Biomarcadores de oclusiones venosas retinianas mediante estrategia de aprendizaje profundo aplicada en imágenes adquiridas por OCT angiografía
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Gallego Suárez, Laura Juliana
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Propósito: Desarrollar un método computacional basado en Deep Learning (DL) para detectar automáticamente biomarcadores de oclusiones de venas retinianas en imágenes adquiridas por angiografía por tomografía de coherencia óptica (OCT-A)
Diseño: Desarrollo de algoritmo para detectar biomarcadores de oclusiones de venas retinianas utilizando datos retrospectivos. (Texto tomado de la fuente)
Abstract
Purpose: To develop a computational method based on Deep Learning (DL) to
automatically detect biomarkers of retinal vein occlusions in images acquired by optical
coherence tomography angiography (OCT- A)
Design: Algorithm development for detect biomarkers of retinal vein occlusions using
retrospective data.
Participants: Images of the superficial, deep, en face, choriocapillaris and outer retina
to choriocapillaris (ORCC) layers obtained from 254 patients attended in an Ophthalmology
Clinic were used to train and test an artificial intelligence (AI) model.
Methods: The OCT-A scans were manually annotated with four biomarkers (BMs):
disruption of the perifoveal capillary plexus, non-perfusion areas (NPAs), vascular
tortuosity and cystoid spaces. Segmentation and identification were subsequently provided
to build and training the DL model using Deep Convolutional Neural Networks (DNN)
Main Outcome Measures: detection rate and jaccard index
Results: The detection rate of the model for disruption of the perifoveal capillary plexus,
non-perfusion areas (NPAs), vascular tortuosity and cystoid spaces were 93%, 92%, 91%
and 84% respectively. The Jaccard index values were 0.85, 0.77, 0.72 and 0.73
respectively
Conclusion: The proposed DL model may id
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