{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:58:03Z","timestamp":1785488283808,"version":"3.56.0"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T00:00:00Z","timestamp":1765929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,12,17]]},"DOI":"10.1145\/3774521.3774570","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T07:34:24Z","timestamp":1785483264000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Severity Grading of Autism Spectrum Disorder from Eye Gaze Scanpath Trajectory using Deep Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-4835-1974","authenticated-orcid":false,"given":"Debmani","family":"Saha","sequence":"first","affiliation":[{"name":"Indian Institute of Information Technology Kalyani, Kalyani, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2592-9620","authenticated-orcid":false,"given":"Kumari","family":"Rina","sequence":"additional","affiliation":[{"name":"All India Institute of Medical Sciences Kalyani, Kalyani, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8819-3067","authenticated-orcid":false,"given":"Deepshikha","family":"Ray","sequence":"additional","affiliation":[{"name":"University of Calcutta, Kolkata, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9928-9670","authenticated-orcid":false,"given":"Kaushik","family":"Mukhopadhyay","sequence":"additional","affiliation":[{"name":"All India Institute of Medical Sciences Kalyani, Kalyani, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8661-1715","authenticated-orcid":false,"given":"Ayoleena","family":"Roy","sequence":"additional","affiliation":[{"name":"All India Institute of Medical Sciences Kalyani, Kalyani, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0348-371X","authenticated-orcid":false,"given":"Oishila","family":"Bandyopadhyay","sequence":"additional","affiliation":[{"name":"Indian Institute of Information Technology Kalyani, Kalyani, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,7,31]]},"reference":[{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"I.\u00a0A. Ahmed E.\u00a0M. Senan T.\u00a0H. Rassem M.\u00a0A. Ali H.\u00a0S.\u00a0A. Shatnawi S.\u00a0M. Alwazer and M. Alshahrani. 2022. Eye tracking-based diagnosis and early detection of autism spectrum disorder using machine learning and deep learning techniques. Electronics 11 4 (2022) 530.","DOI":"10.3390\/electronics11040530"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"crossref","unstructured":"Z.\u00a0A. Ahmed and M.\u00a0E. Jadhav. 2020. Convolutional neural network for prediction of autism based on eye-tracking scanpaths. International Journal of Psychosocial Rehabilitation 24 5 (2020).","DOI":"10.37200\/IJPR\/V24I5\/PR201970"},{"key":"e_1_3_3_1_4_2","doi-asserted-by":"crossref","unstructured":"M. Alsaidi N. Obeid N. Al-Madi H. Hiary and I. Aljarah. 2024. A convolutional deep neural network approach to predict autism spectrum disorder based on eye-tracking scan paths. Information 15 3 (2024) 133.","DOI":"10.3390\/info15030133"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1176\/appi.books.9780890425596"},{"key":"e_1_3_3_1_6_2","unstructured":"N.\u00a0K. Arora M.\u00a0K.\u00a0C. Nair S. Gulati V. Deshmukh A. Mohapatra D. Mishra et\u00a0al. 2018. Prevalence of Autism Spectrum Disorder in Indian Children: A systematic review and meta-analysis. Neurology India 66 Supplement (2018) S100\u2013S104. https:\/\/pubmed.ncbi.nlm.nih.gov\/30860104\/"},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"publisher","unstructured":"C. Cortes and V. Vapnik. 1995. Support-vector networks. Machine Learning 20 3 (1995) 273\u2013297. 10.1007\/BF00994018","DOI":"10.1007\/BF00994018"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2005.177"},{"key":"e_1_3_3_1_9_2","unstructured":"A. Dosovitskiy et\u00a0al. 2020. An image is worth 16\u00d716 words: Transformers for image recognition at scale. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2010.11929 (2020)."},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.5220\/0010975500003123"},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"B.\u00a0K.\u00a0P. Horn and B.\u00a0G. Schunck. 1981. Determining optical flow. Artificial Intelligence 17 1\u20133 (1981) 185\u2013203.","DOI":"10.1016\/0004-3702(81)90024-2"},{"key":"e_1_3_3_1_13_2","unstructured":"A.\u00a0G. Howard M. Zhu B. Chen D. Kalenichenko W. Wang T. Weyand M. Andreetto and H. Adam. 2017. MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1704.04861 (2017)."},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_3_1_15_2","unstructured":"M.\u00a0F. Islam et\u00a0al. 2024. Involution fused ConvNet for classifying eye-tracking patterns of ASD children. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2401.03575 (2024)."},{"key":"e_1_3_3_1_16_2","doi-asserted-by":"crossref","unstructured":"A.\u00a0S. Jaradat M. Wedyan S. Alomari and M.\u00a0M. Barhoush. 2024. Using machine learning to diagnose autism based on eye-tracking technology. Diagnostics 15 1 (2024) 66.","DOI":"10.3390\/diagnostics15010066"},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"W. Jones and A. Klin. 2013. Attention to eyes is present but in decline in 2\u20136-month-old infants later diagnosed with autism. Nature 504 7480 (2013) 427\u2013431.","DOI":"10.1038\/nature12715"},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"M.\u00a0R. Kanhirakadavath and M.\u00a0S.\u00a0M. Chandran. 2022. Investigation of eye-tracking scan path as a biomarker for autism screening using machine learning algorithms. Diagnostics 12 2 (2022) 518.","DOI":"10.3390\/diagnostics12020518"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"crossref","unstructured":"W. Kasri Y. Himeur A. Copiaco et\u00a0al. 2025. Hybrid Vision Transformer-Mamba framework for autism diagnosis via eye-tracking analysis. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2506.06886 (2025).","DOI":"10.1109\/CCNCPS66785.2025.11135843"},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.241"},{"key":"e_1_3_3_1_21_2","unstructured":"M. Lefebvre et\u00a0al. 2021. Early intensive developmental-therapy centered intervention yields reduction in core symptom severity in children with severe ASD. Frontiers in Pediatrics 9 (2021) 785762."},{"key":"e_1_3_3_1_22_2","doi-asserted-by":"publisher","unstructured":"C.\u00a0R. Lorian and J.\u00a0R. Grisham. 2017. Severity of autism symptoms predicts emotional and behavioral problems in children with high-functioning autism: A two-year follow-up study. Frontiers in Psychology 8 (2017) 2004. 10.3389\/fpsyg.2017.02004","DOI":"10.3389\/fpsyg.2017.02004"},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"K. Nayar F. Shic M. Winston and M. Losh. 2022. A constellation of eye-tracking measures reveals social attention differences in ASD and the broad autism phenotype. Molecular Autism 13 1 (2022) 18.","DOI":"10.1186\/s13229-022-00490-w"},{"key":"e_1_3_3_1_24_2","volume-title":"Indian Scale for Assessment of Autism (ISAA): Test Manual","year":"2009","unstructured":"NIMHANS. 2009. Indian Scale for Assessment of Autism (ISAA): Test Manual. Ministry of Social Justice and Empowerment, New Delhi, India."},{"key":"e_1_3_3_1_25_2","unstructured":"S. Patra Y.\u00a0G. Reddy L. Saini D. Bhatt S. Mishra D. Taneja et\u00a0al. 2023. Prevalence of autism spectrum disorder among rural urban and tribal children (1\u201310 years of age). Journal of Neurosciences in Rural Practice 14 3 (2023) 422\u2013428. https:\/\/ruralneuropractice.com\/prevalence-of-autism-spectrum-disorder-among-rural-urban-and-tribal-children-1-10-years-of-age\/"},{"key":"e_1_3_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00474"},{"key":"e_1_3_3_1_27_2","unstructured":"K. Simonyan and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/1409.1556 (2014)."},{"key":"e_1_3_3_1_28_2","first-page":"1","volume-title":"Proceedings of the IEEE IATMSI","author":"Supritha R.","year":"2024","unstructured":"R. Supritha and B.\u00a0M. Gurusamy. 2024. Deep learning for autism detection using eye-tracking scanpaths. In Proceedings of the IEEE IATMSI. 1\u20136."},{"key":"e_1_3_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"e_1_3_3_1_30_2","first-page":"6105","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML)","volume":"97","author":"Tan M.","year":"2019","unstructured":"M. Tan and Q.\u00a0V. Le. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (ICML) , Vol.\u00a097. 6105\u20136114. https:\/\/proceedings.mlr.press\/v97\/tan19a.html"},{"key":"e_1_3_3_1_31_2","doi-asserted-by":"crossref","unstructured":"T. Thanarajan et\u00a0al. 2023. ETASD-CBODL: Chaotic Butterfly Optimization with Inception-LSTM for ASD diagnosis using eye-tracking. IEEE Access 11 (2023) 1995\u20132013.","DOI":"10.32604\/cmc.2023.039644"},{"key":"e_1_3_3_1_32_2","unstructured":"S. Wang Y. Jiang X. Duchesne et\u00a0al. 2018. Tracking and anticipatory looking measures reveal impaired predictive gaze control in autism spectrum disorder. Journal of Autism and Developmental Disorders 48 7 (2018) 2454\u20132468."},{"key":"e_1_3_3_1_33_2","doi-asserted-by":"crossref","unstructured":"T. Wang A. Kwong Z. Liu and J. Kong. 2024. New eye tracking metrics system: The value in early diagnosis of autism spectrum disorder. Journal of Affective Disorders 358 (2024) 326\u2013334.","DOI":"10.1101\/2024.08.28.24312596"},{"key":"e_1_3_3_1_34_2","volume-title":"VISIGRAPP","author":"Xie J.","year":"2019","unstructured":"J. Xie et\u00a0al. 2019. A two-stream end-to-end deep learning network for recognizing atypical visual attention in ASD. In VISIGRAPP."},{"key":"e_1_3_3_1_35_2","doi-asserted-by":"crossref","unstructured":"L. Zamudio and I. Monarca. 2023. Stratification of children with autism spectrum disorder through fusion of temporal information in eye-gaze scan-paths. ACM Transactions on Knowledge Discovery from Data 17 2 (2023) 14.","DOI":"10.1145\/3539226"},{"key":"e_1_3_3_1_36_2","doi-asserted-by":"crossref","unstructured":"O. \u0160pakov and D. Miniotas. 2007. Visualization of eye gaze data using heat maps. Elektronika ir Elektrotechnika 74 2 (2007) 55\u201358.","DOI":"10.5755\/j02.eie.10372"}],"event":{"name":"ICVGIP 2025: Indian Conference on Computer Vision, Graphics, and Image Processing","location":"Mandi Himachal Pradesh India","acronym":"ICVGIP 2025"},"container-title":["Proceedings of the Sixteen Indian Conference on Computer Vision, Graphics and Image Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3774521.3774570","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T08:06:38Z","timestamp":1785485198000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3774521.3774570"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,17]]},"references-count":35,"alternative-id":["10.1145\/3774521.3774570","10.1145\/3774521"],"URL":"https:\/\/doi.org\/10.1145\/3774521.3774570","relation":{},"subject":[],"published":{"date-parts":[[2025,12,17]]},"assertion":[{"value":"2026-07-31","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}