Programa del congreso
Resúmenes y datos de las sesiones para este congreso. Seleccione una fecha o ubicación para mostrar solo las sesiones en ese día o ubicación. Seleccione una sola sesión para obtener una vista detallada (con resúmenes y descargas, si están disponibles).
|
Resumen diario |
| Sesión | |
|
Mi-S3.1-GIB II: Premios SEIB-Fenin (II) Lugar: Salón de Actos Presidente de la sesión: Elisabete Aramendi Ecenarro Presidente de la sesión: Juan Carlos Antony García Presidente de la sesión: Pablo Laguna Trabajos seleccionados para el Premio SEIB-FENIN (estudiantes del Grado en Ingeniería Biomédica). | |
    | |
16:30 - 16:41
Deep learning segmentation for morphological assessment of optic nerve integrity in optic neuritis 1: BCN MedTech, Department of Engineering, Universitat Pompeu Fabra; 2: Neuroradiology Group, Vall d’Hebron Research Institute (VHIR); 3: Department of Neurology,Centre d’Esclerosi M´ultiple de Catalunya (Cemcat), Vall d’Hebron University Hospital; 4: Neuroradiology Section, Radiology Department (IDI), Vall d’Hebron University Hospital Studying the optic nerve is crucial for the diagnosis and monitoring of Multiple Sclerosis (MS), as optic neuritis is a frequent and often early manifestation of the disease. This research developed an automated pipeline to assess optic nerve integrity and detect lesions in MS patients using conventional Magnetic Resonance Imaging (MRI). A deep learning-based U-Net model was used to segment the optic nerve and generate T1/T2 ratio profiles along its trajectory. The segmentation model demonstrated robust performance, and the derived profiles provided clinically meaningful insights. At the eyelevel, the analysis successfully distinguished between affected and non-affected eyes, both globally and regionally. The T1/T2 ratio also showed significant correlations with established clinical measures such as retinal nerve fiber layer thickness, ganglion cell–inner plexiform layer thickness, and visual evoked potential latency. At the patient-level, inter-eye asymmetry provided strong classification ability (AUC of 0.83) for identifying individuals with or without unilateral lesions. These findings highlight the potential of the T1/T2 ratio as a reliable, non-invasive biomarker for axonal loss, complementing existing methods. By relying on conventional MRI, this automated approach offers an objective, reproducible, and clinically accessible tool for evaluating optic nerve integrity in MS.
| |