-- Batten disease (neuronal ceroid lipofuscinosis) is a rare pediatric neurodegenerative disorder whose early MRI signs are subtle and often missed. We propose TinyViT-Batten, a few-shot Vision Transformer (ViT) framework to detect early Batten disease from pediatric brain MRI with limited training cases. Our model achieves high accuracy ( 91%) and area under ROC 0.95 on a multi-site dataset of 79 genetically confirmed Batten-disease MRIs (27 CLN3 from the Hochstein natural-history study, 32 CLN2 from an international longitudinal cohort, 12 early-manifestation CLN2 cases reported by Çokal et al., and 8 public Radiopaedia scans) together with 90 age-matched controls, outperforming a 3D-ResNet and Swin-Tiny baseline. We further integrate Gradient-weighted Class Activation Mapping (Grad-CAM) to highlight disease-relevant brain regions, enabling explainable predictions. The model ' s small size and strong performance (sensitivity >90%, specificity 90%), demonstrates a practical AI solution for early Batten disease detection. Batten disease, or neuronal ceroid lipofuscinosis (NCL), comprises a group of rare lysosomal storage disorders that cause progressive neurodegeneration in children [1]. Early signs on brain MRI can include subtle cerebral and cerebellar atrophy and faint white-matter signal changes. However, these findings are often non-specific and easily overlooked [1]. Early detection of Batten disease is critical--recently an enzyme replacement therapy was approved for CLN2 (late-infantile NCL) [3] and gene therapies for other subtypes are in trials.

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