Assessment of changes in macular structural retinal layers in patients with pathological myopia.

Background/aim: This study aimed to examine changes in the thickness of individual macular retinal layers in eyes with pathological myopia (PM) and to compare the thickness of each retinal layer between the PM and control groups to gain insights into retinal perfusion. Materials and methods: The study included 51 eyes in the PM group and 51 eyes in the control group. Optical coherence tomography (OCT) was used to measure the thickness of each retinal layer in the central fovea, parafoveal, and perifoveal regions. Optical coherence tomography angiography (OCT-A) was used to evaluate the retinal capillary density. Results: In the PM group, the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), and inner nuclear layer (INL) were thicker than in the control group (p = 0.004, p = 0.027, p = 0.020, and p < 0.001, respectively), whereas the outer nuclear layer (ONL) and photoreceptor layer (PRL) were thinner (p = 0.001 and p = 0.003, respectively). In other regions, the RNFL was thicker in the myopic group, whereas the GCL, IPL, INL, and ONL were thinner. OCT-A did not reveal any significant difference between the groups in terms of radial capillary plexus density (p = 0.381); however, the densities of the other plexuses were lower in the PM group. Conclusions: The results showed alterations in the thickness of retinal layers and capillary plexus density in PM. Thus, assessment of the thickness of individual retinal layers may serve as an indicator of vascular diseases that affect the circulation of the retina and choroid.

A Detailed Systematic Review on Retinal Image Segmentation Methods.

The separation of blood vessels in the retina is a major aspect in detecting ailment and is carried out by segregating the retinal blood vessels from the fundus images. Moreover, it helps to provide earlier therapy for deadly diseases and prevent further impacts due to diabetes and hypertension. Many reviews already exist for this problem, but those reviews have presented the analysis of a single framework. Hence, this article on retinal segmentation review has revealed distinct methodologies with diverse frameworks that are utilized for blood vessel separation. The novelty of this review research lies in finding the best neural network model by comparing its efficiency. For that, machine learning (ML) and deep learning (DL) were compared and have been reported as the best model. Moreover, different datasets were used to segment the retinal blood vessels. The execution of each approach is compared based on the performance metrics such as sensitivity, specificity, and accuracy using publically accessible datasets like STARE, DRIVE, ROSE, REFUGE, and CHASE. This article discloses the implementation capacity of distinct techniques implemented for each segmentation method. Finally, the finest accuracy of 98% and sensitivity of 96% were achieved for the technique of Convolution Neural Network with Ranking Support Vector Machine (CNN-rSVM). Moreover, this technique has utilized public datasets to verify efficiency. Hence, the overall review of this article has revealed a method for earlier diagnosis of diseases to deliver earlier therapy.

Long-term changes in retinal layers in patients undergoing intravitreal ranibizumab for neovascular age-related macular degeneration : Retinal layers after anti-VEGF therapy.

PURPOSE: To analyze long-term changes in individual retinal layers (RLs) after intravitreal injections of ranibizumab (IVRs) in patients with neovascular age-related macular degeneration (n-AMD). METHODS: The patients were treated with 0.5-mg IVRs based on an as-needed protocol after the first three monthly doses over a 12-month follow-up period. Patients underwent optical coherence tomography and best-corrected visual acuity (BCVA) evaluation at each visit. The ETDRS grid with central subfield (R1) (r 0.5 mm) and the inner ring (R2) (r 0.5-1.5 mm) was used for calculation of the mean thickness of each RL. Changes in the thickness of segmented RLs within the R1 and R2 of ETDRS circles at months-3, -6, and -12 were compared to baseline. RESULTS: The mean age was 72 ± 7.4 years. The mean number of injections was 9.08 (range 6-11). Mean BCVA improved from 49.7 ± 22.1 to 60.1 ± 19.8 letters. Central macular thickness decreased from 390.25 ± 149.6 to 312.74 ± 118.4 µm. Thicknesses of GCL (from 23.93 ± 13.73 to 19.50 ± 9.50 µm in R1; p 0.001, and from 44.5 ± 12.6 to 39.6 ± 10.6 µm in R2; p 0.005), IPL (from 28.90 ± 14.36 to 22.35 ± 6.23 µm in R1; p 0.001, and from 39.34 ± 8.53 to 35.58 ± 7.93 µm in R2; p 0.004), and total inner RL (ILM to ELM) (from 222.93 ± 93.09 to 180 ± 53 µm in R1; p 0.001, and from 255.06 ± 42.74 to 240.25 ± 40.37 µm in R2; p 0.003) in the central and parafoveal rings decreased statistically at month-12. Decrease in INL was limited to month-6 (from 34.80 ± 15.33 to 27.60 ± 12.59 µm in R1; p 0.001), while decreases in total outer RLs (ELM to RPE) (from 128.32 ± 26.92 to 115.54 ± 43.98 µm in R1; p 0.001, and 103.81 ± 16.73 to 96.38 ± 16.22 µm in R2; p 0.014) and RPE (from 39.12 ± 22.33 to 29.70 ± 22.05 µm in R1; p 0.001, and from 31.27 ± 13.11 to 24.40 ± 9.99 µm in R2; p 0.001) were limited to month-3. CONCLUSIONS: Significant changes were observed in the thickness of the inner RLs after 1-year treatment with IVRs for n-AMD. A significant decrease in RPE thickness confined to the first months disappeared at month-12.

The retinal ganglion cell layer predicts normal-appearing white matter tract integrity in multiple sclerosis: A combined diffusion tensor imaging and optical coherence tomography approach.

We investigated the relationship between retinal layers and normal-appearing white matter (WM) integrity in the brain of patients with relapsing-remitting multiple sclerosis (MS), using a combined diffusion tensor imaging and high resolution optical coherence tomography approach. Fifty patients and 62 controls were recruited. The patients were divided into two groups according to presence (n = 18) or absence (n = 32) of optic neuritis. Diffusion tensor data were analyzed with a voxel-wise whole brain analysis of diffusion metrics in WM with tract-based spatial statistics. Thickness measurements were obtained for each individual retinal layer. Partial correlation and multivariate regression analyses were performed, assessing the association between individual retinal layers and diffusion metrics across all groups. Region-based analysis was performed, by focusing on tracts associated with the visual system. Receiver operating characteristic (ROC) curves were computed to compare the biomarker potential for the diagnosis of MS, using the thickness of each retinal layer and diffusion metrics. In patients without optic neuritis, both ganglion cell layer (GCL) and inner plexiform layer thickness correlated with the diffusion metrics within and outside the visual system. GCL thickness was a significant predictor of diffusion metrics in the whole WM skeleton, unlike other layers. No association was observed for either controls or patients with a history of optic neuritis. ROC analysis showed that the biomarker potential for the diagnosis of MS based on the GCL was high when compared to other layers. We conclude that GCL integrity is a predictor of whole-brain WM disruption in MS patients without optic neuritis.

A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD).

The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep learning, and computer vision, fundamentally transforming the analysis of retinal images. By utilizing a wide array of visual cues extracted from retinal fundus images, sophisticated artificial intelligence models have been developed to diagnose various retinal disorders. This paper concentrates on the detection of Age-Related Macular Degeneration (AMD), a significant retinal condition, by offering an exhaustive examination of recent machine learning and deep learning methodologies. Additionally, it discusses potential obstacles and constraints associated with implementing this technology in the field of ophthalmology. Through a systematic review, this research aims to assess the efficacy of machine learning and deep learning techniques in discerning AMD from different modalities as they have shown promise in the field of AMD and retinal disorders diagnosis. Organized around prevalent datasets and imaging techniques, the paper initially outlines assessment criteria, image preprocessing methodologies, and learning frameworks before conducting a thorough investigation of diverse approaches for AMD detection. Drawing insights from the analysis of more than 30 selected studies, the conclusion underscores current research trajectories, major challenges, and future prospects in AMD diagnosis, providing a valuable resource for both scholars and practitioners in the domain.

Stage specific glaucomatous changes of the macula recorded using spectral domain optical coherence tomography.

BACKGROUND: This study aimed to compare the thickness of different macular retinal layers in glaucomatous eyes and healthy controls, and evaluate the diagnostic performance of spectral domain optical coherence tomography (SD-OCT) parameters. METHODS: In this cross-sectional comparative study, 48 glaucomatous eyes and 44 healthy controls were included. The thickness of the total retina and all retinal layers were obtained using the Early Treatment Diagnostic Retinopathy Study (ETDRS) grid. The minimal and average values of outer and inner ETDRS-rings were calculated. The diagnostic performance for detection of glaucoma was evaluated using the area under the receiver operating characteristic curve (AUC). RESULTS: The thickness of the total retina, ganglion cell layer (GCL), and inner-plexiform layer (IPL) was significantly thinner in glaucomatous eyes in all sectors except the center (all p<0.05). The thickness of retinal nerve fiber layer (RNFL) was significantly thinner in the glaucoma group except in the center, nasal inner, and temporal outer sectors (all p<0.05). Layer thinning advanced with glaucoma severity. The minimal outer GCL thickness showed the highest AUC value for discrimination between glaucomatous eyes and healthy controls(0.955). The minimal outer IPL showed the highest AUC value for discriminating early-stage glaucomatous eyes from healthy controls (0.938). CONCLUSIONS: Glaucomatous eyes were found to have significant thinning in the macular region. GCL and IPL showed high ability to discriminate glaucomatous and early-stage glaucomatous eyes from controls. Applying the minimal value to the ETDRS grid has the potential to provide good diagnostic abilities in glaucoma screening.

Segmentation-guided domain adaptation and data harmonization of multi-device retinal optical coherence tomography using cycle-consistent generative adversarial networks.

BACKGROUND: Medical images such as Optical Coherence Tomography (OCT) images acquired from different devices may show significantly different intensity profiles. An automatic segmentation model trained on images from one device may perform poorly when applied to images acquired using another device, resulting in a lack of generalizability. This study addresses this issue using domain adaptation methods improved by Cycle-Consistent Generative Adversarial Networks (CycleGAN), especially when the ground-truth labels are only available in the source domain. METHODS: A two-stage pipeline is proposed to generate segmentation in the target domain. The first stage involves the training of a state-of-the-art segmentation model in the source domain. The second stage aims to adapt the images from the target domain to the source domain. The adapted target domain images are segmented using the model in the first stage. Ablation tests were performed with integration of different loss functions, and the statistical significance of these models is reported. Both the segmentation performance and the adapted image quality metrics were evaluated. RESULTS: Regarding the segmentation Dice score, the proposed model ssppg achieves a significant improvement of 46.24% compared to without adaptation and reaches 87.4% of the upper limit of the segmentation performance. Furthermore, image quality metrics, including FID and KID scores, indicate that adapted images with better segmentation also have better image qualities. CONCLUSION: The proposed method demonstrates the effectiveness of segmentation-driven domain adaptation in retinal imaging processing. It reduces the labor cost of manual labeling, incorporates prior anatomic information to regulate and guide domain adaptation, and provides insights into improving segmentation qualities in image domains without labels.

Evaluation of Structural Retinal Layer Alterations in Retinitis Pigmentosa.

Objective: This study aimed to analyze retinal layers in the macular region using spectral-domain optical coherence tomography (SD-OCT). Additionally, we examined the retinal vascular plexus densities of the eyes using optical coherence tomography angiography (OCT-A), specifically in patients with retinitis pigmentosa (RP). Methods: In the study, 36 eyes from patients with retinitis pigmentosa (RP) and 36 eyes from healthy controls were included. Measurements involved assessing the thicknesses of each retinal layer at the central fovea, parafoveal, and perifovea using spectral domain optical coherence tomography (SD-OCT). Moreover, the study involved the evaluation of retinal capillary plexus densities (RCPD), encompassing deep capillary plexus density values, superficial capillary plexus, and radial peripapillary capillary plexus. This assessment was performed using optical coherence tomography angiography (OCT-A). Results: No statistically significant difference in retinal thickness was found in the central fovea between the two groups. The thicknesses of the INL, OPL, and PRL in the parafoveal regions as well as the RPE in the perifoveal regions increased in the RP group. Nonetheless, the ONL, IPL, GCL, and RNFL demonstrated reduced thickness in both the perifoveal and parafoveal areas. The OCT-A findings indicated that patients with RP exhibited lower values for all RCPD. Conclusion: The retinal layers and RCPD were significantly impacted at varying rates of RP. It is essential to acknowledge that this alteration may be significant in the context of the retinal findings in patients with RP. Abbreviations: SD-OCT = Spectral-domain optical coherence tomography, OCT-A = Optical coherence tomography angiography, RP = Retinitis pigmentosa, ETDRS = Early Treatment Diabetic Retinopathy Study, SD = standard deviation, TRT = Total Retinal Thickness, IRT = Inner Retinal Thickness, ORT = Outer Retinal Thickness, RNFL = Retinal Nerve Fiber Layer, GCL = Ganglion Cell Layer, IPL = Inner Plexiform Layer, INL = inner nuclear layer, OPL = Outer Plexiform Layer, ONL = Outer Nuclear Layer, PRL = Photoreceptor layer, RPE = Retinal Pigment Epithelium, µm = micrometer, PaFoSu = parafovea superior, PeFoSu = perifovea superior, PaFoNa = parafovea nasal, PeFoNa = perifovea nasal, PaFoTe = parafovea temporal, PeFoTe = perifovea temporal, PaFoIn = parafovea inferior, PeFoIn = perifovea inferior.

Identifying Optical Coherence Tomography Markers for Multiple Sclerosis Diagnosis and Management.

BACKGROUND: Multiple sclerosis (MS) is a common neurological disease affecting the optic nerve, directly or indirectly, through transsynaptic axonal degeneration along the visual pathway. New ophthalmological tools, arguably the most important being optical coherence tomography (OCT), could prove paramount in redefining MS diagnoses and shaping their follow-up protocols, even when the optic nerve is not involved. METHODS: A prospective clinical study was conducted. In total, 158 eyes from patients previously diagnosed with relapsing remitting MS (RRMS)-with or without optic neuritis (ON), clinically isolated syndrome (CIS) with or without ON, and healthy controls were included. Each patient underwent an ophthalmologic exam and OCT evaluation for both eyes (a posterior pole analysis (PPA) and the optic nerve head radial circle protocol (ONH-RC)). RESULTS: The macular retinal thickness (the 4 × 4, respectively, 2 × 2 grid) and thickness of the peripapillary retinal nerve fiber layer (pRNFL) were investigated. Various layers of the retina were also compared. Our study observed significant pRNFL thinning in the RRMS eyes compared to the control group, the pRNFL atrophy being more severe in the RRMS-ON eyes than the RRMS-NON eyes. In the ON group, the macular analysis showed statistically significant changes in the RRMS-ON eyes when compared only to the CIS-ON eyes, regarding decreases in the inner plexiform layer (IPL) thickness and inner nuclear layer (INL) on the central 2 × 2 macular grid. The neurodegenerative process affected both the inner retina and pRNFL, with clinical damage appearing for the latter in the following order: CIS-NON, CIS-ON, RRMS-NON, and RRMS-ON. In the presence of optic neuritis, SMRR patients presented an increase in their outer retina thickness compared to CIS patients. CONCLUSIONS: To differentiate the MS patients from the CIS patients, in the absence of optic neuritis, OCT Posterior Pole Analysis could be a useful tool when using a central 2 × 2 sectors macular grid. Retinal changes in MS seem to start from the fovea and spread to the posterior pole. Finally, MS could lead to alterations in both the inner and outer retina, along with pRNFL.

Determination of Neurodegeneration in Polycystic Ovary Syndrome with Retinal Segmentation Analysis.

Purpose: To compare the thickness of each retinal layer in patients with polycystic ovary syndrome (PCOS) versus healthy, age-matched controls by using retinal segmentation analysis.Methods: In our cross-sectional study, 37 patients with PCOS (i.e., patient group) and 35 healthy individuals (i.e., control group) underwent spectral-domain optical coherence tomography imaging. Using built-in automatic retinal segmentation software to analyze the images collected, we compared the thickness of the retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer, outer plexiform layer, outer nuclear layer, photoreceptor layer (PRL), retinal pigment epithelium (RPE), inner retinal layers, and outer retinal layers between the groups. To analyze the measurements, we used a traditional Early Treatment Diabetic Retinopathy Study (ETDRS) grid.Results: In ETDRS subfields, 6-mm nasal RNFL thickness; 3- and 6-mm nasal GCL thickness; 3-mm superior and 6-mm nasal IPL thickness; 1-mm central, 3-mm nasal, superior, and inferior, and 6-mm nasal and inferior PRL thickness; and 6-mm inferior RPE thickness were significantly thinner in patients with PCOS than that of healthy controls.Conclusion: The results of our retinal segmentation analysis indicate that patients with PCOS tend to have thinner GCL, IPL, and PRL than healthy, age-matched controls due to neurodegeneration likely caused by insulin resistance, or subclinical retinal inflammation.

CNN-based CP-OCT sensor integrated with a subretinal injector for retinal boundary tracking and injection guidance.

SIGNIFICANCE: Subretinal injection is an effective way of delivering transplant genes and cells to treat many degenerative retinal diseases. However, the technique requires high-dexterity and microscale precision of experienced surgeons, who have to overcome the physiological hand tremor and limited visualization of the subretinal space. AIM: To automatically guide the axial motion of microsurgical tools (i.e., a subretinal injector) with microscale precision in real time using a fiber-optic common-path swept-source optical coherence tomography distal sensor. APPROACH: We propose, implement, and study real-time retinal boundary tracking of A-scan optical coherence tomography (OCT) images using a convolutional neural network (CNN) for automatic depth targeting of a selected retinal boundary for accurate subretinal injection guidance. A simplified 1D U-net is used for the retinal layer segmentation on A-scan OCT images. A Kalman filter, combining retinal boundary position measurement by CNN and velocity measurement by cross correlation between consecutive A-scan images, is applied to optimally estimate the retinal boundary position. Unwanted axial motions of the surgical tools are compensated by a piezoelectric linear motor based on the retinal boundary tracking. RESULTS: CNN-based segmentation on A-scan OCT images achieves the mean unsigned error (MUE) of ∼3 pixels (8.1 µm) using an ex vivo bovine retina model. GPU parallel computing allows real-time inference (∼2 ms) and thus real-time retinal boundary tracking. Involuntary tremors, which include low-frequency draft in hundreds of micrometers and physiological tremors in tens of micrometers, are compensated effectively. The standard deviations of photoreceptor (PR) and choroid (CH) boundary positions get as low as 10.8 µm when the depth targeting is activated. CONCLUSIONS: A CNN-based common-path OCT distal sensor successfully tracks retinal boundaries, especially the PR/CH boundary for subretinal injection, and automatically guides the tooltip's axial position in real time. The microscale depth targeting accuracy of our system shows its promising possibility for clinical application.

A comparative evaluation of segmental analysis of macular layers in patients with early glaucoma, ocular hypertension, and healthy eyes.

PURPOSE: To compare macular layer thicknesses between early glaucoma, ocular hypertension, and healthy eyes and to evaluate the accuracy of spectralis optical coherence tomography (OCT) segmentation software in discriminating early glaucoma from ocular hypertension and healthy eyes. METHODS: OCT scans were performed using the standard macular and peripapillary retinal nerve fiber layer (pRNFL) protocols on the Spectralis-OCT. The following macular thickness parameters were compared in the inner and outer circles of the Early Treatment Diabetic Retinopathy Study: total macular thickness (MT), retinal nerve fiber layer (mRNFL), ganglion cell layer (mGCL), inner plexiform layer (mIPL), inner nuclear layer (mINL), outer plexiform layer (mOPL), outer nuclear layer (mONL) and ganglion cell complex (mGCC: sum of mRNFL, mGCL, and mIPL). Sectors and layers with the best area under the receiver operating characteristic curve (AUC) were determined and compared between groups. RESULTS: Of a total of 200 eyes included in this study, 64 eyes had primary open angle glaucoma, 69 had ocular hypertension, and 67 were healthy. Peripapillary RNFL was significantly thinner in the early glaucoma group (P<0.05). For the macular variables, there was a significant reduction in the MT, mGCC, mRNFL, mGCL, and mIPL thicknesses in the early glaucoma group; while there were no statistically significant differences between the ocular hypertension and control groups (P>0.05). For distinguishing early glaucoma from normal eyes, AUCs for mGCC, mGCL, and mRNFL were similar to the pRNFL; and for early glaucoma and ocular hypertension, AUCs for mGCC, mGCL mRNF and mIPL were comparable to the pRNFL. CONCLUSION: Macular segmentation can assist in the early diagnosis of glaucoma as a complementary study to pRNFL analysis.

Anti-Vascular Endothelial Growth Factor Intravitreal Therapy and Macular Ganglion Cell Layer Thickness in Patients with Neovascular Age-Related Macular Degeneration.

Purpose: To assess macular ganglion cell layer (GCL) thickness in patients treated with intravitreal anti-vascular endothelial growth factor (anti-VEGF) for exudative age-related macular degeneration (AMD). Materials and Methods: This was a two-year retrospective institutional case series where records of patients treated with anti-VEGF injections for unilateral AMD were reviewed for BCVA, intraocular pressure, and spectral domain optical coherence tomography. Macular GCL thickness was evaluated with automated retinal segmentation based on ETDRS grid rings. Retinal layer segmentation was carefully assessed and manually corrected for any misalignment. Results: 48 eyes of 24 patients with unilateral exudative AMD were included. The mean number of anti-VEGF injections was 10.4 ± 3.2. Fellow eyes were classified as AREDS category one and two. There was significant thinning of the 3-mm macular GCL in treated compared with fellow eyes at one and two years (P = .03 and P = .04, respectively). GCL thickness compared to baseline showed a significant decrease at one and two years in treated (P = .01 and <0.0001) and at two years in untreated fellow eyes (P = .02). Conclusions: There is a decrease of macular GCL thickness in eyes with exudative AMD treated with anti-VEGF intravitreal injections in comparison with fellow eyes. There is longitudinal thinning of the GCL from baseline in eyes with both treated exudative and non-treated early AMD.

An overview of optical coherence tomography angiography and the posterior pole.

Optical coherence tomography angiography is a relatively new, noninvasive technology that has revolutionized imaging of the retinal and choroidal microvasculature. This technology is based on the detection of movement or changes that represent moving red cells in sequential optical coherence tomography scans. As with other established imaging technologies, it has unique benefits as well as certain disadvantages, which include a limited field of view and vulnerability to imaging artifacts. However, software and hardware improvements are continually evolving to mitigate these limitations. Optical coherence tomography angiography has been used to gain a better understanding of microvascular changes across a spectrum of ocular diseases including diabetic retinopathy, age-related macular degeneration, glaucoma, and retinal vein occlusions. In this article, we review algorithms and techniques commonly utilized in optical coherence tomography angiography systems and compare optical coherence tomography angiography to fluorescein angiography, the current gold standard for imaging the retinal vasculature. In addition, we provide an overview of important optical coherence tomography angiography findings in a variety of ocular diseases. Although the clinical role of this technology is still poorly defined, optical coherence tomography angiography has the potential to become an invaluable tool in the diagnosis and monitoring of vascular pathologies.

Is the Retina a Mirror of the Aging Brain? Aging of Neural Retina Layers and Primary Visual Cortex Across the Lifespan.

How aging concomitantly modulates the structural integrity of the brain and retina in healthy individuals remains an outstanding question. Given the strong bottom-up retinocortical connectivity, it is important to study how these structures co-evolve during healthy aging in order to unravel mechanisms that may affect the physiological integrity of both structures. For the 56 participants in the study, primary visual cortex (BA17), as well as frontal, parietal and temporal regions thicknesses were measured in T1-weighted magnetic resonance imaging (MRI), and retinal macular thickness (10 neuroretinal layers) was measured by optical coherence tomography (OCT) imaging. We investigated the statistical association of these measures and their age dependence. We found an age-related decay of primary visual cortical thickness that was significantly correlated with a decrease in global and multiple layer retinal thicknesses. The atrophy of both structures might jointly account for the decline of various visual capacities that accompany the aging process. Furthermore, associations with other cortical regions suggest that retinal status may index cortical integrity in general.

Retinal thickness profiles in patients with Behçet uveitis during remission / Perfis de espessura da retina em pacientes com uveíte de Behçet durante remissão

ABSTRACT Purpose: The purpose of this study was to evaluate the intraretinal layer thickness in the macular region and its correlation with the duration of uveitis and visual acuity in patients with Behçet uveitis. Methods: In this cross-sectional study, we included 93 eyes of 57 patients with Behçet uveitis and 100 eyes of 50 healthy individuals admitted to a tertiary center from January to September 2017. We performed macular measurements in all subjects via spectral domain-optical coherence tomography (SD-OCT) and divided the retina into layers using automated segmentation software on the SD-OCT device. We then compared layer thicknesses between the patient and control groups and evaluated the correlation between OCT parameters and the duration of uveitis and visual acuity in the patient group. Results: Our records show a mean age of 37.9 ± 10.8 (18-64) years and 37.7 ± 12.2 (21-61) years in the patient and control groups (p=0.821), respectively. Meanwhile, data reveal a mean duration of uveitis of 6.9 ± 4.7 (1-20) years. We found a reduction in the total outer layer thickness in the patient group (p<0.001). However, we did not find a statistically significant difference in the inner retinal layers except in the inner nuclear layer. The duration of uveitis negatively correlated with the outer retinal layer's thickness (correlation coefficient = -0.250). On the other hand, visual acuity positively correlated with the central macular, the total inner layer, and the outer retinal layer thicknesses (correlation coefficients: 0.194, 0.154, and 0.364, respectively). However, the inner nuclear layer negatively correlated with visual acuity. Conclusions: Using retinal segmentation via SD-OCT for follow-ups can help estimate visual loss in patients with Behçet uveitis, which can cause significant changes in intraretinal layers in the macular region.

RESUMO Objetivo: Avaliar a espessura das camadas intraretinianas na região macular e sua relação com a duração da uveíte e acuidade visual em pacientes com uveíte de Behçet. Métodos: Este estudo transversal incluiu 93 olhos de 57 pacientes com uveíte de Behçet e 100 olhos de 50 indivíduos saudáveis que foram admitidos em um hospital terciário entre janeiro de 2017 e setembro de 2017. As medições maculares foram realizadas com tomografia de coerência óptica de domínio espectral (SD-OCT) em todos os pacientes. A retina foi dividida em camadas usando software de segmentação automatizado no dispositivo SD-OCT. As espessuras da camada foram comparadas entre os pacientes e os grupos controle. No grupo de pacientes, foi avaliada a correlação entre os parâmetros obtidos na OCT e a duração da uveíte e acuidade visual. Resultados: A média de idade foi de 37,9 ± 10,8 (18-64) no grupo de pacientes e 37,7 ± 12,2 (21-61) no grupo controle (p=0,821). A duração média da uveíte foi de 6,9 ± 4,7 (1-20) anos. A espessura total das camadas externas no grupo de pacientes foi reduzida (p<0,001). Uma diferença estatisticamente significativa não foi encontrada nas camadas internas da retina, exceto na camada nuclear interna. Uma correlação negativa foi detectada entre a duração da uveíte e a espessura da camada externa da retina (coeficiente de correlação = -0,250). Uma correlação positiva significativa foi detectada entre a acuidade visual e a espessura macular central bem como a espessura total das camadas internas e externas da retina (coeficientes de correlação 0,194; 0,154 e 0,364, respectivamente). A camada nuclear interna foi negativamente correlacionada com a acuidade visual. Conclusões: A uveíte de Behçet pode causar alterações significativas nas camadas intraretinianas na região macular. A segmentação da retina com SD-OCT pode ser útil para acompanhamentos e para estimar a perda visual em pacientes com uveíte de Behçet.

Three-dimensional mapping of peripapillary retinal layers using a spectral domain optical coherence tomography.

PURPOSE: To map and view the effects of age, gender, and axial length on seven individual retinal layers around the optic nerve head (ONH). METHODS: We scanned 242 healthy patients using the Spectralis spectral domain optical coherence tomography in an outpatient setting. The layers were observed on the Early Treatment Diabetic Retinopathy Study sectors using the standard Spectralis Family Acquisition Module 6.0.11.0. The center was the ONH, the inner circle (IC) was 1-3 mm away, and the outer circle (OC) was 3-6 mm away. The seven layers were retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), inner plexiform layer (IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer (ONL), and retinal pigment epithelium (RPE). Additionally, we calculated the mean thickness of two combined layers: inner retinal layer (IRL) and photoreceptor layer (PL). Finally, we measured the mean of the total retinal thickness (TRT). RESULTS: The TRT was highest at the inferior end in the IC and at the nasal end in the OC. The RPE (p<0.001) and PL (p<0.001) were thicker in males; however, the IRL (p=0.015) was thicker in females. We found that the RNFL (p<0.001, r=0.139), GCL (p<0.001, r=0.116), IPL (p=0.016, r=0.059), INL (p<0.001, r=0.104), OPL (p=0.009, r=0.064), ONL (p<0.001, r=0.157), RPE (p=0.001, r=0.079), IRL (p<0.001, r=0.190), PL (p=0.030, r=0.053), and TRT (p<0.001, r=0.191) correlated negatively with age. The axial length significantly and negatively correlated at the GCL (p=0.003, r=0.093), IPL (p=0.020, r=0.072), INL (p=0.018, r=0.073), ONL (p<0.001, r=0.110), IRL (p=0.003, r=0.092), and TRT (p=0.003, r=0.094). We found poor reproducibility in the IC; however, this was excellent in the OC. CONCLUSION: We found significant differences in layers according to age, gender, and axial length. Additionally, reproducibility can be improved by altering the algorithm to account for the ONH parameters.

Ganglion cell layer measurements correlate with disease severity in patients with Alzheimer's disease.

PURPOSE: To evaluate the thickness of the 10 retinal layers of patients with Alzheimer's disease (AD) using a new segmentation technology of the Spectralis optical coherence tomography (OCT) and to determine whether the thickness of specific layers predicts neurodegeneration or AD severity. METHODS: Patients with AD (n = 150) and age-matched healthy controls (n = 75) were analysed using the segmentation application prototype to automatically segment all retinal layers in a macular scan. Thicknesses of each layer were compared between patients with AD and controls, and between patients with disease durations of less than or at least 3 years. Associations between retinal layer thicknesses, disease duration and AD severity were evaluated. RESULTS: Patients with AD had reduced thickness in the retinal nerve fibre, ganglion cell, inner plexiform and outer nuclear layers (p < 0.05). The inner retinal layers were more affected in patients with long disease duration. Ganglion cell and retinal nerve fibre layer thicknesses were inversely correlated with AD duration and severity. Ganglion cell and inner plexiform layers thicknesses were predictive of axonal damage. CONCLUSIONS: The segmentation application revealed ganglion cell and retinal layer atrophy in patients with AD compared with controls, especially in the inner layers of patients with long disease duration. Ganglion cell layer reduction was associated with increased axonal damage and may predict greater disease severity.