Gaze-Tracking-Based Tests for Autism in Children: A Diagnostic Test Accuracy Systematic Review and Meta-Analysis

Abstract

Abstract

Atypical gaze patterns are consistently reported in autism, reflecting differences in social attention and interest. Gaze-tracking paradigms provide an objective way to quantify these differences and may serve as early indicators of autism. This diagnostic test accuracy systematic review and meta-analysis evaluated the performance of eye-tracking-based gaze measures in children. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) guidance, studies published between 2015 and 2025 that compared gaze-tracking paradigms with standardized autism diagnoses were synthesized. Pooled diagnostic odds ratio (DOR), sensitivity, and specificity were estimated using random-effects and hierarchical summary receiver operating characteristic models. Risk of bias was assessed with QUADAS-2 and funnel plots. Seventeen studies ( n = 4,256) from six countries met the inclusion criteria. Tasks included social-geometric preference, motherese-nonsocial speech, and visual-orienting paradigms analyzed with rule-based or machine-learning methods. The pooled area under the hierarchical summary receiver operating characteristic curve (HSROC AUC) was 0.845; DOR 15.03 (95% CI 8.00–28.50); sensitivity 0.77 (95% CI 0.65–0.85); and specificity 0.80 (95% CI 0.75–0.84). Although heterogeneity was high ( I 2 = 87.78%), effect directions were consistent. Dynamic social stimuli and higher-frequency tracking systems achieved the best performance. Gaze-tracking tests distinguished autistic and nonautistic children across diverse settings, supporting their potential role as a quantitative, observer-independent adjunct for early identification and clinical decision support. Lay abstract Autism is a form of neurodiversity characterized by differences in social communication, sensory processing, and patterns of attention and interest, which often shape how autistic people look at and interpret the world around them. Eye-tracking technology records where a person looks on a screen and how long their gaze remains on elements, such as people, faces, or objects. Because it is objective and does not rely on language or complex instructions, eye-tracking may support earlier identification of autism. This study reviewed 17 research papers published between 2015 and 2025 that explored how eye-tracking distinguishes autistic and nonautistic children. Together, these studies included over 4,000 participants and compared attention to social scenes, like people talking or playing, with attention to nonsocial or geometric patterns. On average, eye-tracking correctly identified autism about 77% of the time and nonautistic children about 80% of the time, with the best results achieved with dynamic social videos and high-quality tracking cameras. These findings suggest that gaze-based measures capture meaningful differences in social attention and could complement existing diagnostic approaches through earlier, more objective assessment.