Nonglaucomatous Optic Neuropathy Research Articles

Myopia: Histology, clinical features, and potential implications for the etiology of axial elongation.

Myopic axial elongation is associated with various non-pathological changes. These include a decrease in photoreceptor cell and retinal pigment epithelium (RPE) cell density and retinal layer thickness, mainly in the retro-equatorial to equatorial regions; choroidal and scleral thinning pronounced at the posterior pole and least marked at the ora serrata; and a shift in Bruch's membrane opening (BMO) occurring in moderately myopic eyes and typically in the temporal/inferior direction. The BMO shift leads to an overhang of Bruch's membrane (BM) into the nasal intrapapillary compartment and BM absence in the temporal region (i.e., parapapillary gamma zone), optic disc ovalization due to shortening of the ophthalmoscopically visible horizontal disc diameter, fovea-optic disc distance elongation, reduction in angle kappa, and straightening/stretching of the papillomacular retinal blood vessels and retinal nerve fibers. Highly myopic eyes additionally show an enlargement of all layers of the optic nerve canal, elongation and thinning of the lamina cribrosa, peripapillary scleral flange (i.e., parapapillary delta zone) and peripapillary choroidal border tissue, and development of circular parapapillary beta, gamma, and delta zone. Pathological features of high myopia include development of macular linear RPE defects (lacquer cracks), which widen to round RPE defects (patchy atrophies) with central BM defects, macular neovascularization, myopic macular retinoschisis, and glaucomatous/glaucoma-like and non-glaucomatous optic neuropathy. BM thickness is unrelated to axial length. Including the change in eye shape from a sphere in emmetropia to a prolate (rotational) ellipsoid in myopia, the features may be explained by a primary BM enlargement in the retro-equatorial/equatorial region leading to axial elongation.

Disagreement of Radial Peripapillary Capillary Density Among Four Optical Coherence Tomography Angiography Devices.

This prospective study evaluated the agreement among four optical coherence tomography angiography (OCTA) devices in the assessment of radial peripapillary capillary (RPC) density. The study included 48 eyes of 48 subjects (14 healthy, 19 glaucomatous, and 15 non-glaucomatous optic neuropathy). Each participant was scanned using four OCTA devices in a random sequence: RTVue XR Avanti (RTVue), DRI OCT Triton (Triton), Revo NX 130 (Revo), and PLEX Elite 9000 (PlexE). All 6 × 6-mm grayscale OCTA images from each device were analyzed for RPC density using a customized algorithm. Agreement between each pair of devices was assessed using intraclass correlation coefficients (ICCs) and Bland-Altman plots. There was a poor correlation between devices in all comparisons (RTVue-Triton, ICC = 0.34; RTVue-Revo, ICC = 0.31; RTVue-PlexE, ICC = 0.28; Triton-Revo, ICC = 0.31; Triton-PlexE, ICC = 0.17; Revo-PlexE, ICC = 0.34). Significant proportional biases (P < 0.05) and wide limits of agreement with apparent constant biases were identified in all comparisons. The mean difference was greatest for the RTVue-Revo pair (-49.3%; 95% confidence interval [CI], -52.9 to -45.8) and smallest for the Triton-PlexE pair (-7.7%; 95% CI, -10.1 to -5.3). The RPC densities obtained from each device had poor inter-device agreement and significant biases and cannot be used interchangeably. RPC density obtained from different OCTA devices is not interchangeable; thus, the progression of optic neuropathy should be monitored using the same OCTA device.

Comparison of glaucomatous from non-glaucomatous optic neuropathy using Bruch's membrane opening minimum rim width optical coherence tomography measurements.

To compare glaucomatous from non-glaucomatous optic atrophy using optical coherence tomography (OCT) based on the measurement values of Bruch's membrane opening minimum rim width (BMO-MRW), which is a difficult task otherwise due to their varied course of disease progression, treatment protocols, and systemic association to visual impairment. This study was conducted in 40 eyes, comprising 20 eyes with non-glaucomatous optic neuropathy (NGON) and 20 eyes with glaucomatous optic neuropathy (GON). All patients underwent a complete ophthalmic examination followed by an OCT optic disc scan to calculate the measurement of BMO-MRW. The 5-fold cross-validated area under the curve for GON versus NGON from logistic regression models was 0.95 (95% confidence interval [CI]: 0.86-1.00) using BMO-MRW values from all sectors. The results revealed that the measurements were significantly lesser in GON than in NGON patients. Hence, OCT-based BMO-MRW values could be used as an additional test to compare glaucomatous with non-glaucomatous optic neuropathy patients, especially in cases of high clinical suspicion.

Non-glaucomatous Optic Disc Cupping: A Brief Review

Although optic disc cupping is mostly seen in glaucoma patients, it can occur in non-glaucomatous optic neuropathies (NGON). The characteristics of NGON are cupping toxic optic neuropathies, optic neuritis, compressive ischemia, and hereditary nature. The basic components of optic disc cupping are prelaminar and laminar. Prelaminar thinning, which seemed to be non-specific, occurs in all types of retinal ganglion cell axon loss; such as compressive, ischemic, and inflammatory events; glaucoma; and aging. This form of cupping is usually shallow, with less excavation of the optic disc. Laminar type of cupping which is a clinically profound type of cupping, may damage peripapillary scleral and lamina cribrosa. Sometimes experienced clinicians cannot clearly distinguish glaucomatous from non-glaucomatous cupping. The non-glaucomatous optic neuropathy has more neuroretinal rim pallor with less excavation of the disc than in glaucoma. It also involves central visual acuity and color vision in primary levels with visual field defects aligned vertically and respecting the midline. Evaluation of the patient’s medical records, disease presentation, ocular function, and examination are also crucial. Secondary examinations, including visual field examination and optical coherence tomography (OCT) or neuroimaging, facilitate the disease’s differentiation. This review presents the methods of examining a patient with an increased cup-to-disc ratio.

Discrimination of glaucomatous from non-glaucomatous optic neuropathy with swept-source optical coherence tomography

To assess the ability of optic nerve head (ONH) parameters, peripapillary retinal nerve fiber layer (pRNFL), and macular ganglion cell layer (GCL) thickness measurements with swept-source optical coherence tomography (SS-OCT), to discriminate between glaucomatous and non-glaucomatous optic neuropathy (GON and NGON). This retrospective cross-sectional study involved 189 eyes of 189 patients, 133 with GON and 56 with NGON. The NGON group included ischemic optic neuropathy, previous optic neuritis, and compressive, toxic-nutritional, and traumatic optic neuropathy. Bivariate analyses of SS-OCT pRNFL and GCL thickness and ONH parameters were performed. Multivariable logistic regression analysis was employed to obtain predictor variables from OCT values, and the area under the receiver operating characteristic curve (AUROC) was calculated to differentiate between NGON and GON. Bivariate analyses showed that the overall and inferior quadrant of the pNRFL was thinner in the GON group (P=0.044 and P<0.01), while patients with NGON had thinner temporal quadrants (P=0.044). Significant differences between the GON and NGON groups were identified in almost all the ONH topographic parameters. Patients with NGON had thinner superior GCL (P=0.015), but there were no significant differences in GCL overall and inferior thickness. Multivariate logistic regression analysis demonstrated that vertical cup-to-disc ratio (CDR), cup volume, and superior GCL provided independent predictive value for differentiating GON from NGON. The predictive model of these variables along with disc area and age achieved an AUROC=0.944 (95% CI 0.898-0.991). SS-OCT is useful in discriminating GON from NGON. Vertical CDR, cup volume, and superior GCL thickness show the highest predictive value.

Retinal Nerve Fiber Layer Thickness/Minimum Rim Width Ratio Differentiates Glaucoma from Other Optic Neuropathies.

Global peripapillary retinal nerve fiber layer thickness/Bruch membrane opening- minimum rim width ratio is an objective and effective parameter to separate glaucomatous optic neuropathies (GONs) from nonglaucomatous optic neuropathies (NGONs). This study was undertaken to evaluate the diagnostic capability of the peripapillary retinal nerve fiber layer (pRNFL)/ Bruch membrane opening-minimum rim width (BMO-MRW) ratio to differentiate glaucomatous optic neuropathies (GONs) from nonglaucomatous optic neuropathies (NGONs). This retrospective study included patients with an optic neuropathy (ON), visual loss for>6 months and a confirmed single etiology. pRNFL thickness and BMO-MRW were measured with spectral-domain OCT (Spectralis®, Heidelberg Engineering, Heidelberg, Germany). The diagnostic accuracies of pRNFL, BMO-MRW and the global pRNFL/BMO-MRW ratio were evaluated with the areas under receiver operating characteristics curves (AUROCs). One eye each from 171 patients was investigated: 50 primary open-angle glaucomas, 15 normal pressure glaucomas, 50 optic neuritises, 15 nonarteritic anterior ischemic ONs, 24 compressive ONs, 10 dominant optic atrophies and 7 nutritional ONs. The global pRNFL/BMO-MRW ratio had the highest AUROC [0.97 vs. 0.92; P =0.01]. It was able to distinguish between GONs and NGONs with a cut-off value of 0.34. Increased mean deviation of the visual field-defect severity was associated with a higher ratio for GONs and a lower ratio for NGONs. Compared to NGONs and for the same degree of pRNFL thinning, lower BMO- MRW was found to be a specific marker of glaucoma, reflecting the neuroglial architecture changes within the optic nerve head typical of glaucoma and supporting fundamental pathophysiological differences.

Differentiating Glaucomatous Optic Neuropathy From Non-glaucomatous Optic Neuropathies Using Deep Learning Algorithms

A deep learning framework to differentiate glaucomatous optic disc changes due to glaucomatous optic neuropathy (GON) from non-glaucomatous optic disc changes due to non-glaucomatous optic neuropathies (NGONs). Cross-sectional study. A deep-learning system was trained, validated, and externally tested to classify optic discs as normal, GON, or NGON, using 2183 digital color fundus photographs. A Single-Center data set of 1822 images (660 images of NGON, 676 images of GON, and 486 images of normal optic discs) was used for training and validation, whereas 361 photographs from 4 different data sets were used for external testing. Our algorithm removed the redundant information from the images using an optic disc segmentation (OD-SEG) network, after which we performed transfer learning with various pre-trained networks. Finally, we calculated sensitivity, specificity, F1-score, and precision to show the performance of the discrimination network in the validation and independent external data set. For classification, the algorithm with the best performance for the Single-Center data set was DenseNet121, with a sensitivity of 95.36%, precision of 95.35%, specificity of 92.19%, and F1 score of 95.40%. For the external validation data, the sensitivity and specificity of our network for differentiating GON from NGON were 85.53% and 89.02%, respectively. The glaucoma specialist who diagnosed those cases in masked fashion had a sensitivity of 71.05% and a specificity of 82.21%. The proposed algorithm for the differentiation of GON from NGON yields results that have a higher sensitivity than those of a glaucoma specialist, and its application for unseen data thus is extremely promising.

Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study

BackgroundTo verify efficacy of automatic screening and classification of glaucoma with deep learning system.MethodsA cross-sectional, retrospective study in a tertiary referral hospital. Patients with healthy optic disc, high-tension, or normal-tension glaucoma were enrolled. Complicated non-glaucomatous optic neuropathy was excluded. Colour and red-free fundus images were collected for development of DLS and comparison of their efficacy. The convolutional neural network with the pre-trained EfficientNet-b0 model was selected for machine learning. Glaucoma screening (Binary) and ternary classification with or without additional demographics (age, gender, high myopia) were evaluated, followed by creating confusion matrix and heatmaps. Area under receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, and F1 score were viewed as main outcome measures.ResultsTwo hundred and twenty-two cases (421 eyes) were enrolled, with 1851 images in total (1207 normal and 644 glaucomatous disc). Train set and test set were comprised of 1539 and 312 images, respectively. If demographics were not provided, AUC, accuracy, precision, sensitivity, F1 score, and specificity of our deep learning system in eye-based glaucoma screening were 0.98, 0.91, 0.86, 0.86, 0.86, and 0.94 in test set. Same outcome measures in eye-based ternary classification without demographic data were 0.94, 0.87, 0.87, 0.87, 0.87, and 0.94 in our test set, respectively. Adding demographics has no significant impact on efficacy, but establishing a linkage between eyes and images is helpful for a better performance. Confusion matrix and heatmaps suggested that retinal lesions and quality of photographs could affect classification. Colour fundus images play a major role in glaucoma classification, compared to red-free fundus images.ConclusionsPromising results with high AUC and specificity were shown in distinguishing normal optic nerve from glaucomatous fundus images and doing further classification.

Yield of investigations in patients with questionable nonglaucomatous optic neuropathy

ObjectifLa neuropathie optique non glaucomateuse (NONG) peut souvent être diagnostiquée à tort comme un glaucome à pression normale (GPN). Il est important de faire la distinction entre ces deux affections pour ce qui est de choisir le traitement, d’établir le pronostic et de déterminer le besoin d'examens plus approfondis. Nous présentons ici les caractéristiques d'une cohorte de patients qui nous avaient été adressés en vue d'une consultation neuro-ophtalmologique afin de faire la distinction entre la neuropathie optique glaucomateuse (NOG) et la NONG. MéthodesAnalyse rétrospective des dossiers de patients qui ont été examinés en raison d'une NOG par opposition à une NONG entre 2018 et 2020 dans une clinique de soins tertiaires de neuro-ophtalmologie. Les patients ont reçu un diagnostic de NOG ou de NONG en fonction du degré de pâleur du disque optique, du degré d'excavation papillaire et de l'acuité visuelle centrale, et/ou lorsque les examens ont mis au jour une cause de NONG. RésultatsSur les 83 patients admis à l’étude, on a jugé que 71 patients (86 %) présentaient une NONG possible après l'examen initial et la neuro-imagerie. De ce nombre, on a conclu que 14 patients (19,7 %) présentaient effectivement une NONG et que 7 patients (9,9 %) présentaient à la fois une NOG et une NONG. Voici les causes les plus fréquentes de NONG : causes indéterminées (8), antécédents de névrite optique (4) et conflit neurovasculaire (2). La neuro-imagerie n'a pas donné beaucoup de résultats : des anomalies ont été observées chez seulement 4 des 71 patients (5,6 %). Aucun cas d'excavation bilatérale en l'absence de déficit pupillaire afférent relatif (DPAR) ne s'accompagnait d'anomalies à l'imagerie; de même, aucun des patients chez lesquels on a noté des anomalies à l'imagerie n'a eu besoin d'une intervention. La pression intraoculaire et le rapport cupule/disque des patients qui présentaient une NONG étaient plus faibles, et chez la plupart de ceux-ci (86 %), on notait une pâleur du disque optique. Les anomalies des faisceaux de fibres nerveuses étaient plus fréquentes tant dans la NOG que la NONG, les anomalies nasales étant plus fréquentes en présence de NOG. ConclusionsLorsque la NONG imite la NOG, la cause sous-jacente est souvent inconnue, et les résultats obtenus à la neuro-imagerie sont peu probants. L'intérêt d'examiner les patients qui présentent une excavation bilatérale sans DPAR afin de mettre au jour une NONG est particulièrement faible.

Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer.

The clinical diagnostic evaluation of optic neuropathies relies on the analysis of the thickness of the retinal nerve fibre layer (RNFL) by optical coherence tomography (OCT). However, false positives and false negatives in the detection of RNFL abnormalities are common. Here we show that an algorithm integrating measurements of RNFL thickness and reflectance from standard wide-field OCT scans can be used to uncover the trajectories and optical texture of individual axonal fibre bundles in the retina and to discern distinctive patterns of loss of axonal fibre bundles in glaucoma, compressive optic neuropathy, optic neuritis and non-arteritic anterior ischaemic optic neuropathy. Such optical texture analysis can detect focal RNFL defects in early optic neuropathy, as well as residual axonal fibre bundles in end-stage optic neuropathy that were indiscernible by conventional OCT analysis and by red-free RNFL photography. In a diagnostic-performance study, optical texture analysis of the RNFL outperformed conventional OCT in the detection of glaucoma, as defined by visual-field testing or red-free photography. Our findings show that optical texture analysis of the RNFL for the detection of optic neuropathies is highly sensitive and specific.

Les profils de la déficience visuelle au centre hospitalier universitaire Yalgado Ouédraogo : à propos de 501 cas

To determine the main causes of visual impairment according to age in patients admitted to the ophthalmology department. This was a retrospective study of 501 cases of visual impairment. Included were patients of both sexes whose best-corrected visual acuity in the better eye was strictly less than 5/10. The parameters studied were age, gender, place of residence, corrected visual acuity in the better eye, and diagnosis. The data were entered and processed with the Epi info 7.2 software. Subjects over 50 were the most prevalent (48.5%). In patients over 50 years of age, cataracts (54.32%) were the leading cause of visual impairment, followed by glaucoma (24.69%) and non-glaucomatous optic neuropathy (26.19%). Trauma (17.62%) was frequently found in patients aged 16 to 50 years. Tropical endemic limbo-conjunctivitis (25.00%) was the leading cause of visual impairment in subjects aged 5 to 15 years. This study confirms the results of the literature, which have shown that cataracts are the leading cause of visual impairment in developing countries. A major effort to raise awareness, provide information and therapeutic care would reduce the incidence of visual impairment.

Neuro-Ophthalmological Optic Nerve Cupping: An Overview

Optic nerve cupping or enlargement of the cup-to-disc ratio is widely recognized as a feature of glaucoma, however it may also occur in non-glaucomatous optic neuropathies. The most well-recognized non-glaucomatous optic neuropathies that cause cupping include compressive optic neuropathies, arteritic anterior ischemic optic neuropathies, hereditary optic neuropathies, and optic neuritis. Cupping is thought to consist of two main components: prelaminar and laminar thinning. The former is a shallow form of cupping and related to loss of retinal ganglion cells, whereas the latter involves damage to the lamina cribrosa and peripapillary scleral connective tissue. Differentiating glaucomatous and non-glaucomatous optic nerve cupping remains challenging even for experienced observers. Classically, the optic nerve in non-glaucomatous causes has pallor of the neuroretinal rim, but the optic nerve should not be examined in isolation. The patient’s medical history, history of presenting illness, visual function (visual acuity, color vision and visual field testing) and ocular examination also need to be considered. Ancillary testing such as optical coherence tomography of the retinal nerve fiber layer and ganglion cell layer-inner plexiform layer may also be helpful in localizing the disease. In this review, we review the non-glaucomatous causes of cupping and provide an approach to evaluating a patient that presents with an enlarged cup-to-disc ratio.

Parapapillary atrophy in optic neuropathies: Histology and clinical relevance.

Parapapillary atrophy is one of the parameters of the optic nerve head area which are assessed during the ophthalmoscopic examination particularly useful to characterize glaucomatous optic neuropathy. Optical coherence tomography evaluation provides high-resolution images of the optic nerve head and surrounding area, and can be used to study parapapillary atrophy. Different parapapillary atrophy zones were described depending on their histological features and research has been conducted to investigate the possible association between the presence and/ or size of parapapillary atrophy zones and several optic nerve disorders. In this review we discuss the histology and the clinical findings related to parapapillary atrophy in patients with glaucomatous optic neuropathy, non-glaucomatous optic neuropathies (e.g. arteritic and non-arteritic anterior ischemic optic neuropathies; suprasellar and parasellar tumors), and other ocular conditions (e.g. high myopia; age-related macular degeneration). Two different histologic classifications were identified. Parapapillary atrophy was demonstrated in glaucoma and glaucoma-like neuropathies, but not in other types of optic nerve disorders.

Effect of vitamin B12 deficiency anemia on peripapillary retinal nerve fiber layer.

To evaluate the effect of vitamin B12 deficiency anemia (BDA) on peripapillary retinal nerve fiber layer thickness (RNFLT) using spectral domain optical coherence tomography (SD-OCT), and to determine any correlation arising thereof. In this cross-sectional observational study, 99 eyes of 50 BDA patients of age 18-65y were compared with 100 eyes of 50 healthy control subjects. All subjects underwent comprehensive clinical, ophthalmic, and hematological evaluation, followed by peripapillary RNFLT assessment using SD-OCT. The mean total, inferior, nasal, and temporal RNFLT were significantly lower in BDA group as compared to control group (P<0.05). The mean total, inferior and nasal RNFLT correlated significantly (P<0.05) with serum Hb%, B12 and mean corpuscular volume (MCV) level (r=0.310, 0.435, -0.386 for total; r=0.932, 0.481, -0.513 for inferior; r=0.344, 0.254, -0.233 for nasal; respectively), while temporal and superior RNFLT quadrant did not show any correlation with any of the hematological parameters (r=0.144, 0.167, -0.096; r=0.111, 0.070, -0.099; respectively). The mean total RNFLT showed progressive thinning at par with the progression of anemia, except in very severe BDA, where an inverse relationship was documented. The mean total, inferior, nasal, and temporal peripapillary RNFLT was significantly thinner in BDA patients. Peripapillary RNFLT thinning seemed to proceed at par with the progression of severity of anemia, except in very sever grade. Early assessment of peripapillary RNFLT may be crucial in BDA patients to prevent potential blinding sequelae. Peripapillary RNFLT thinning in BDA patients should be considered in the differential diagnosis of other non-glaucomatous optic neuropathies, as well.

Detection of Optic Disc Abnormalities in Color Fundus Photographs Using Deep Learning.

To date, deep learning-based detection of optic disc abnormalities in color fundus photographs has mostly been limited to the field of glaucoma. However, many life-threatening systemic and neurological conditions can manifest as optic disc abnormalities. In this study, we aimed to extend the application of deep learning (DL) in optic disc analyses to detect a spectrum of nonglaucomatous optic neuropathies. Using transfer learning, we trained a ResNet-152 deep convolutional neural network (DCNN) to distinguish between normal and abnormal optic discs in color fundus photographs (CFPs). Our training data set included 944 deidentified CFPs (abnormal 364; normal 580). Our testing data set included 151 deidentified CFPs (abnormal 71; normal 80). Both the training and testing data sets contained a wide range of optic disc abnormalities, including but not limited to ischemic optic neuropathy, atrophy, compressive optic neuropathy, hereditary optic neuropathy, hypoplasia, papilledema, and toxic optic neuropathy. The standard measures of performance (sensitivity, specificity, and area under the curve of the receiver operating characteristic curve (AUC-ROC)) were used for evaluation. During the 10-fold cross-validation test, our DCNN for distinguishing between normal and abnormal optic discs achieved the following mean performance: AUC-ROC 0.99 (95 CI: 0.98-0.99), sensitivity 94% (95 CI: 91%-97%), and specificity 96% (95 CI: 93%-99%). When evaluated against the external testing data set, our model achieved the following mean performance: AUC-ROC 0.87, sensitivity 90%, and specificity 69%. In summary, we have developed a deep learning algorithm that is capable of detecting a spectrum of optic disc abnormalities in color fundus photographs, with a focus on neuro-ophthalmological etiologies. As the next step, we plan to validate our algorithm prospectively as a focused screening tool in the emergency department, which if successful could be beneficial because current practice pattern and training predict a shortage of neuro-ophthalmologists and ophthalmologists in general in the near future.

Unsupervised Machine Learning Identifies Quantifiable Patterns of Visual Field Loss in Idiopathic Intracranial Hypertension.

PurposeArchetypal analysis, a form of unsupervised machine learning, identifies archetypal patterns within a visual field (VF) dataset such that any VF is described as a weighted sum of its archetypes (ATs) and has been used to quantify VF defects in glaucoma. We applied archetypal analysis to VFs affected by nonglaucomatous optic neuropathy caused by idiopathic intracranial hypertension (IIH).MethodsWe created an AT model from 2862 VFs prospectively collected from 330 eyes in the IIH Treatment Trial (IIHTT). We compared baseline IIH AT patterns with their descriptive VF classifications from the IIHTT.ResultsThe optimum IIH AT model yielded 14 ATs resembling VF patterns reported in the IIHTT. Baseline VFs contained four or fewer meaningful ATs in 147 (89%) of study eyes. AT2 (mild general VF depression pattern) demonstrated the greatest number of study eyes with meaningful AT weight at baseline (n = 114), followed by AT1 (n = 91). Other ATs captured patterns of blind spot enlargement, hemianopia, arcuate, nasal defects, and more nonspecific patterns of general VF depression. Of all ATs, AT1 (normal pattern) had the strongest correlation with mean deviation (r = 0.69, P < 0.001). For 65 of the 93 VFs with a dominant AT, this AT matched the expert classification.ConclusionsArchetypal analysis identifies quantifiable, archetypal VF defects that resemble those commonly seen in IIH.Translational RelevanceArchetypal analysis provides a quantitative, objective method of measuring and monitoring disease-specific regional VF defects in IIH.

Effects of primary glaucoma on sleep quality and daytime sleepiness of patients residing at an equatorial latitude.

To investigate the impact of primary glaucoma on sleep quality and daytime sleepiness of patients. Prospective cross-sectional study with consecutive sampling in South-East Asian population was performed. Validated questionnaires: the Pittsburg Sleep Quality Index (PSQI) and Epworth Sleepiness Scale (ESS) were administered prospectively. Subjects with non-glaucomatous optic neuropathy or concomitant retinal pathology were excluded. Glaucoma severity was based on HVF 24-2 perimetry. Binocular single vision was represented based on the better eye. Frequency of and predictive factors for poor sleep quality and excessive daytime sleepiness were compared. A total of 79 primary open angle glaucoma (POAG), 27 primary angle-closure glaucoma (PACG) patients, and 89 controls were recruited. PACG patients had higher median PSQI scores (P=0.004) and poorer sleep quality (P<0.001). Compared to controls, PACG patients were 3.34 times more likely to have poor sleep quality (P=0.008), which remained significant after adjustment for demographics (P=0.016) and predictive variables (P=0.013). PACG patients have poorer sleep quality when visual acuity (VA) was 6/15 or worse (P=0.009). Univariate and multivariate analysis of predictive variables for poor sleep quality and daytime sleepiness did not find statistical significance. PACG patients have poorer sleep quality but not daytime sleepiness. This is important in South-East Asian population with heavy disease burden. Evaluations on sleep disturbances can be considered to provide more holistic care.

Bruch's Membrane Opening Minimum Rim Width Provides Objective Differentiation between Glaucoma and Nonglaucomatous Optic Neuropathies

A challenging clinical scenario is distinguishing between normal tension glaucoma (NTG) and non-glaucomatous optic neuropathies (NGON). The key to the assessment remains identifying the presence of optic nerve head cupping. Recent optical coherence tomography (OCT) measurements now allow objective assessment of cupping by minimum rim width at Bruch's membrane opening (MRW-BMO). This study assessed the hypothesis that the MRW-BMO measurement quantifies cupping and therefore can differentiate between NTG and NGON. Diagnostic evaluation with area under the curve. Setting: multicenter tertiary hospitals and outpatient clinics. 81 eyes of 81 patients were enrolled, 27 with NTG and 54 with NGON, including ischemic optic neuropathy, previous optic neuritis, and compressive and inherited optic neuropathies. All NGON patients with intraocular pressure >21mm Hg, narrow drainage angles, or a family history of glaucoma were excluded. Observational procedure: optic disc OCT images were obtained of both the retinal nerve fiber layer thickness and the MRW-BMO. the utility of the MRW-BMO in differentiating GON from NGON was assessed using the area under the curve (AUC) estimated from a logistic regression model. The 5-fold cross-validated AUC for glaucoma versus nonglaucoma from logistic regression models using MRW-BMO values from all sectors was 0.95 (95% confidence interval: 0.86-1.00). The measurement of MRW-BMO effectively differentiates between NTG and NGON with a high level of sensitivity and specificity. Incorporating this measurement into routine glaucoma assessment may provide a robust method of assisting clinicians to improve diagnosis and therefore treatment of optic nerve diseases.

Different damage patterns of retinal nerve fiber layer and ganglion cell-inner plexiform layer between early glaucoma and non-glaucomatous optic neuropathy.

To compare the damage pattern of the peripapillary retinal nerve fiber layer (pRNFL) and the macular ganglion cell-inner plexiform layer (mGCIPL) between early glaucomatous and non-glaucomatous optic neuropathy (EGON and NGON). It is a cross-sectional study. Thirty-eight healthy controls, 74 EGONs and 70 NGONs with comparable average pRNFL loss were included. The NGON group included 23 eyes of optic neuritis (ON), 13 eyes of hereditary optic neuropathy (HON), 19 eyes of toxic optic neuropathy (TON) and 15 eyes of compressive neuropathy (CON). The sectoral pRNFL and mGCIPL thickness obtained by high definition optical coherence tomography were analyzed. Compared to normal controls, the pRNFL thickness in all quadrants showed a decrease in both EGON and NGON group (P<0.001), but the average pRNFL thickness of EGON group was not different to that of NGON group (P=0.94). The inferior and superior pRNFL was thinner in EGON group compared to NGON group (P<0.001). The temporal pRNFL was thinner in NGON group compared to EGON group (P<0.001). No statistically significant difference was found in nasal pRNFL between EGON and NGON. While the nasal pRNFL was thinner in CON than other three types of NGON (P=0.01), no statistically significant difference was found in other three quadrantal pRNFL among the four types of NGON (P>0.05). The mGCIPL of EGON and NGON group were thinner than control group (P<0.001). In EGON group the severest sites of mGCIPL reduction was located at inferotemporal and inferior sectors. While, compared to EGON group, the average mGCIPL of NGON group were significantly thinner, especially in superonasal and inferonasal sectors (P<0.001). The damage pattern of pRNFL and mGCIPL caused by glaucoma is distinct from other NGON such as ON, TON, HON and CON, and this characteristic damage pattern is helpful in differentiating early glaucoma from other NGON.

Efficacy for Differentiating Nonglaucomatous Versus Glaucomatous Optic Neuropathy Using Deep Learning Systems

We sought to assess the performance of deep learning approaches for differentiating nonglaucomatous optic neuropathy with disc pallor (NGON) vs glaucomatous optic neuropathy (GON) on color fundus photographs by the use of image recognition. Development of an Artificial Intelligence Classification algorithm. This single-institution analysis included 3815 fundus images from the picture archiving and communication system of Seoul National University Bundang Hospital consisting of 2883 normal optic disc images, 446 NGON images, and 486 GON images. The presence of NGON and GON was interpreted by 2 expert neuro-ophthalmologists and had corroborated evidence on visual field testing and optical coherence tomography. Images were preprocessed in size and color enhancement before input. We applied the convolutional neural network (CNN) of ResNet-50 architecture. The area under the precision-recall curve (average precision) was evaluated for the efficacy of deep learning algorithms to assess the performance of classifying NGON and GON. The diagnostic accuracy of the ResNet-50 model to detect GON among NGON images showed a sensitivity of 93.4% and specificity of 81.8%. The area under the precision-recall curve for differentiating NGON vs GON showed an average precision value of 0.874. False positive cases were found with extensive areas of peripapillary atrophy and tilted optic discs. Artificial intelligence-based deep learning algorithms for detecting optic disc diseases showed excellent performance in differentiating NGON and GON on color fundus photographs, necessitating further research for clinical application.