Nerve Fiber Layer Defects Research Articles

Case Series: Discordance Between Visible Retinal Nerve Fiber Layer Defects and Spectral Domain Optical Coherence Tomographic Analysis in Patients with Glaucoma

Case Series: Discordance Between Visible Retinal Nerve Fiber Layer Defects and Spectral Domain Optical Coherence Tomographic Analysis in Patients with Glaucoma

Characteristics of Normal-tension Glaucoma Patients with Temporal Retinal Nerve Fibre Defects

Glaucomatous visual field (VF) damage usually involves in the Bjerrum area, which refers to outside the central 10° region. However, some reports suggest that structural damage to the macula occurs even in the early stages of glaucoma. We investigated the characteristics of normal tension glaucoma (NTG) patients with temporal retinal nerve fibre layer (RNFL) defects. Ninety eyes from 90 subjects including 30 normal eyes, 30 eyes of 30 patients with normal-tension glaucoma with temporal RNFL defects, and 30 eyes of 30 patients with normal-tension glaucoma with inferotemporal or superotemporal RNFL defects were enrolled. The best-corrected visual acuity (BCVA) decreased significantly in glaucomatous eyes with temporal RNFL defects as compared with in controls and glaucomatous eyes with inferotemporal or superotemporal RNFL defects. VF tests showed more frequent central or cecocentral VF defects involving the central 10° region in glaucomatous eyes with temporal RNFL defects. VF defects were more frequently detected on short-wavelength automated perimetry (SWAP). Eyes with temporal RNFL defects had generally reduced ganglion cell-inner plexiform layer (GCIPL) thickness. In addition, the BCVA, GCIPL thicknesses, and SWAP findings were significantly different in glaucoma patients with temporal RNFL defects according to their colour vision deficiency, not RNFL thickness or standard automated perimetry (SAP) results.

Frequency Of Pseudophakic Glaucoma In A Tertiary Care Hospital Of Pakistan

Purpose: To evaluate the frequency, causes and management of pseudophakic glaucoma among the pseudophakic patients presenting in a tertiary care hospital of Pakistan.
 Study Design: Descriptive cross sectional study.
 Place and Duration of Study: Department of Ophthalmology, Abbasi Shaheed Hospital, Pakistan, from August 2017 to June 2018.
 Material and Methods: Adult patients between 50 to 70 years of age with pseudophakic glaucoma were included in the study by non-probability convenience sampling after institutional review board approval. Patients with primary open angle, primary angle closure, traumatic glaucoma, diabetes mellitus and hypertension were excluded. Pseudophakic glaucoma was labeled in case of cataract surgery with intraocular lens implantation and intraocular pressure > 21 mmHg or more in one eye along with glaucomatous optic disc or retinal nerve fiber layer defect on OCT (optical coherence tomography). Frequencies were computed for categorical variables. Data was analyzed on SPSS version 20.
 Results: Twenty-eight eyes with pseudophakic glaucoma were studied. There were 15 (53.57%) males. Mean age was 63 ± 10.4 SD years. Mean IOP was 30.78 ± 7.5 mm Hg. Patients with extracapsular cataract extraction were 18 (64.2%) and 10 (35.8%) had phacoemulsification. Most frequent cause was posterior capsular rupture (n = 16, 57.1%) followed by pupillary block, (n = 4, 14.2%) and UGH (n = 3, 10.7%). Medical treatment was successful in 20 (71.4%) and surgical treatment was done in 8 patients.
 Conclusion: Most common causes of pseudophakic glaucoma are posterior capsular rupture, vitreous loss, uveitis and pupillary block. Pseudophakic glaucoma is more common with anterior chamber intraocular lenses and extracapsular cataract extraction.

Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs.

To develop a deep learning approach based on deep residual neural network (ResNet101) for the automated detection of glaucomatous optic neuropathy (GON) using color fundus images, understand the process by which the model makes predictions, and explore the effect of the integration of fundus images and the medical history data from patients. A total of 34,279 fundus images and the corresponding medical history data were retrospectively collected from cohorts of 2371 adult patients, and these images were labeled by 8 glaucoma experts, in which 26,585 fundus images (12,618 images with GON-confirmed eyes, 1114 images with GON-suspected eyes, and 12,853 NORMAL eye images) were included. We adopted 10-fold cross-validation strategy to train and optimize our model. This model was tested in an independent testing dataset consisting of 3481 images (1524 images from NORMAL eyes, 1442 images from GON-confirmed eyes, and 515 images from GON-suspected eyes) from 249 patients. Moreover, the performance of the best model was compared with results obtained by two experts. Accuracy, sensitivity, specificity, kappa value, and area under receiver operating characteristic (AUC) were calculated. Further, we performed qualitative evaluation of model predictions and occlusion testing. Finally, we assessed the effect of integrating medical history data in the final classification. In a multiclass comparison between GON-confirmed eyes, GON-suspected eyes and NORMAL eyes, our model achieved 0.941 (95% confidence interval [CI], 0.936-0.946) accuracy, 0.957 (95% CI, 0.953-0.961) sensitivity, and 0.929 (95% CI, 0.923-0.935) specificity. The AUC distinguishing referrals (GON-confirmed and GON-suspected eyes) from observation was 0.992 (95% CI, 0.991-0.993). Our best model had a kappa value of 0.927, while the two experts' kappa values were 0.928 and 0.925 independently. The best 2 binary classifiers distinguishing GON-confirmed/GON-suspected eyes from NORMAL eyes obtained 0.955, 0.965 accuracy, 0.977, 0.998 sensitivity, and 0.929, 0.954 specificity, while the AUC was 0.992, 0.999 respectively. Additionally, the occlusion testing showed that our model identified the neuroretinal rim region, retinal nerve fiber layer (RNFL) defect areas (superior or inferior) as the most important parts for the discrimination of GON, which evaluated fundus images in a way similar to clinicians. Finally, the results of integration of fundus images with medical history data showed a slight improvement in sensitivity and specificity with similar AUCs. This approach could discriminate GON with high accuracy, sensitivity, specificity, and AUC using color fundus photographs. It may provide a second opinion on the diagnosis of glaucoma to the specialist quickly, efficiently and at low cost, and assist doctors and the public in large-scale screening for glaucoma.

Relationship between nerve fiber layer defect and the presence of epiretinal membrane in a Japanese population: The JPHC-NEXT Eye Study

The study subjects were residents of Chikusei city, Japan, aged 40 years or older who attended annual health check-up programs and participated in the JPHC-NEXT Eye Study which performed non-mydriatic fundus photography of both eyes. The relationship of glaucomatous fundus changes such as optic disc cupping (cup to disc ratio ≥ 0.7) and retinal nerve fiber layer defect (NFLD) with the presence of epiretinal membrane (ERM) were examined cross-sectionally. A total of 1990 persons gave consent to participate in this study in 2013. The overall prevalence of ERM was 12.9%. Of these, 1755 had fundus photographs of sufficient quality and no history of intraocular surgery (mean age: 62.3 ± 10.0 years). After adjusting for age, sex and refractive error, NFLD was positively associated with the presence of ERM (odds ratio [OR]: 2.48; 95% confidence interval [CI]: 1.24, 4.96; P = 0.010), but optic disc cupping was not (OR: 1.33; CI: 0.71, 2.48; P = 0.37). The results did not necessarily suggest an association between glaucoma and ERM, but indicated an association between NFLD and ERM.

Artificial intelligence and its applications in ophthalmology

The term artificial intelligence (AI) was proposed in 1956 by Dartmouth scholar John McCarthy, which refers to hardware or software that exhibits behavior which appears intelligent.1 During recent times, AI gained immense popularity as new algorithms, specialized hardware, huge data and cloud-based services were developed. Machine learning (ML), a subset of AI, originated in 1980 and is defined as a set of methods that automatically detect patterns in data and then incorporate this information to predict future data under uncertain conditions. Another escalating technology of ML called Deep learning (DL), launched in 2000s, is an escalating technology of ML and has revolutionized the world of AI. These technologies are powerful tools utilized by modern society for objects' recognition in images, real-time languages' translation, device manipulation via speech (such as Apple's Siri®, Amazon’s Alexa®, Microsoft’s Cortana®, etc.). The steps for AI model include preprocessing image data, train, validate and test the model, and evaluate the trained model's performance. To increase AI prediction efficiency, raw data need to be preprocessed. Data collected from different sources needs to be integrated and the most relevant features selected and extracted to improve the learning process performance. Data set is randomly partitioned into two independent subsets, one is for modeling and the other is for testing. The test set is used to evaluate the final performance of the trained model. The area under receiver operating characteristic curves (AUC) is most used evaluation metrics for quantitative assessment of a model in AI diagnosis. The AUCs effective models range from 0.5 to 1; higher the value of AUC, better the performance of the model.2 In the medical field, AI gained popularity by visualization of input images of highly potential abnormal sites which can be reviewed and analyzed in future.
 AI and DL algorithms or systems are also widely used in field of ophthalmology. More intensively studied fields are diabetic retinopathy, age related macular degeneration, and cataract and glaucoma. Various ophthalmic imaging modalities used for AI diagnosis include fundus image, optical coherence tomography (OCT), ocular ultrasound, slit-lamp image and visual field. Diabetic retinopathy (DR), a diabetic complication, is a vasculopathy that affects one-third of diabetic patients leading to irreversible blindness. AI has been in use to predict DR risk and its progression. Gulshan and colleague were the first to report the application of DL for DR identification.3 They used large fundus image data sets in supervised manner for DR detection. Other studies applied DL to identify and stage DR. DL-based computer-aided system was introduced to detect DR through OCT images, achieving a specificity of 0.98.4 A computer-aided diagnostic (CAD) system based on CML algorithms using optical coherence tomography angiography images to automatically diagnose non-proliferative DR (NPDR) also achieved high accuracy and AUC.5 Age-related macular degeneration (AMD) is the leading cause of irreversible blindness among old people in the developed world. ML algorithms are being used to identify AMD lesions and prompt early treatment with accuracy usually over 80%.6 Using ML to predict treatment of retinal neovascularity in AMD and DR by anti-vascular endothelial growth factor (Anti VEGF) injection requirements can manage patients' economic burden and resource management. ML algorithms have been applied to diagnose and grade cataract using fundus images, ultrasounds images, and visible wavelength eye images.7 Glaucoma is the third largest sight-threatening eye disease around the world. Glaucoma patients suffered from high intraocular pressure, damage of the optic nerve head, retina nerve fiber layer defect, and gradual vision loss. Studies using DL methods to diagnose glaucoma are few. So far, fundus images and wide-field OCT scans have all been used to construct DL-based glaucomatous diagnostic models. Mostly, the DL-based methods show excellent results.8
 In this era of “evidence-based medicine,” clinicians and patients find it difficult to trust a mysterious machine to diagnose yet cannot provide explanations of why the patient has certain disease. In future, advanced AI interpreters will be launched which will contribute significantly to revolutionize current disease diagnostic pattern.

Characteristics of diffuse retinal nerve fiber layer defects in red-free photographs as observed in optical coherence tomography en face images

BackgroudTo determine whether diffuse retinal nerve fiber layer (RNFL) defects, identified on red-free fundus photographs, could be identified on optical coherence tomography (OCT) en face structural images and to evaluate which factors are related to the different recognition patterns on en face images.MethodsThis retrospective, cross-sectional study included open-angle glaucoma eyes with diffuse RNFL defects in the inferior hemifield, identified in red-free photographs. The corresponding OCT en face structural images were divided into 3 groups: (1) no defect, (2) localized defect, and (3) diffuse defect. We compared the demographic and clinical ocular characteristics among the groups.ResultsA total of 209 eyes from 157 patients were included. The distribution of OCT en face images was: no defect, 25 eyes (11.96%); localized defect, 106 eyes (50.72%); diffuse defect, 78 eyes (37.32%). Logistic regression analysis revealed that eyes with greater mean deviation (P = 0.004) and thicker inferior RNFL (P = 0.008) would be included in the no defect and localized defect groups based on OCT en face images, rather than in the diffuse defect group.ConclusionAround half of diffuse RNFL defects identified in the red-free photographs appeared as localized defects in OCT en face images. Mild glaucomatous damage was related to no defect and localized defect groups, classified based on the OCT en face images, in eyes with diffuse photographic RNFL defects. OCT en face images may be helpful in further assessing diffuse RNFL defects seen in red-free photographs in eyes with open-angle glaucoma.

Non-glaucomatous Retinal Nerve Fiber Layer Defect Associated with Paravascular Inner Retinal Defect

Non-glaucomatous Retinal Nerve Fiber Layer Defect Associated with Paravascular Inner Retinal Defect

A Novel Adaptive Weighted Loss Design in Adversarial Learning for Retinal Nerve Fiber Layer Defect Segmentation

Glaucoma is a chronic eye disease that can cause permanent visual loss and is difficult to detect early. Retinal nerve fiber layer defect (RNFLD) is clinical evidence for the diagnosis of glaucoma. Classical deep learning based methods can be used to segment RNFLD from fundus images. However, the segmentation results of these methods do not have the specific geometry of RNFLD, and the segmentation errors of fundus images with special styles are large. In this paper, we present a novel conditional adversarial shuffle U-shaped network (CASU-Net) to segment RNFLD, which consists of a generator and a discriminator. For the generator, a mixed loss is designed, which consists of an adaptive weighted segmentation loss and an adversarial loss. This adaptive weighted segmentation loss can balance the segmentation accuracy of the target and background region, and assign more attention to the hard samples, thus ensuring the consistent improvement of the segmentation accuracy of all fundus images. The adversarial loss not only helps to improve the pixel-wise segmentation accuracy but also makes the geometry of the RNFLD segmentation closer to the ground truth. In addition, in the generator, a shuffle module was designed to fully mine the information of all channels to improve the feature extraction capability of the model. The proposed CASU-Net is verified on a RNFLD dataset from Beijing Tongren Hospital. The experiments show that the CASU-Net achieves state-of-the-art results on this dataset.

Development of a nomogram using fundus photography to predict glaucoma progression in patients showing disc hemorrhage

AbstractPurposeTo develop a nomogram to predict the progression of glaucoma by fundus photography in patients with disc hemorrhage.Material and methodsRetrospective review of the medical records of patients with disc hemorrhage, which was detected during follow up with open angle glaucoma, from January 2010 to January 2019. Patients were divided into glaucoma progression (n = 52) or non‐progression (n = 38) groups. We assessed proximal location and morphology of disc hemorrhage; relationship to retinal nerve fiber layer defects with disc hemorrhage; and angular extent of disc hemorrhage, between groups using fundus photography. Multiple logistic regression analysis was performed to select prognostic factors, and we constructed a nomogram to predict glaucoma progression.ResultThe incidences of disc hemorrhage at the border of retinal nerve fiber layer defects (p < 0.001) and peripapillary disc hemorrhage (p = 0.009) were significantly higher in the progression group. We used angular extent; location of disc hemorrhage with retinal nerve fiber layer defects; and proximal location of disc hemorrhage to construct the nomogram. The area under the receiver operating characteristic curve of the nomogram was 0.829.ConclusionWe created the nomogram using fundus photography in patients showing disc hemorrhage as a novel and accurate screening method to predict glaucoma progression and aid clinicians to decide on the best treatment plan.

Association between localised retinal nerve fibre layer defects and cardiovascular risk factors

Localised retinal nerve fibre layer defects (RNFLDs) are reported to indicate the degree of glaucomatous damage but can also be sequelae of retinal vascular insufficiency as a result of systemic vascular factors. We investigated the association between RNFLDs and cardiovascular risk factors. RNFLDs were detected in 440 (29.2%) of 1508 subjects. The presence of RNFLDs was associated with higher HbA1c (odds ratio [OR] 1.289, p < 0.001), higher 24-h mean systolic blood pressure (SBP; OR 1.013, p < 0.005), and lower estimated glomerular filtration rate (eGFR; OR 0.995, p < 0.005). An increasing number of RNFLDs was correlated with higher SBP (r = 0.186, p < 0.001), higher HbA1c (r = 0.128, p < 0.010), lower eGFR (r = −0.112, p < 0.020), and younger age (r = −0.303, p < 0.001). Subjects with RNFLDs had a higher predicted 10-year risk of atherosclerotic cardiovascular disease than did those without RNFLDs (9.7% vs 7.9%, p = 0.008 in middle-aged subjects, 25.6% vs 23.2%, p = 0.040 in older subjects). In conclusion, RNFLDs are associated with SBP, eGFR, and HbA1c. Concomitant cardiovascular risk factors should be considered when evaluating patients with localised RNFLDs.

OCT angiography in optic disc drusen: comparison with structural and functional parameters

BackgroundOptic disc drusen (ODD) can cause retinal nerve fibre layer (RNFL) defects with progressive visual field (VF) loss. Microvascular changes are discussed as a cause. We measured the vessel density...

Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs

To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referral decisions by glaucoma specialists (GSs) and the algorithm, and to compare the performance of the algorithm with eye care providers. Development and validation of an algorithm. Fundus images from screening programs, studies, and a glaucoma clinic. A DL algorithm was trained using a retrospective dataset of 86 618 images, assessed for glaucomatous ONH features and referable GON (defined as ONH appearance worrisome enough to justify referral for comprehensive examination) by 43 graders. The algorithm was validated using 3 datasets: dataset A (1205 images, 1 image/patient; 18.1% referable), images adjudicated by panels of GSs; dataset B (9642 images, 1 image/patient; 9.2% referable), images from a diabetic teleretinal screening program; and dataset C (346 images, 1 image/patient; 81.7% referable), images from a glaucoma clinic. The algorithm was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity for referable GON and glaucomatous ONH features. The algorithm's AUC for referable GON was 0.945 (95% confidence interval [CI], 0.929-0.960) in dataset A, 0.855 (95% CI, 0.841-0.870) in dataset B, and 0.881 (95% CI, 0.838-0.918) in dataset C. Algorithm AUCs ranged between 0.661 and 0.973 for glaucomatous ONH features. The algorithm showed significantly higher sensitivity than 7 of 10 graders not involved in determining the reference standard, including 2 of 3 GSs, and showed higher specificity than 3 graders (including 1 GS), while remaining comparable to others. For both GSs and the algorithm, the most crucial features related to referable GON were: presence of vertical cup-to-disc ratio of 0.7 or more, neuroretinal rim notching, retinal nerve fiber layer defect, and bared circumlinear vessels. A DL algorithm trained on fundus images alone can detect referable GON with higher sensitivity than and comparable specificity to eye care providers. The algorithm maintained good performance on an independent dataset with diagnoses based on a full glaucoma workup.

Comparison of retinal ganglion cell-related layer asymmetry between early glaucoma eyes with superior and inferior hemiretina damage

AimTo compare retinal ganglion cell (RGC)-related layer thickness asymmetry between early open-angle glaucoma (EG) eyes with superior and inferior hemiretina damage.MethodsThis was a retrospective study including 95 EG eyes (mean...

Blue-filter Fundus Photography for Detection of Retinal Nerve Fiber Layer Defect in Myopic Eyes

Blue-filter Fundus Photography for Detection of Retinal Nerve Fiber Layer Defect in Myopic Eyes

Different Disc Characteristics Associated With High Myopia and the Location of Glaucomatous Damage in Primary Open-Angle Glaucoma and Normal-Tension Glaucoma.

To evaluate the disc characteristics in high myopia and the relationships between disc tilt and torsion and the location of wedge-shaped RNFL defects in primary POAG and NTG. The differences in disc properties and clinical characteristics between POAG (n=53) and NTG (n=82), and between high myopic (<-6 D, n=77) and non-high myopic (≥-6 D, n=58) glaucoma, were investigated. The association between disc tilt and torsion and the location of wedge-shaped RNFL defects in POAG and NTG were assessed by univariate and multivariate logistic regression analyses. In NTG, but not in POAG, high myopia eyes had significantly smaller discs, a lower tilt ratio (0.73±0.09), and more disc tilt (56.5%) than did non-high myopia eyes (0.80±0.09, 33.3%). In POAG, eyes with lower wedge-shaped RNFL defects had a significantly smaller torsion angle (-5.44±19.62 degrees, inferior disc torsion) than did eyes with upper defects (9.70±23.62 degrees, superior disc torsion; P=0.014). In NTG, a significantly decreased tilt ratio (0.74±0.93; P=0.001) and more disc tilt (55.9%, P=0.005) were found in eyes with lower wedge-shaped RNFL defects than in eyes with upper defects (0.81±0.09; 21.7%). The torsion degree and tilt ratio were the only factors associated with the location of wedge-shaped RNFL defects in POAG and NTG, respectively. In NTG, smaller and tilted discs were the characteristics of high myopia. Disc tilt was associated with lower wedge-shaped RNFL defects. In POAG, the direction of disc torsion corresponded to the location of the wedge-shaped RNFL defects.

Development and Validation of Deep Learning Models for Screening Multiple Abnormal Findings in Retinal Fundus Images

To develop and evaluate deep learning models that screen multiple abnormal findings in retinal fundus images. Cross-sectional study. For the development and testing of deep learning models, 309 786 readings from 103 262 images were used. Two additional external datasets (the Indian Diabetic Retinopathy Image Dataset and e-ophtha) were used for testing. A third external dataset (Messidor) was used for comparison of the models with human experts. Macula-centered retinal fundus images from the Seoul National University Bundang Hospital Retina Image Archive, obtained at the health screening center and ophthalmology outpatient clinic at Seoul National University Bundang Hospital, were assessed for 12 major findings (hemorrhage, hard exudate, cotton-wool patch, drusen, membrane, macular hole, myelinated nerve fiber, chorioretinal atrophy or scar, any vascular abnormality, retinal nerve fiber layer defect, glaucomatous disc change, and nonglaucomatous disc change) with their regional information using deep learning algorithms. Area under the receiver operating characteristic curve and sensitivity and specificity of the deep learning algorithms at the highest harmonic mean were evaluated and compared with the performance of retina specialists, and visualization of the lesions was qualitatively analyzed. Areas under the receiver operating characteristic curves for all findings were high at 96.2% to 99.9% when tested in the in-house dataset. Lesion heatmaps highlight salient regions effectively in various findings. Areas under the receiver operating characteristic curves for diabetic retinopathy-related findings tested in the Indian Diabetic Retinopathy Image Dataset and e-ophtha dataset were 94.7% to 98.0%. The model demonstrated a performance that rivaled that of human experts, especially in the detection of hemorrhage, hard exudate, membrane, macular hole, myelinated nerve fiber, and glaucomatous disc change. Our deep learning algorithms with region guidance showed reliable performance for detection of multiple findings in macula-centered retinal fundus images. These interpretable, as well as reliable, classification outputs open the possibility for clinical use as an automated screening system for retinal fundus images.

Optical coherence tomography findings in chronic progressive external ophthalmoplegia.

Background:Chronic progressive external ophthalmoplegia (CPEO) is a mitochondrial encephalomyopathy caused by multiple mtDNA abnormalities. There is little information about the changes of ocular fundus with CPEO. The aim of this work was to measure and evaluate changes in the macular retinal thickness and optic nerve head in patients with CPEO using spectral-domain optical coherence tomography and to compare the findings with those of healthy individuals.Methods:Totally, 18 CPEO patients were enrolled in this study. Healthy volunteers matched for gender, age, and diopter settings were included as a control group. The retinal thickness of macular central fovea, inner and outer retinal layer thickness of perifoveal macular, optic nerve head parameters, and peripapillay retinal nerve fiber layer thickness (pRNFLT) for all included cases were measured using spectral-domain optical coherence tomography. A paired t test was used to compare the differences in the studied parameters between the two groups. The correlations between macular retinal thickness, pRNFLT, disease duration, and age of onset were also analyzed.Results:Among the macular parameters, retinal thickness of macular central fovea (t = −2.135, P < 0.05) and outer retinal layer thickness (t = −1.994, P < 0.05) of patients in the CPEO group were statistically significant lower than those of patients in the normal control group. For the optic nerve head parameters, the patients in the CPEO group showed a larger rim volume (t = −2.499, P < 0.05) and nerve head volume (t = −2.103, P < 0.05). The overall pRNFLT of patients in the CPEO group was statistically significant lower than that of patients in the control group (t = −4.125, P < 0.05). The comparison of pRNFLT in eight sectors showed that the pRNFLT of patients in the CPEO group was statistically significant lower than that of the control group mainly in the inferior and temporal sectors. The degree of pRNFL defect negatively correlated with the disease duration (r = −0.583, P < 0.05).Conclusions:The retinal thickness of patients with CPEO was significantly thinner, which was mostly the outer retina. The patients’ optic discs had a low volume and the loss of the retinal nerve fiber layer was obvious. With the extension of the disease duration, the retinal nerve fiber layer defect was even more significant.

Risk factors for disease progression in low-teens normal-tension glaucoma

Background/AimsTo investigate the risk factors for disease progression of normal-tension glaucoma (NTG) with pretreatment intraocular pressure (IOP) in the low-teens.MethodsOne-hundred and two (102) eyes of 102 patients with NTG with...

Characteristics of Diabetic Capillary Nonperfusion in Macular and Extramacular White Spots on Optical Coherence Tomography Angiography.

To compare the characteristics of macular and extramacular white spots on wide-field swept-source optical coherence tomography angiography (SS-OCTA) and optical coherence tomography (OCT) images in diabetic retinopathy (DR). We retrospectively reviewed 107 eyes of 64 patients with DR, of whom nominal 12 × 12 mm SS-OCTA images centered on the optic disc and ultrawide field photographs were acquired. White spots on fundus photographs corresponded to hyperreflective lesions in the superficial en-face OCT images, and the characteristics of these white spots were investigated. We compared such OCT findings with the vertical and horizontal extents of nonperfused areas (NPAs) on OCTA images. We observed 136 white spots and corresponding hyperreflective lesions in 49 eyes. The hyperreflective lesions in the extramacular areas had greater areas (P < 0.001) and more frequently spanned from the nerve fiber layer to the outer plexiform layer (P < 0.001), while those in the macula were superficial. All of macular hyperreflective lesions were accompanied with nerve fiber layer defects, whereas only 18 (15.4%) of 117 extramacular lesions had them (P < 0.001). Comparative studies showed that most extramacular hyperreflective lesions corresponded to the NPAs in the whole layers on OCTA images, compared to the lamellar NPAs of the superficial layer in most of the macular lesions (P < 0.001). The NPAs extended to the peripheral side more frequently in the extramacular hyperreflective lesions compared with macular lesions (P < 0.001). This study proposed that most of the extramacular white spots may be discriminated from macular spots with respect to diabetic NPAs on OCTA images.