Abstract
Introduction: Histological morphometric analysis of retinal layers has inherent limitations while processing the specimen. A new in vivo technique, optical coherence tomography (OCT), has been developed that can be used to analyze and differentiate normal and pathological retina. To do a morphometric analysis of normal macula in the adult population of India and study its variations on the grounds of sex and age. Material and Methods: One hundred (200 Eyes) healthy adult subjects (18–65 years) underwent macular cube scanning using Zeiss spectral-domain OCT (SD-OCT). Macular thickness from all nine regions of the Early Treatment Diabetic Retinopathy study map was documented for each subject. Their variations for age and sex were determined manually and automatically. Statistical analysis was done by entering into an MS Excel sheet using IBM SPSS Statistics for Windows,Version 25.0. Armonk, NY: IBM Corp. (2017). The data were also analyzed using an independent t-test and analysis of variance. Results: The mean age of the subjects was 34.2 ± 13 (range, 19–65) years. The mean Central Subfield Thickness (CST) measured automatically (foveal thickness) and manually was 239.52 ± 22.9 μm and 167.75 ± 21.94 μm, respectively, while mean macular thickness was 284.73 ± 15.7 μm and 276.76 ± 14.84 μm. Males were associated with greater foveal, central foveal thickness, and mean macular thickness than females (P < 0.0001). There was no significant correlation of CST, outer and inner ring, and mean macular thickness with increasing age (>30 years). However, with respect to gender in the inner ring (parafoveal region), all the quadrants except the inferior quadrant, CST was significantly (P < 0.0001) higher in males than females while in the outer ring (perifoveal region), it was the temporal quadrant that had statistically significant higher CST in males compared to females. Discussion and Conclusion: The results will add evidence and can serve as a normal database in morphometry of macula in Indians, created and found significantly different in already fed normal comparative data in SD-OCT machines. It will help analyze morphometry of macula and understand macular pathologies in Indian eyes.
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