Glaucoma is the leading cause of irreversible blindness worldwide. The diagnosis process for glaucoma involves the measurement of the thickness of retinal layers in order to track its degeneration. The elongated shape of highly myopic eyes can hinder this diagnosis process, since it affects the OCT scanning process, producing deformations that can mimic or mask the degeneration caused by glaucoma. In this work, we present the first comprehensive cross-disease analysis that is focused on the anatomical structures most impacted in glaucoma and high myopia patients, facilitating precise differential diagnosis from those solely afflicted by myopia. To achieve this, a fully automatic approach for the retinal layer segmentation was specifically tailored for the accurate measurement of retinal thickness in both highly myopic and emmetropic eyes. To the best of our knowledge, this is the first approach proposed for the analysis of retinal layers in circumpapillary optical coherence tomography images that takes into account the elongation of the eyes in myopia, thus addressing critical diagnostic needs. The results from this study indicate that the temporal superior (mean difference 11.1μm, p<0.05), nasal inferior (13.1μm, p<0.01) and temporal inferior (13.3μm, p<0.01) sectors of the retinal nerve fibre layer show the most significant reduction in retinal thickness in patients of glaucoma and myopia with regards to patients of myopia.

Objectives: This study aimed to evaluate the diagnostic performance of peripapillary retinal nerve fiber layer (RNFL) thickness measurements corrected by artificial intelligence (AI) compared to original uncorrected values for glaucoma detection in highly myopic patients. Methods: This cross-sectional diagnostic accuracy study included 57 eyes from highly myopic patients (31 with glaucoma, 26 without glaucoma). Peripapillary RNFL parameters were obtained using Spectralis optical coherence tomography (OCT). A deep learning algorithm (MGU-Net) was employed to automatically segment retinal layers and compensate for scan tilt in elongated eyes, producing AI-corrected measurements. RNFL thickness values were extracted for six sectoral parameters (ST, SN, N, IN, T, IT) and global. Diagnostic performance was assessed using area under the ROC curve (AUC) and compared between corrected and uncorrected values. Multivariable logistic regression models were also developed using stepwise selection. Results: AI-corrected values were significantly lower than original measurements in all sectors (p < 0.001), with mean differences ranging from 15 to 35 µm. In glaucomatous eyes, significant thinning was observed in the global (p = 0.049) and inferior nasal (IN) sector (p = 0.037) among corrected values. The highest AUCs were found in IN (0.69), IT (0.67), and global (0.66) for corrected values, and in IT (0.63), T (0.59), and global (0.63) for uncorrected data. A model combining ST, T, and IT AI-corrected values achieved an AUC of 0.79. Conclusions: AI-corrected RNFL thickness measurements improve consistency and enhance diagnostic performance in highly myopic glaucoma patients. Correction algorithms may reduce false positives and help reveal glaucomatous damage otherwise obscured by myopic anatomical changes.