Decision trees for glaucoma screening based on the asymmetry of the retinal nerve fiber layer in optical coherence tomography
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Berenguer Vidal, Rafael; Verdú Monedero, Rafael; Morales Sánchez, Juan; Sellés Navarro, Inmaculada; Kovalyk, Oleksandr; [et al.]Área de conocimiento
Teoría de la Señal y las ComunicacionesPatrocinadores
This work has been partially supported by Spanish National projects AES2017-PI17/00771, AES2017-PI17/00821 (Instituto de Salud Carlos III), and Regional project 20901/PI/18 (Fundación Séneca).Fecha de publicación
2022-06-27Editorial
MDPICita bibliográfica
Berenguer-Vidal, R., Verdú-Monedero, R., Morales-Sánchez, J., Sellés-Navarro, I., Kovalyk, O., Sancho-Gómez, J.-L. (2022). Decision Trees for Glaucoma Screening Based on the Asymmetry of the Retinal Nerve Fiber Layer in Optical Coherence Tomography. Sensors, 22(13), 4842. https://doi.org/10.3390/s22134842Revisión por pares
siPalabras clave
Optical coherence tomography (OCT)Automatic layer segmentation
Retinal imaging analysis
Mathematical morphology
Active contours
Glaucoma
Resumen
Purpose: The aim of this study was to analyze the relevance of asymmetry features between both eyes of the same patient for glaucoma screening using optical coherence tomography. Methods: Spectral-domain optical coherence tomography was used to estimate the thickness of the peripapillary retinal nerve fiber layer in both eyes of the patients in the study. These measurements were collected in a dataset from healthy and glaucoma patients. Several metrics for asymmetry in the retinal nerve fiber layer thickness between the two eyes were then proposed. These metrics were evaluated using the dataset by performing a statistical analysis to assess their significance as relevant features in the diagnosis of glaucoma. Finally, the usefulness of these asymmetry features was demonstrated by designing supervised machine learning models that can be used for the early diagnosis of glaucoma. Results: Machine learning models were designed and optimized, specifically decision trees, based on the values ...
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