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

Abstract

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.

Keywords

• Autism
• eye-tracking technology
• visual attention
• biomarkers
• machine learning.

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

Abstract

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.

Keywords

• Otizm
• göz izleme teknolojisi
• görsel dikkat
• biyobelirteçler
• makine öğrenmesi.

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