WISE-Screen: A Smartphone-Based Analytical Framework for Automated ASD Screening and Phenotyping via High-Fidelity Eye-tracking

The rising prevalence of autism spectrum disorder (ASD) strains clinical infrastructure. Gold-standard tools like ADOS-2 face high costs, specialized training requirements, and extensive waitlists, delaying diagnosis and intervention. While eye-tracking offers a promising digital biomarker, existing tools lack scalable community deployment due to hardware costs and operational constraints. Here, we introduce the WISE-Screen framework, a smartphone-based real-time architecture for autonomous ASD Screening and multidimensional phenotypic profiling, evaluating its conceptual feasibility across a development-tally diverse age range. Two machine learning pipelines processed smartphone-captured eye-gaze data: (1) a Scanpath-based (SP) pipeline utilizing saliency maps and engineered scanpath features across 34 stimuli to estimate ASD-typical gaze probabilities, and (2) a Domain-task-based (DT) pipeline evaluating responses to 17 specialized tasks across four phenotypic domains (social, emotional, sensory, executive). Models were evaluated using leave-one-out cross-validation on 35 participants (16 ASD, 19 Non-ASD, ages 2.5-17) with ADOS-2 confirmed status. Compared to a baseline demographic model (ROC-AUC = 0.82; 95% CI: 0.68-0.96), performance improved using SP model (ROC-AUC = 0.90; 95% CI: 0.78-1.00) and DT model (ROC-AUC = 0.88; 95% CI: 0.75-1.00), with the integrated model reaching a peak ROC-AUC of 0.91 (95% CI: 0.80-1.00). Age- and sex-residualized models maintained an adjusted ROC-AUC of 0.74 (95% CI:0.57-0.92), with sensory, social and emotional domains showing the strongest association. WISE-Screen offers a scalable, automated adjunct to traditional protocols, providing accessible digital phenotyping to overcome systemic ASD screening barriers, though further evaluation in larger cohorts is warranted.