco.0000000000000043.

Article  Google Scholar 

Lord C, Elsabbagh M, Baird G, Veenstra-Vanderweele J. Autism spectrum disorder. The Lancet. 2018;392(10146):508–20. https://doi.org/10.1016/s0140-6736(18)31129-2.

Article  Google Scholar 

American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Virginia: American Psychiatric Association; 2013. https://doi.org/10.1176/appi.books.9780890425596.

Book  Google Scholar 

Autism—who.int. https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disorders. Accessed 18 Jun 2023.

CDC. Data and statistics on autism spectrum disorder | CDC—cdc.gov. https://www.cdc.gov/ncbddd/autism/data.html. Accessed 18 Jun 2023.

Elder J, Kreider C, Brasher S, Ansell M. Clinical impact of early diagnosis of autism on the prognosis and parent-child relationships. Psychol Res Behav Manag. 2017;10:283–92. https://doi.org/10.2147/prbm.s117499.

Article  Google Scholar 

Foxx RM. Applied behavior analysis treatment of autism: the state of the art. Child Adolesc Psychiatr Clin N Am. 2008;17(4):821–34. https://doi.org/10.1016/j.chc.2008.06.007.

Article  Google Scholar 

Ishizaki Y, Higuchi T, Yanagimoto Y, Kobayashi H, Noritake A, Nakamura K, Kaneko K. Eye gaze differences in school scenes between preschool children and adolescents with high-functioning autism spectrum disorder and those with typical development. BioPsychoSocial Med. 2021. https://doi.org/10.1186/s13030-020-00203-w.

Article  Google Scholar 

Stuart N, Whitehouse A, Palermo R, Bothe E, Badcock N. Eye gaze in autism spectrum disorder: a review of neural evidence for the eye avoidance hypothesis. J Autism Dev Disord. 2022;53(5):1884–905. https://doi.org/10.1007/s10803-022-05443-z.

Article  Google Scholar 

Wang Y, Peng S, Shao Z, Feng T. Active viewing facilitates gaze to the eye region in young children with autism spectrum disorder. J Autism Dev Disord. 2022;53(3):1082–90. https://doi.org/10.1007/s10803-022-05462-w.

Article  Google Scholar 

Wilkinson KM, Madel M. Eye tracking measures reveal how changes in the design of displays for augmentative and alternative communication influence visual search in individuals with down syndrome or autism spectrum disorder. Am J Speech Lang Pathol. 2019;28(4):1649–58. https://doi.org/10.1044/2019_ajslp-19-0006.

Article  Google Scholar 

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. J Biomed Inform. 2023;137: 104254. https://doi.org/10.1016/j.jbi.2022.104254.

Article  Google Scholar 

Vargas-Cuentas NI, Roman-Gonzalez A, Gilman RH, Barrientos F, Ting J, Hidalgo D, Jensen K, Zimic M. Developing an eye-tracking algorithm as a potential tool for early diagnosis of autism spectrum disorder in children. PLoS ONE. 2017;12(11): e0188826. https://doi.org/10.1371/journal.pone.0188826.

Article  Google Scholar 

Jing W, Zehui L, Yifan W, Ling W, Linfei S. An eye movement study of joint attention deficits in children with autism spectrum disorders. In: Intelligent robotics and applications. Cham: Springer International Publishing; 2022. p. 392–402. https://doi.org/10.1007/978-3-031-13844-7_38.

Chapter  Google Scholar 

Tao Y, Shyu ML. Sp-asdnet: Cnn-lstm based asd classification model using observer scanpaths. In: 2019 IEEE International conference on multimedia & expo workshops (ICMEW). IEEE; 2019. pp 641–646.

Duan H, Zhai G, Min X, Che Z, Fang Y, Yang X, Gutiérrez J, Le Callet P. A dataset of eye movements for the children with autism spectrum disorder. 2019. https://zenodo.org/record/2647418

Carette R, Cilia F, Dequen G, Bosche J, Guerin JL, Vandromme L. Automatic autism spectrum disorder detection thanks to eye-tracking and neural network-based approach. In: Internet of Things (IoT) technologies for healthcare. Cham: Springer International Publishing; 2018. p. 75–81.

Chapter  Google Scholar 

Ahmed IA, Senan EM, Rassem TH, Ali MA, Shatnawi HSA, Alwazer SM, 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.

Article  Google Scholar 

Ahmed ZA, Albalawi E, Aldhyani TH, Jadhav ME, Janrao P, Obeidat MRM. Applying eye tracking with deep learning techniques for early-stage detection of autism spectrum disorders. Data. 2023;8(11):168.

Article  Google Scholar 

Cilia F, Carette R, Elbattah M, Guérin JL, Dequen G. Eye-tracking dataset to support the research on autism spectrum disorder. 2022. https://doi.org/10.21203/rs.3.rs-2099817/v1.

Kanhirakadavath MR, Chandran MSM. Investigation of eye-tracking scan path as a biomarker for autism screening using machine learning algorithms. Diagnostics. 2022;12(2):518. https://doi.org/10.3390/diagnostics12020518.

Article  Google Scholar 

Alcañiz M, Chicchi-Giglioli IA, Carrasco-Ribelles LA, Marín-Morales J, Minissi ME, 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 Res. 2021;15(1):131–45. https://doi.org/10.1002/aur.2636.

Article  Google Scholar 

Cilia F, Carette R, Elbattah M, Dequen G, Guérin J-L, Bosche J, Vandromme L, Driant BL. Computer-aided screening of autism spectrum disorder: eye-tracking study using data visualization and deep learning. JMIR Hum Factors. 2021;8(4): e27706. https://doi.org/10.2196/27706.

Article  Google Scholar 

Elbattah M, Guérin JL, Carette R, Cilia F, Dequen G. Vision-based approach for autism diagnosis using transfer learning and eye-tracking. In: Proceedings of the 15th international joint conference on biomedical engineering systems and technologies. SCITEPRESS-Science and Technology Publications; 2022. p. 256–63.

Chapter  Google Scholar 

Almourad MB, Bataineh E, Stocker J, Marir F. Analyzing the behavior of autistic and normal developing children using eye tracking data. In: Advances in intelligent systems and computing. Singapore: Springer; 2018. p. 340–9. https://doi.org/10.1007/978-981-10-8612-0_36.

Chapter  Google Scholar 

Sharma N, Jain V, Mishra A. An analysis of convolutional neural networks for image classification. Proc Comput Sci. 2018;132:377–84. https://doi.org/10.1016/j.procs.2018.05.198.

Article  Google Scholar 

Chung J, Gulcehre C,Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. 2014. Available from: http://arxiv.org/abs/1412.3555

Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, Lin Z, Gimelshein N, Antiga L, Desmaison A, Kopf A, Yang E, DeVito Z, Raison M, Tejani A, Chilamkurthy S, Steiner B, Fang L, Bai J, Chintala S. Pytorch: an imperative style, high-performance deep learning library. In: Advances in neural information processing systems 32. Curran Associates Inc.; 2019. p. 8024–35.

Abualigah L, Al-Ajlouni YY, Daoud MS, Altalhi M, Migdady H. Fake news detection using recurrent neural network based on bidirectional lstm and glove. Soc Netw Anal Min. 2024;14(1):1–16.

Article  Google Scholar