aceAre the Eyes a Mirror of Autism? Explainable AI-Based Biomarker Discovery for Diagnosis
Autism spectrum disorder (ASD) presents significant difficulties in diagnosis due to its nature and diversity of symptoms. Traditional diagnostic methods rely on comprehensive clinical evaluations and complex diagnostic processes. Recent advances in machine learning (ML) and explainable artificial intelligence (XAI) offer promising models that help improve diagnostic accuracy and efficiency in ASD. XAI methods provide explainability by determining feature importance and indicating the extent to which features im…
Using explainable AI to select eye-tracking features improved autism classification over traditional feature selection and surfaced four candidate biomarkers that traditional methods missed.
The biomarkers are described only as candidates, and the best AUC of 0.78 indicates moderate rather than definitive diagnostic performance.
Evidence
- Peer-reviewedApplied Sciences2026-10-19
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Truvace Impact Record TRV-2026-2018, v1: “Are the Eyes a Mirror of Autism? Explainable AI-Based Biomarker Discovery for Diagnosis.” Truvace, 2026-10-20. /record/TRV-2026-2018 (accessed at citation time). sha256 56d2ccc5179bba94…
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