Geometric Deep Learning for Essential Tremor Screening Using

Abstract

[Abstract]: Essential tremor (ET) is a prevalent movement disorder characterized by motor and non-motor symptoms, often associated with neurodegeneration. Optical coherence tomography (OCT) has emerged as a valuable tool to identify retinal biomarkers in ET patients. This study presents a novel methodology for ET detection using 3D point clouds derived from retinal OCT layers. Leveraging advanced geometric deep learning (GDL) architectures, including PointTransformer, PointCNN, PointNet++ and SplineCNN, we evaluated the diagnostic potential of individual retinal layers, including the Retinal Nerve Fiber Layer (RNFL), Ganglion Cell Layer (GCL) and Bruch’s Membrane (BM), as well as their combined representation. Our approach achieved state-of-the-art results, with PointTransformer obtaining an F1-score of 0.85 using only BM retinal surface, while requiring just 2% of the original point cloud size. These findings underscore the diagnostic value of OCT-derived 3D data and demonstrate the potential of GDL for computational biomarker extraction in neurodegenerative disorders, offering a scalable and efficient framework for ET diagnosis.