# MRI-based ASD Diagnosis

Following PRISMA, this review searched Scopus, Web of Science, and PubMed for 2015–2026 studies and included 86 Q1 journal articles, systematically mapping MRI-based machine learning and deep learning studies for autism spectrum disorder (ASD) classification across datasets, preprocessing pipelines, brain atlases, model architectures, and validation strategies; it finds fMRI is the most used modality with graph neural networks and transformers as dominant trends, and shows reported accuracy depends heavily on evaluation setup—single-site studies reach 87.4%–99.39%, the full ABIDE cohort sits around 70%–75%, and the strictest leave-one-site-out testing lands near 67%.

Following PRISMA, this review searched Scopus, Web of Science, and PubMed for 2015–2026 studies and included 86 Q1 journal articles, systematically mapping MRI-based machine learning and deep learning studies for autism spectrum disorder (ASD) classification across datasets, preprocessing pipelines, brain atlases, model architectures, and validation strategies; it finds fMRI is the most used modality with graph neural networks and transformers as dominant trends, and shows reported accuracy depends heavily on evaluation setup—single-site studies reach 87.4%–99.39%, the full ABIDE cohort sits around 70%–75%, and the strictest leave-one-site-out testing lands near 67%.