Álvarez-Rodríguez, LorenaMoura, Joaquim deViladés, ElisaGarcía-Martín, ElenaNovo Buján, JorgeOrtega Hortas, Marcos2025-11-182025-11-182025-11-14L. Álvarez-Rodríguez, J. de Moura, E. Vilades, E. Garcia-Martin, J. Novo and M. Ortega, "Geometric Deep Learning for Essential Tremor Screening Using OCT-Derived 3D Point Clouds," 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 2025, pp. 1-8, doi: 10.1109/IJCNN64981.2025.11229335979-8-3315-1042-82161-4407https://hdl.handle.net/2183/46475This version of the article has been accepted for publication, after peer review. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The Version of Record is available online at: https://doi.org/10.1109/IJCNN64981.2025.11229335 Traballo presentado en: 2025 International Joint Conference on Neural Networks (IJCNN), Rome, Italy, 30 June - 05 July 2025[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.eng© 2025 IEEE.Geometric deep learningEssential TremorOptical Coherence TomographyRetinal ImagingNeural Network Applicationsconference outputopen access10.1109/IJCNN64981.2025.11229335