Machine learning-based diagnosis of autism spectrum disorder in children and adolescents using eye-tracking data: a...

Eye-tracking technology has been increasingly investigated as an objective approach for distinguishing individuals with Autism Spectrum Disorder (ASD) from typically developing (TD) individuals. Artificial intelligence and machine learning (ML) methods have been widely applied to support ASD diagnosis and treatment, and prior studies suggest that ML models leveraging eye-tracking data can achieve high diagnostic accuracy. This systematic review and meta-analysis aimed to evaluate the diagnostic performance of machine-learning models using eye-tracking data to distinguish children and adolescents with ASD from TD peers.

We systematically searched PubMed, Embase, Web of Science, IEEE Xplore, Scopus, and the Cochrane Library from inception to August 3, 2025. We included studies that applied ML methods to eye-tracking data to distinguish children with ASD from TD children. We extracted data on participant characteristics, model performance, eye-tracking protocols, and machine-learning algorithms. The review protocol was registered in PROSPERO (CRD420251162462).

We identified 1,045 records, of which 25 studies were included in the meta-analysis. The included studies comprised 2,319 participants, with sample sizes ranging from 32 to 529 per study. The pooled accuracy, sensitivity, and specificity of machine-learning models using eye-tracking data to distinguish children with ASD from TD children were 85 % (95 % CI, 81-89 %), 86 % (95 % CI, 82-89 %), and 86 % (95 % CI, 79-91 %), respectively. These results suggest that eye-tracking-based machine-learning approaches have good diagnostic performance for identifying ASD.

Eye-tracking-based machine-learning approaches show considerable potential for distinguishing children with ASD from TD children. However, the robustness and generalizability of these findings are limited by the lack of external validation, small sample sizes, and substantial between-study heterogeneity. To establish generalizability, future research should prioritize standardized eye-tracking paradigms and large-scale, prospective, multicenter study designs with external validation. Such efforts may facilitate the translation of these models into clinical practice as objective and efficient adjunctive screening tools.