Este trabajo compara las arquitecturas nnU-Net, MGU-Net y SDLayer-Net en la segmentación de las capas de la retina en imágenes de Tomografía por Coherencia Óptica. Se propone una normalización anatómica centrada en la fóvea con el objetivo de mejorar la capacidad de generalización, así como una nueva metodología de evaluación basada en las violaciones topológicas de la disposición de las capas.

Background: The latest revision of the McDonald criteria for diagnosis of multiple sclerosis (MS) establishes that the optic nerve can serve as a fifth anatomical location within the central nervous system for diagnosis. Optical coherence tomography (OCT) images can serve as evidence for this purpose. Objective: To assess the accuracy of automated artificial-intelligence-based classification of MS patients using OCT data obtained from two different centers. Methods: OCT data were collected from two centers using standardized APOSTEL-based protocols and similar equipment. Retinal layer thicknesses—mean and standard deviation (STD) values—were analyzed in four layers and in six regions per layer per eye. A support vector machine classifier with recursive feature elimination and Shapley additive explanations value analysis was applied to identify the most relevant features and maximize classification accuracy between control subject and MS patient eyes. Results: The database drawn from two hospitals comprised 112 eyes with MS without prior history of optic neuritis and 193 eyes of control subjects. The classifier achieved maximum accuracy (0.8459) using 20 input features. The mean and STD metrics had similar importance, with the most influential layers being the ganglion cell layer, inner plexiform layer, and the inner retinal layer complex. Key regions included the papillomacular bundle and the superior temporal perimacular area. Conclusions: OCT data facilitates highly accurate MS diagnosis across different centers. Artificial intelligence assessment could facilitate automated classification. These findings provide evidence of the important role of the optic nerve in MS diagnosis.

Background/Objectives: To evaluate long-term neuroretinal changes in patients with persistent COVID-19 (PC) versus asymptomatic controls, using optical coherence tomography (OCT) over a two-year follow-up. Methods: This prospective longitudinal study enrolled 133 participants: 94 PC patients and 39 control subjects. Participants underwent comprehensive ophthalmological assessment at baseline, at 1 year (voluntary), and at 2 years. Structural retinal changes were analysed using spectral-domain OCT with the Bruch’s Membrane Opening–Minimum Rim Width and Posterior Pole protocols. Longitudinal analyses focused on within-subject change from baseline to 2 years and on between-group comparisons of these change scores, with Bonferroni correction applied within predefined families of comparisons. Results: The most robust corrected between-group longitudinal finding was a greater decrease in the central macular sector (C0) of the inner plexiform layer (IPL) in PC patients than in control subjects. Conclusions: Although these findings suggest possible subtle inner retinal involvement in PC patients, particularly in the central IPL, the results should be interpreted cautiously because of the modest control sample, control-group heterogeneity, and the exploratory nature of the sectoral OCT analyses. OCT may be useful in research settings as a complementary tool for exploring subtle neuroretinal changes in PC patients; however, its clinical utility and role as a validated biomarker remain unproven.