Search results for " tomografía de coherencia óptica"
showing 3 items of 3 documents
Intravitreal therapies for non-neovascular age-related macular degeneration with intraretinal or subretinal fluid
2017
OBJECTIVE: To evaluate the efficacy of intravitreal therapies in cases of atrophic age-related macular degeneration (AMD) with subretinal or intraretinal fluid. METHODS: A retrospective review was made of the clinical charts of patients diagnosed with atrophic AMD with subretinal or intraretinal fluid. Fundus photographs and spectral-domain optical coherence tomography images were examined, and an analysis was made on the presence of fluid and its density. Neovascularisation was ruled out by fluorescein and/or indocyanine green angiography. RESULTS: The study included 14 eyes from 13 patients with a mean age of 72.64 years and a mean follow-up of 80.5 weeks. Intraretinal fluid was observed …
Cirugía no penetrante del glaucoma con espolonectomía. Valoración anatómica y funcional.
2016
INTRODUCCIÓN El glaucoma es una neuropatía óptica multifactorial que cursa con una pérdida progresiva de campo visual. La presión intraocular (PIO) es el factor de riesgo más importante y el parámetro modificable que mejor permite variar la evolución natural de la enfermedad. El tratamiento médico permite un control de la enfermedad en la mayoría de los casos, pero en ocasiones es necesario recurrir al tratamiento quirúrgico para producir descensos de la presión intraocular que eviten la progresión del daño. La trabeculectomía es el técnica quirúrgica de referencia, pero han ido apareciendo diferentes opciones con la finalidad de mantener los buenos resultados tensionales disminuyendo las c…
Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography
2019
Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal d…