Abstract
To evaluate the physiological changes related with age of all retinal layers thickness measurements in macular and peripapillary areas in healthy eyes. Wide protocol scan (with a field of view of 12x9 cm) from Triton SS-OCT instrument (Topcon Corporation, Japan) was performed 463 heathy eyes from 463 healthy controls. This protocol allows to measure the thickness of the following layers: Retina, Retinal nerve fiber layer (RNFL), Ganglion cell layer (GCL +), GCL++ and choroid. In those layers, mean thickness was compared in four groups of ages: Group 1 (71 healthy subjects aged between 20 and 34 years); Group 2 (65 individuals aged 35-49 years), Group 3 (230 healthy controls aged 50-64 years) and Group 4 (97 healthy subjects aged 65-79 years). The most significant thinning of all retinal layers occurs particularly in the transition from group 2 to group 3, especially in temporal superior quadrant at RNFL, GCL++ and retinal layers (p≤0.001), and temporal superior, temporal inferior, and temporal half in choroid layer (p<0.001). Curiously group 2 when compared with group 1 presents a significant thickening of RNFL in temporal superior quadrant (p = 0.001), inferior (p<0.001) and temporal (p = 0.001) halves, and also in nasal half in choroid layer (p = 0.001). Excepting the RNFL, which shows a thickening until the third decade of life, the rest of the layers seem to have a physiological progressive thinning.
Highlights
OCT is widely used in clinical practice and clinical trials accepting their measurements for the evaluation of the response to treatment and the progression of pathologies [1]
Group 2 when compared with group 1 presents a significant thickening of Retinal nerve fiber layer (RNFL) in temporal superior quadrant (p = 0.001), inferior (p
Excepting the RNFL, which shows a thickening until the third decade of life, the rest of the layers seem to have a physiological progressive thinning
Summary
OCT is widely used in clinical practice and clinical trials accepting their measurements for the evaluation of the response to treatment and the progression of pathologies [1]. Retinal thickness or central macular thickness (CMT) measured with OCT is used, which correlates with pathological changes and response to treatment for a variety of eye diseases [2]. We can classify the existing segmentation algorithms into two clusters, mathematical modeling and machine learning approaches. Pure machine learning algorithms for retinal layer segmentation classifies each pixel from an image on how they fall under a particular layer or boundary, that means that boundaries between layers are not linear [2]
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