Explore the vast range of titles available.

Various imaging features on optical coherence tomography (OCT) are crucial for identifying and defining disease progression. Establishing a consensus on these imaging features is essential, particularly for training deep learning models for disease classification. This study aims to analyze the inter-rater reliability in labeling the quality and common imaging signatures of retinal OCT scans. 500 OCT scans obtained from CIRRUS HD-OCT 5000 devices were displayed at 512x1024x128 resolution on a customizable, in-house annotation software. Each patient's eye was represented by 16 random scans. Two masked reviewers independently labeled the quality and specific pathological features of each scan. Evaluated features included overall image quality, presence of fovea, and disease signatures including subretinal fluid (SRF), intraretinal fluid (IRF), drusen, pigment epithelial detachment (PED), and hyperreflective material. The raw percentage agreement and Cohen's kappa (κ) coefficient were used to evaluate concurrence between the two sets of labels. Our analysis revealed κ = 0.60 for the inter-rater reliability of overall scan quality, indicating substantial agreement. In contrast, there was slight agreement in determining the cause of poor image quality (κ = 0.18). The binary determination of presence and absence of retinal disease signatures showed almost complete agreement between reviewers (κ = 0.85). Specific retinal pathologies, such as the foveal location of the scan (0.78), IRF (0.63), drusen (0.73), and PED (0.87), exhibited substantial concordance. However, less agreement was found in identifying SRF (0.52), hyperreflective dots (0.41), and hyperreflective foci (0.33). Our study demonstrates significant inter-rater reliability in labeling the quality and retinal pathologies on OCT scans. While some features show stronger agreement than others, these standardized labels can be utilized to create automated machine learning tools for diagnosing retinal diseases and capturing valuable pathological features in each scan. This standardization will aid in the consistency of medical diagnoses and enhance the accessibility of OCT diagnostic tools.

Retinal diseases, major causes of vision impairment and blindness, are assessed using optical coherence tomography (OCT) scans. Automated report generation for retinal OCT scans, powered by deep learning, can help standardize interpretations and track retinal disease in clinical settings. A key challenge is accurately segmenting retinal disease signatures. This study explores using the diffusion model to segment subretinal fluid (SRF), intraretinal fluid (IRF), and pigment epithelial detachment (PED) in typical clinical settings, comparing their performance to other leading segmentation models. We labeled OCT scans and extracted those with specific pathologic retinal features: 269 scans with SRF, 224 scans with IRF, and 114 scans with PED. Three trained reviewers manually segmented these features for downstream analysis. Using manually segmented scans as the ground truth, we trained the diffusion model, Nested U-Net, nnU-Net, TransUNet, and SwinUNet to predict these segmentations. All models were evaluated using 5-fold cross-validation, with performance measured by Dice coefficient, sensitivity, specificity, Pearson correlation coefficient, and R2. All models show high similarly with ground truth segmentations in predicting SRF, IRF, and PED, as shown by the Dice coefficient (Diffusion model: 0.81 ± 0.12, 0.66 ± 0.09, 0.75 ± 0.11). The diffusion model has relatively higher sensitivity compared to most other models, while all models display very high specificity. The Pearson correlation coefficient and R2 values show strongly associated pixel quantification of segmented areas for models, with the nnU-Net model performing the strongest overall. This study demonstrates that while diffusion models can comparably segment retinal pathologies using a limited number of manually annotated scans, the nnU-Net model remains the most effective overall for automated OCT analysis.

Purpose: To report the diurnal variation in choroidal vascularity index (CVI) in subfoveal (SF-CVI) and peripapillary area in healthy eyes. Methods: The study was a cross-sectional study including 12 healthy subjects. Swept-source optical coherence tomography scans were taken at 9 am, 11 am, 1 pm, 3 pm, and 5 pm. Subfoveal choroidal thickness (SFCT) and CVI were calculated using automated segmentation techniques and previously validated algorithms. Systemic parameters including systolic blood pressure (SBP), diastolic blood pressure, mean arterial pressure, and mean ocular perfusion pressure were calculated and correlated with SFCT and CVI. Results: A total of 12 eyes (right eye) of 12 patients (mean age: 26 ± 3.77 years) were analyzed. The mean (±standard deviation) amplitude of SFCT and SF-CVI variation was 35.91 ± 14.8 μm (range, 15-69 μm) and 0.05 ± 0.02 (range, 0.02-0.08). The mean CVI showed a significant diurnal variation in the temporal quadrant of the peripapillary region (P = 0.02). Conclusion: SFCT and SF-CVI showed a significant diurnal variation in amplitude (peak-trough analysis) and SF-CVI correlated well with SBP suggestive of a direct influence of blood pressure on choroidal vascularity. The mean peripapillary CVI in the temporal quadrant also showed a significant diurnal variation with no significant change in other quadrants.

To evaluate structural changes in the choroid of patients with retinitis pigmentosa (RP) using swept-source optical coherence tomography (OCT) scans. A prospective study was conducted comparing 35 eyes of 35 patients with RP and 26 eyes of 26 normal patients. OCT images of the choroid were binarized into luminal and stromal areas to derive choroidal vascularity index (CVI). Subfoveal choroidal thickness (CT) was also measured and compared. There was a significant decrease in the mean CVI among eyes with RP as compared to normal eyes (56.91 ± 1.43% vs. 59.47 ± 1.55%; P < .0001). Mean subfoveal CT was significantly greater in eyes with RP as compared to normal eyes (262.82 μm ± 69.69 μm vs. 194.65 μm ± 23.55 μm; P < .0001). Patients with RP showed a significant reduction in CVI and an increase in CT as compared to normal eyes. [Ophthalmic Surg Lasers Imaging Retina. 2018;49:191-197.].

Purpose: This study aims to compare the effect of laser photocoagulation or observation on choroidal vascularity in acute central serous chorioretinopathy (CSCR). Methods: A retrospective analysis of 30 patients with acute CSCR treated either with laser photocoagulation (16 eyes) or sham laser (14 eyes) was performed. Demographic details, visual acuity (VA) assessment, and other relevant clinical data were considered from baseline to the 3rd and 6th month follow-up visits. Participants with chronic CSCR and missing follow-up or inadequate data were excluded. Choroidal analysis including choroidal thickness and choroidal vascularity index (CVI) assessment was done for each visit using Spectral Domain (SD) Optical Coherence Tomography (OCT) images. Results: In laser arm group, there was a statistically significant change in VA, contrast sensitivity and central macular thickness (CMT) and neurosensory detachment (NSD) (P < 0.05) at the 3rd and 6th month visits. However, there was no statistically significant difference in subfoveal choroidal thickens (SFCT) and CVI (P > 0.05) at both the visits. In sham laser group, similarly, there was a significant improvement in VA, contrast sensitivity, CMT, and CVI (P < 0.05) at the 3rd and 6th month visits. There was significant reduction in NSD at the 3rd month; however, it was not statistically significant at the 6th month visit. SFCT did not change significantly at both the visits. There was no significant difference for the changes in parameters between the groups at the 6th month. Regression analysis showed no significant correlation with final VA with any of the baseline parameters. Conclusion: Early laser photocoagulation does lead to change in choroidal morphology, though insignificant, in comparison to observation. The present data, yet again, support no additional benefit of early laser photocoagulation in acute CSCR.

Purpose: To report the prevalence of pachydrusen in Indian population and their characteristics in relation to subfoveal choroidal thickness (SFCT), choroidal vascularity index (CVI) in comparison to eyes with soft drusen and subretinal drusenoid deposits (SDD) in age-related macular degeneration (AMD). Methods: The study was a retrospective, cross-sectional study involving patients with a diagnosis of dry AMD in at least one eye. The diagnosis of soft drusen, SDD, and pachydrusen was made on the basis of color fundus photograph and optical coherence tomography (OCT). SFCT and CVI was calculated and compared among the different subtypes of drusen. Results: A total of 169 eyes (143 dry and 26 wet AMD) of 85 patients with a mean age of 67.67 ± 9.57 years were included. In eyes with dry AMD, pachydrusen were seen in 12 eyes (8.4%) with a mean (±SD) SFCT of 289.66 ± 91.01 μ. The difference in SFCT was statistically significant (P = 0.001) using analysis of variance (ANOVA) test. The eyes with pachydrusen had significantly thickened choroid compared to the eyes with SDD (30 eyes; 21.0%) or combination of soft drusen and SDD (29 eyes; 20.3%) but not soft drusen (72 eyes; 50.3%). The difference of CVI in different subgroups was significant (P = 0.03). One eye in wet AMD group had concurrent pachydrusen. Comparison of SFCT and CVI in wet AMD and fellow dry AMD eyes were not significant. Conclusion: In Indian eyes with dry AMD, prevalence of pachydrusen (8.4%) is slightly lower compared to western literature (11.7%) and is associated with thicker choroid and higher CVI.

Purpose: To compare the accuracy of manual and automated binarization technique for the analysis of choroidal vasculature. Methods: This retrospective study was performed on a total of 98 eyes of 60 healthy subjects. Fovea-centered swept source optical coherence tomography (SS-OCT) scans were obtained and choroidal area was binarized using manual and automated image binarization technique separately. Choroidal vessel visualization in the binarized scans were subjectively graded (grades 0-100) by comparing them with the original OCT scan images by two masked graders. The subjective variability and repeatability was compared between two binarization method groups. Intergrader and intragrader variability was estimated using paired t-test. The degree of agreement between the grades for each observer and between the observers was evaluated using Bland-Altman plot. Results: The mean accuracy grades of the automatically binarized images were significantly (P < 0.001) higher (93.38% ± 1.70%) than that of manually binarized images (78.06% ± 2.92%). There was a statistically significant variability and poor agreement between the mean interobserver grades in the manual binarization arm. Conclusion: Automated image binarization technique is faster and appears to be more accurate in comparison to the manual method.

Purpose: To investigate choroidal hyper-reflective foci (HRF) in subjects with retinal dystrophy [Stargardt's disease (SGD) and retinitis pigmentosa (RP)] and their association with demographics, visual acuity, choroidal thickness (CT), and choroidal vascularity index (CVI). Methods: Single center retrospective study of subjects with previously diagnosed SGD or RP. Swept-source optical coherence tomography images were analyzed for the presence of choroidal HRFs and CVI using previously validated automated algorithm. A Spearman's rank correlation coefficient was used to evaluate the correlation between the number of HRF and various baseline parameters including age, visual acuity, intraocular pressure, and other optical coherence tomography (OCT) parameters (CT, choroidal area, and CVI) were evaluated in these subjects. Results: This study included 46 eyes (23 subjects) and 55 eyes (28 subjects) with previously diagnosed RP and SGD, respectively. In the RP group, the mean number of HRFs was 247.9 ± 57.1 and mean CVI was 0.56 ± 0.04. In SGD group, mean HRF was 192.5 ± 44.3 and mean CVI was 0.41 ± 0.04. Mean HRF was significantly greater in the RP group (0.02), however, the mean CVI was not statistically different. In RP, mean HRF were correlated only with CVI (r = 0.49; P = 0.001), however, in SGD, it correlated with only choroidal area (r = 0.27; P = 0.04). Conclusion: Choroidal HRF were present in both RP and SGD subjects with more HRFs in those with RP. These HRFs were associated with alteration in choroidal vascularity, which further adds into the pathogenesis of these diseases.

Purpose: To report the prevalence of pachydrusen and their relationship with subfoveal choroidal thickness (SFCT) and large choroidal vessel layer thickness (SF-LCVT) in eyes with polypoidal choroidal vasculopathy (PCV) and their fellow eyes. Methods: The case records of 50 patients (99 eyes; 59 PCV and 40 fellow eyes) were retrospectively analyzed for the presence of pachydrusen and other drusen types such as soft drusen. The diagnosis was established using colour fundus photography and optical coherence tomography (OCT). SFCT and SF-LCVT were measured and correlated with the different types of drusen. Results: The mean age of the study cohort was 62.26 ± 10.67 years and included 27 males and 23 females. Pachydrusen and soft drusen were seen in 14 (PCV: 8 and fellow eyes: 6) and 8 eyes (PCV: 2 and fellow eyes: 6) respectively. The mean SFCT and SF-LCVT in the eyes with and without pachydrusen was not significanty different (280.29 ± 103.11 μ vs. 292.63 ± 87.17 μ; P = 0.63 and 180.57 ± 59.20 vs. 173.73 ± 54.86 μ; P = 0.67, respectively). The pachydrusen were most commonly located near the vascular arcades and showed scattered distribution pattern. Though SFCT and SF-LCVT was lower in the eyes with soft drusen compared to eyes with pachydrusen, it failed to reach statistical significance (SFCT, P = 0.1 and SF-LCVT, P = 0.06). Conclusion: The prevalence of pachydrusen in PCV and their fellow eyes is lower in Indian population suggestive of ethnic variations. SFCT and SF-LCVT was not noted to vary signifcantly in eyes with and without pachydrusen in this study cohort.

To assess the choroidal vessels in healthy eyes using a novel three-dimensional (3D) deep learning approach. In this cross-sectional retrospective study, swept-source OCT 6 × 6 mm scans on Plex Elite 9000 device were obtained. Automated segmentation of the choroidal layer was achieved using a deep-learning ResUNet model along with a volumetric smoothing approach. Phansalkar thresholding was employed to binarize the choroidal vasculature. The choroidal vessels were visualized in 3D maps, and divided into five sectors: nasal, temporal, superior, inferior, and central. Choroidal thickness (CT) and choroidal vascularity index (CVI) of the whole volumes were calculated using the automated software. The three vessels for each sector were measured, to obtain the mean choroidal vessel diameter (MChVD). The inter-vessel distance (IVD) was defined as the distance between the vessel and the nearest non-collateral vessel. The choroidal biomarkers obtained were compared between different age groups (18 to 34 years old, 35 to 59 years old, and ≥ 60) and sex. Linear mixed models and univariate analysis were used for statistical analysis. A total of 80 eyes of 53 patients were included in the analysis. The mean age of the patients was 44.7 ± 18.5 years, and 54.7% were females. Overall, 44 eyes of 29 females and 36 eyes of 24 males were included in the study. We observed that 33% of the eyes presented at least one choroidal vessel larger than 200 μm crossing the central 3000 μm of the macula. Also, we observed a significant decrease in mean CVI with advancing age ( p  < 0.05), whereas no significant changes in mean MChVD and IVD were observed ( p  > 0.05). Furthermore, CVI was increased in females compared to males in each sector, with a significant difference in the temporal sector ( p  < 0.05). MChVD and IVD did not show any changes with increasing age, whereas CVI decreased with increasing age. Also, CVI was increased in healthy females compared to males. The 3D assessment of choroidal vessels using a deep-learning approach represents an innovative, non-invasive technique for investigating choroidal vasculature, with potential applications in research and clinical practice.