Volumetric spline-based Kolmogorov-Arnold architectures surpass CNNs, vision transformers, and graph networks for Parkinson’s disease detection

ConvKAN architectures offer promising improvements for Parkinson's Disease detection from brain scans, particularly for early-stage cases where diagnosis is most challenging. The computational efficiency and strong performance across diverse datasets suggest potential for clinical implementation. These findings establish a framework for artificial intelligence-assisted diagnosis that could support earlier detection and intervention in Parkinson's Disease.