000171614 001__ 171614
000171614 005__ 20260527123126.0
000171614 0247_ $$2doi$$a10.1109/IJCNN64981.2025.11229335
000171614 0248_ $$2sideral$$a149394
000171614 037__ $$aART-2025-149394
000171614 041__ $$aeng
000171614 100__ $$aÁlvarez-Rodríguez, Lorena
000171614 245__ $$aGeometric Deep Learning for Essential Tremor Screening Using OCT-Derived 3D Point Clouds
000171614 260__ $$c2025
000171614 5203_ $$aEssential 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.
000171614 536__ $$9info:eu-repo/grantAgreement/ES/AEI/TED2021-131201B-I00$$9info:eu-repo/grantAgreement/ES/DGA/B23-23R$$9info:eu-repo/grantAgreement/EC/HORIZON EUROPE/101189689/EU/Human-Centred Machine Learning: Lighter, Clearer, Safer/ACHILLES$$9info:eu-repo/grantAgreement/ES/ISCIII/FORT23-00010$$9info:eu-repo/grantAgreement/ES/ISCIII/PI20-00437$$9info:eu-repo/grantAgreement/ES/ISCIII/PI23-00935$$9info:eu-repo/grantAgreement/ES/ISCIII/RD21-0002-0050$$9info:eu-repo/grantAgreement/ES/MICINN/PDC2022-133132-I00$$9info:eu-repo/grantAgreement/ES/MICINN/PID2023-148913OB-I00$$9info:eu-repo/grantAgreement/ES/MINECO-AEI/DPI2017-01726
000171614 540__ $$9info:eu-repo/semantics/closedAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000171614 655_4 $$ainfo:eu-repo/semantics/conferenceObject$$vinfo:eu-repo/semantics/publishedVersion
000171614 700__ $$ade Moura, Joaquim
000171614 700__ $$0(orcid)0000-0001-9411-5834$$aVilades, Elisa$$uUniversidad de Zaragoza
000171614 700__ $$0(orcid)0000-0001-6258-2489$$aGarcia-Martin, Elena$$uUniversidad de Zaragoza
000171614 700__ $$aNovo, Jorge
000171614 700__ $$aOrtega, Marcos
000171614 7102_ $$11013$$2646$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Oftalmología
000171614 773__ $$g(2025), [8 pp.]$$pProc. Int. Jt. Conf. Neural Netw.$$tProceedings of ... International Joint Conference on Neural Networks$$x2161-4393
000171614 8564_ $$s1267750$$uhttps://zaguan.unizar.es/record/171614/files/texto_completo.pdf$$yVersión publicada
000171614 8564_ $$s3377558$$uhttps://zaguan.unizar.es/record/171614/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000171614 909CO $$ooai:zaguan.unizar.es:171614$$particulos$$pdriver
000171614 951__ $$a2026-05-27-11:24:57
000171614 980__ $$aARTICLE