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Early Detection of Autism in the Pre-Symptomatic Period: Eye-Tracking Technologies and Visual Attention Biomarkers

Yıl 2025, Cilt: 7 Sayı: 1, 1 - 28, 06.09.2025
https://doi.org/10.37233/trsped.drr.1678367

Öz

Traditionally, autism diagnosis has been based on distinct behavioral characteristics such as impairments in social interaction, communication difficulties, and repetitive behaviors. However, behavioral symptoms are not always sufficiently specific or pronounced to support early diagnosis and typically become evident during the second year of life. The identification of autism during the pre-symptomatic period is critically important for early intervention and long-term developmental outcomes. The assessment of visual attention limitations through eye-tracking technologies is considered an advantageous method in autism research due to its potential to detect autism-related traits earlier, more objectively, and more reliably compared to traditional assessment tools. Recent studies suggest that the analysis of large-scale eye-tracking data using artificial intelligence and machine learning approaches can contribute to an objective autism diagnosis. Accordingly, this study focuses on the reliability, consistency, and diagnostic value of visual attention biomarkers, which have explanatory potential in autism, and explores their role in diagnostic processes.

Kaynakça

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Otizmin Semptom Öncesi Dönemde Tespiti: Göz İzleme Teknolojileri ve Görsel Dikkat Biyobelirteçleri

Yıl 2025, Cilt: 7 Sayı: 1, 1 - 28, 06.09.2025
https://doi.org/10.37233/trsped.drr.1678367

Öz

Geleneksel olarak otizm tanısı, sosyal etkileşimdeki bozukluklar, iletişim sorunları ve tekrarlayıcı davranışlar gibi belirgin davranışsal özelliklere dayanmaktadır. Ancak, davranışsal belirtiler erken tanıyı tam olarak desteklemek için her zaman yeterince belirgin ya da spesifik olmamakta ve genellikle yaşamın ikinci yılında açık hale gelmektedir. Otizmin semptom öncesi dönemde tespiti, erken müdahale ve uzun vadeli gelişimsel sonuçlar açısından büyük önem taşımaktadır. Semptom öncesi dönemde biyolojik süreçlerin ölçümü ise otizm riski altındaki bebekleri belirlemek için alternatif ve umut verici bir yol sunmaktadır. Görsel dikkatteki sınırlılıkların göz izleme teknolojileri ile ölçülmesi, otizmin erken dönemde tespit edilmesini mümkün kılmaktadır. Bu yöntem, otizme özgü özelliklerin geleneksel değerlendirme yöntemlerine kıyasla daha erken, daha objektif ve daha güvenilir bir biçimde ayırt etme potansiyeli nedeniyle otizm araştırmalarında avantajlı bir yaklaşım olarak değerlendirilmektedir. Güncel araştırmalar, görsel dikkate yönelik göz izleme teknolojilerinden elde edilen büyük verinin yapay zekâ ve makine öğrenmesi yöntemleriyle işlenerek objektif otizm tanısına önemli katkılar sunabileceğini ortaya koymaktadır. Bu kapsamda bu çalışma güvenilirliği, tutarlılığı ve otizmde açıklayıcı potansiyeli olan görsel dikkat biyobelirteçleri ve tanılama süreçlerindeki rolüne odaklanmaktadır.

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  • Minas, D., Tews, L., Fotopoulos, A., Xenos, M., Calvo-Córdoba, A., & Rivas-Vidal, M. (2025). Eye-tracking technologies for facilitating multimodal interaction in aviation environments. Engineering Proceedings, 90(1), 110. https://doi.org/10.3390/engproc2025090110
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  • Murias, M., Major, S., Davlantis, K., Franz, L., Harris, A., Rardin, B., Sabatos‐DeVito, M., & Dawson, G. (2018). Validation of eye‐tracking measures of social attention as a potential biomarker for autism clinical trials. Autism Research, 11(1), 166–174. https://doi.org/10.1002/aur.1894
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  • Rogala, J., Żygierewicz, J., Malinowska, U., Cygan, H., Stawicka, E., Kobus, A., & Vanrumste, B. (2023). Enhancing autism spectrum disorder classification in children through the integration of traditional statistics and classical machine learning techniques in EEG analysis. Scientific Reports, 13(1). https://doi.org/10.1038/s41598- 023-49048-7 Ruggeri, B., Sarkans, U., Schumann, G., & Persico, A. M. (2013). Biomarkers in autism spectrum disorder: the old and the new. Psychopharmacology, 231(6), 1201–1216. https://doi.org/10.1007/s00213-013-3290-7
  • Salgado-Cacho, J. M., Del Pilar Moreno-Jiménez, M., & De Diego-Otero, Y. (2021). Detection of early warning signs in autism Spectrum Disorders: A Systematic review. Children, 8(2), 164. https://doi.org/10.3390/children8020164
  • Sasson, N. J., & Elison, J. T. (2012). Eye tracking young children with autism. Journal of Visualized Experiments, 61. https://doi.org/10.3791/3675
  • Schröder, R., Keidel, K., Trautner, P., Radbruch, A., & Ettinger, U. (2022). Neural mechanisms of background and velocity effects in smooth pursuit eye movements. Human Brain Mapping, 44(3), 1002–1018. https://doi.org/10.1002/hbm.26127
  • Shindler, A. E., Hill-Yardin, E. L., Petrovski, S., Bishop, N., & Franks, A. E. (2019). Towards identifying genetic biomarkers for gastrointestinal dysfunction in autism. Journal of Autism and Developmental Disorders, 50(1), 76–86. https://doi.org/10.1007/s10803- 019-04231-6
  • Song, Y., Hakoda, Y., Sanefuji, W., & Cheng, C. (2015). Can they see ıt? The functional field of view ıs narrower in ındividuals with autism spectrum disorder. PLoS ONE, 10(7), e0133237. https://doi.org/10.1371/journal.pone.0133237
  • Song, D., Kim, S. Y., Bong, G., Kim, J. M., & Yoo, H. J. (2019). The use of artificial intelligence in screening and diagnosis of autism spectrum disorder: A literature review. Journal of Korean Academy of Child and Adolescent Psychiatry, 30(4), 145–152. https://doi.org/10.5765/jkacap.190027
  • Spering, M. (2022). Eye movements as a window into decision-making. Annual Review of Vision Science, 8(1), 427–448. https://doi.org/10.1146/annurev-vision-100720-125029
  • Stahmer, A. C., Vejnoska, S., Iadarola, S., Straiton, D., Segovia, F. R., Luelmo, P., Morgan, E. H., Lee, H. S., Javed, A., Bronstein, B., Hochheimer, S., Cho, E., Aranbarri, A., Mandell, D., Hassrick, E. M., Smith, T., & Kasari, C. (2019). Caregiver Voices: Cross-Cultural Input on Improving Access to Autism Services. Journal of Racial and Ethnic Health Disparities, 6(4), 752–773. https://doi.org/10.1007/s40615-019-00575-y
  • Strupp, M. L., Straumann, D., & Helmchen, C. (2021). Nystagmus: diagnosis, topographic anatomical localization and therapy. Klinische Monatsblätter Für Augenheilkunde, 238(11), 1186–1195. https://doi.org/10.1055/a-1525-0030 Stuart, N., Whitehouse, A., Palermo, R., Bothe, E., & Badcock, N. (2022). Eye gaze in Autism Spectrum Disorder: A review of neural evidence for the eye avoidance hypothesis. Journal of Autism and Developmental Disorders, 53(5), 1884–1905. https://doi.org/10.1007/s10803-022-05443-z
  • Thankachan, B. (2018). Haptic feedback to gaze events (Doctoral dissertation). University of Tampere.
  • Van Der Donck, S., Vettori, S., Dzhelyova, M., Mahdi, S. S., Claes, P., Steyaert, J., & Boets, B. (2021). Investigating automatic emotion processing in boys with autism via eye tracking and facial mimicry recordings. Autism Research, 14(7), 1404–1420. https://doi.org/10.1002/aur.2490
  • Van ’t Hof, M., Tisseur, C., Van Berckelear-Onnes, I., Van Nieuwenhuyzen, A., Daniels, A. M., Deen, M., Hoek, H. W., & Ester, W. A. (2020). Age at autism spectrum disorder diagnosis: A systematic review and meta-analysis from 2012 to 2019. Autism, 25(4), 862–873. https://doi.org/10.1177/1362361320971107
  • Vivanti, G., & Stahmer, A. (2018). Early intervention for autism: Are we prioritizing feasibility at the expenses of effectiveness? A cautionary note. Autism, 22(7), 770–773. https://doi.org/10.1177/1362361318803043
  • Wang, T., Guo, H., Xiong, B., Stessman, H. A., Wu, H., Coe, B. P., Turner, T. N., Liu, Y., Zhao, W., Hoekzema, K., Vives, L., Xia, L., Tang, M., Ou, J., Chen, B., Shen, Y., Xun, G., Long, M., Lin, J., & Eichler, E. E. (2016). De novo genic mutations among a Chinese autism spectrum disorder cohort. Nature Communications, 7(1). https://doi.org/10.1038/ncomms13316
  • Wang, S., Jiang, M., Duchesne, X. M., Laugeson, E. A., Kennedy, D. P., Adolphs, R., & Zhao, Q. (2015). Atypical visual saliency in autism spectrum disorder quantified through model-based eye tracking. Neuron, 88(3), 604–616. https://doi.org/10.1016/j.neuron.2015.09.042
  • Wan, G., Kong, X., Sun, B., Yu, S., Tu, Y., Park, J., Lang, C., Koh, M., Wei, Z., Feng, Z., Lin, Y., & Kong, J. (2019). Applying eye tracking to identify autism spectrum disorder in children. Journal of Autism and Developmental Disorders, 49(1), 209–215. https://doi.org/10.1007/s10803-018-3690-y
  • Wang, Y. L., Kapoor, M., Fielding, J., Reddel, S., Zhu, C., Clough, M., Botrous, M., Monif, M., & Van Der Walt, A. (2025). Saccadic eye movements in neurological disease: cognitive mechanisms and clinical applications. Journal of Neurology, 272(8). https://doi.org/10.1007/s00415-025-13275-x
  • Wang, R. K., Kwong, K., Liu, K., & Kong, X. (2024). New eye tracking metrics system: the value in early diagnosis of autism spectrum disorder. Frontiers in Psychiatry, 15. https://doi.org/10.3389/fpsyt.2024.1518180
  • Washington, P., Park, N., Srivastava, P., Voss, C., Kline, A., Varma, M., Tariq, Q., Kalantarian, H., Schwartz, J., Patnaik, R., Chrisman, B., Stockham, N., Paskov, K., Haber, N., & Wall, D. P. (2019). Data-Driven Diagnostics and the potential of mobile artificial intelligence for digital therapeutic phenotyping in computational psychiatry. Biological Psychiatry Cognitive Neuroscience and Neuroimaging, 5(8), 759–769. https://doi.org/10.1016/j.bpsc.2019.11.015
  • Wei, Q., Cao, H., Shi, Y., Xu, X., & Li, T. (2023). Machine learning based on eye-tracking data to identify Autism Spectrum Disorder: A systematic review and meta-analysis. Journal of Biomedical Informatics, 137, 104254. https://doi.org/10.1016/j.jbi.2022.104254
  • Wei, Q., Dong, W., Yu, D., Wang, K., Yang, T., Xiao, Y., Long, D., Xiong, H., Chen, J., Xu, X., & Li, T. (2024). Early identification of autism spectrum disorder based on machine learning with eye-tracking data. Journal of Affective Disorders, 358, 326–334. https://doi.org/10.1016/j.jad.2024.04.049
  • Wibble, T., & Pansell, T. (2024b). Human proprioceptive gaze stabilization during passive body rotations underneath a fixed head. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-68116-0 Zhou, Y., Yu, F., & Duong, T. (2014). Multiparametric MRI characterization and prediction in autism spectrum disorder using graph theory and machine learning. PLoS ONE, 9(6), e90405. https://doi.org/10.1371/journal.pone.0090405
  • Zuckerman, K. E., Hill, A. P., Guion, K., Voltolina, L., & Fombonne, E. (2014). Overweight and obesity: Prevalence and correlates in a large clinical sample of children with autism spectrum disorder. Journal of Autism and Developmental Disorders, 44(7), 1708–1719. https://doi.org/10.1007/s10803-014-2050-9
  • Zuckerman, K. E., Lindly, O. J., & Sinche, B. K. (2015). Parental concerns, provider response, and timeliness of autism spectrum disorder diagnosis. The Journal of Pediatrics, 166(6), 1431-1439.e1. https://doi.org/10.1016/j.jpeds.2015.03.007
  • Zwaigenbaum, L., Brian, J. A., & Ip, A. (2019). Early detection for autism spectrum disorder in young children. Paediatrics & Child Health, 24(7), 424–432. https://doi.org/10.1093/pch/pxz119
  • Zwaigenbaum, L., Bryson, S., Lord, C., Rogers, S., Carter, A., Carver, L., Chawarska, K., Constantino, J., Dawson, G., Dobkins, K., Fein, D., Iverson, J., Klin, A., Landa, R., Messinger, D., Ozonoff, S., Sigman, M., Stone, W., Tager-Flusberg, H., & Yirmiya, N. (2009). Clinical assessment and management of toddlers with suspected autism Spectrum Disorder: Insights from studies of High-Risk Infants. PEDIATRICS, 123(5), 1383–1391. https://doi.org/10.1542/peds.2008-1606
  • Zwaigenbaum, L., & Penner, M. (2018). Autism spectrum disorder: advances in diagnosis and evaluation. BMJ, k1674. https://doi.org/10.1136/bmj.k1674
  • Zwaigenbaum, L., Thurm, A., Stone, W., Baranek, G., Bryson, S., Iverson, J., Kau, A., Klin, A., Lord, C., Landa, R., Rogers, S., & Sigman, M. (2007). Studying the emergence of autism spectrum disorders in high-risk infants: Methodological and practical issues. Journal of Autism and Developmental Disorders, 37(3), 466–480. https://doi.org/10.1007/s10803-006-0179-x
Toplam 114 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Otistik Çocuklar Eğitimi, Otizm ve Spekrum Bozukluğu Eğitimi
Bölüm Makaleler
Yazarlar

Işık Akın Bülbül 0000-0001-5964-6082

Yayımlanma Tarihi 6 Eylül 2025
Gönderilme Tarihi 17 Nisan 2025
Kabul Tarihi 31 Ağustos 2025
Yayımlandığı Sayı Yıl 2025 Cilt: 7 Sayı: 1

Kaynak Göster

APA Akın Bülbül, I. (2025). Otizmin Semptom Öncesi Dönemde Tespiti: Göz İzleme Teknolojileri ve Görsel Dikkat Biyobelirteçleri. Turkish Journal of Special Education Research and Practice, 7(1), 1-28. https://doi.org/10.37233/trsped.drr.1678367