Explore the vast range of titles available.

To evaluate the Childhood Glaucoma Research Network (CGRN) classification system and describe the prevalence of each subtype according to this classification. Retrospectively, the medical records of 205 consecutive childhood glaucoma and glaucoma suspect patients at an urban tertiary care center were reviewed. The initial diagnosis and new diagnosis according to CGRN classification were recorded. All patients fit one of the seven categories of the new classification. Seventy-one percent of diagnoses were changed upon reclassification. Twenty-three percent of patients had primary glaucoma (juvenile open-angle glaucoma and primary congenital glaucoma [PCG]); 36% had secondary glaucoma (glaucoma associated with nonacquired ocular anomalies; glaucoma associated with nonacquired systemic disease or syndrome; glaucoma associated with acquired condition; and glaucoma following cataract surgery); and 39% were glaucoma suspect. Of the patients diagnosed with glaucoma, PCG was the most common diagnosis, seen in 32% of patients. The CGRN classification provides a useful method of classifying childhood glaucoma.

Background and Objectives: Open-angle glaucoma subtypes share a structural phenotype but differ in pathophysiology: pseudoexfoliative glaucoma (PXG) involves vascular endothelial dysfunction associated with deposition of exfoliative material, whereas normal-tension glaucoma (NTG) reflects primary vascular dysregulation in the absence of elevated intraocular pressure. We characterized subtype-specific OCT angiography (OCTA) profiles obtained from a 3 × 3 mm macular scan and evaluated their discriminatory power for pairwise subtype classification. Materials and Methods: This was a single-center, cross-sectional study of 304 eyes: 198 glaucomatous eyes—primary open-angle glaucoma (POAG, glaucoma simplex in our clinical nomenclature), n = 102; PXG (glaucoma capsulare), n = 68; NTG (glaucoma sine tensio), n = 28—and 106 healthy controls. The Cirrus HD-OCT 5000 AngioPlex 3 × 3 mm OCTA protocol was used to assess vessel density (VD), perfusion density, foveal avascular zone (FAZ) morphology, ganglion cell complex (GCC), and retinal nerve fiber layer (RNFL) thickness. Analyses included Kruskal–Wallis tests with Bonferroni post hoc correction, ROC analysis with DeLong comparison of combined versus structural-only models, multivariate regression, and an exploratory XGBoost classifier with SHAP-based interpretation. Results: VD Inner and Perfusion Inner were lower in PXG (16.37 ± 3.33%; 0.31 ± 0.05) than in POAG (18.73 ± 3.41%; 0.34 ± 0.05; both p < 0.001); Perfusion Inner was also lower than in NTG (p < 0.05). FAZ Area was largest in NTG (0.27 ± 0.11 mm2) and greater than in PXG (0.19 ± 0.08; p < 0.01); FAZ Circularity differed across subtypes (p < 0.001). Combined OCTA–structural models outperformed structural-only models for POAG vs. PXG (DeLong p = 0.002) and for PXG vs. NTG (AUC = 0.770; p = 0.010). Sector-resolved Spearman analysis revealed subtype-specific coupling: in NTG, VD Inner and Perfusion Inner correlated with the inferior RNFL (r = 0.53 and r = 0.52; both p < 0.01); in PXG, coupling shifted nasally (r = 0.41 and r = 0.46; both p < 0.001). The exploratory XGBoost classifier separated glaucoma from controls with an internal cross-validated AUC of 0.975 ± 0.008 (5-fold CV; not externally validated); FAZ Circularity (mean |SHAP| = 0.418) and FAZ Area (0.411) were the top inter-subtype features, supported by case-level SHAP. RNFL avg and average GCC independently predicted MD across subtypes; in PXG, Perfusion Inner also predicted MD (β = −32.78; p = 0.032). Conclusions: In this single-center, cross-sectional cohort, OCTA revealed subtype-associated macular microvascular profiles that are complementary to structural OCT. Reduced vessel and perfusion density characterized PXG, whereas FAZ enlargement and reduced circularity distinguished NTG and PXG. Vascular–structural coupling was nasal-predominant in PXG and inferior-predominant in NTG. Combined multimodal models outperformed structural-only approaches. Macular perfusion additionally predicted MD in PXG. The XGBoost/SHAP analysis is exploratory; prospective and externally validated studies are required before clinical deployment.

Lowering of intraocular pressure is currently the only therapeutic measure for Glaucoma management. Many longterm, randomized trials have shown the efficacy of lowering IOP, either by a percentage of baseline, or to a specified level. This has lead to the concept of 'Target\" IOP, a range of IOP on therapy, that would stabilize the Glaucoma/prevent further visual field loss, without significantly affecting a patient's quality of life. A clinical staging of Glaucoma by optic nerve head evaluation and perimetric parameters, allows a patient's eye to be categorized as having - mild, moderate or severe Glaucomatous damage. An initial attempt should be made to achieve the following IOP range for both POAG or PACG after an iridotomy. In mild glaucoma the initial target IOP range could be kept as 15-17 mmHg, for moderate glaucoma 12-15 mmHg and in the severe stage of glaucomatous damage 10-12 mmHg. Factoring in baseline IOP, age, vascular perfusion parameters, and change on perimetry or imaging during follow up, this range may be reassessed over 6 months to a year. \"Target\" IOP requires further lowering when the patient continues to progress or develops a systemic disease such as a TIA. Conversely, in the event of a very elderly or sick patient with stable nerve and visual field over time, the target IOP could be raised and medications reduced. An appropriate use of medications/laser/surgery to achieve such a \"Target\" IOP range in POAG or PACG can maintain visual fields and quality of life, preventing Glaucoma blindness.

Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.

ForewordWe practice medicine in times of exponentially increasing medical knowledge. In 1950, it was estimated that the doubling time was 50 years; by 1980, it was 7 years and by 2010, 3.5 years. In 2020, it was projected to be just 73 days! To continue to practice evidence-based medicine and to provide the best possible care for our patients, clinicians need to adapt their strategies to keep their knowledge up to date. There will always be a role for critical appraisal of individual studies in the field of a clinician’s practice, but with such an increase in the volume of published research, it becomes impossible to appraise all relevant material. For this reason, sources of distilled knowledge, such as the EGS Guidelines, become essential references for best practice medicine.Rigorous methods for evidence synthesis, such as the systematic reviews overseen by Cochrane, provide a comprehensive summary of the current state of knowledge for important clinical questions. However, for many clinical uncertainties, there is little or no high-quality evidence, let alone an evidence synthesis. Where evidence is lacking, practice guidance needs to be built from expert opinion and consensus, while acknowledging the limitations of such an approach. Expert opinion, derived from sound medical knowledge and years of practice experience, also has an important role in understanding the relevance of lines of evidence and the nuances of implementing them in practice. Thus, the expert commentary around the evidence base given in these Guidelines is essential for proper implementation of published evidence. Importantly, the EGS Guidelines also include ‘Choosing wisely’ elements indicating actions which should be avoided due to insufficient evidence and/or unsubstantiated belief.Guidelines need regular updating to take account of new knowledge and aspects of clinical care that have not been given sufficient emphasis in the past. This 6th Edition of the EGS Guidelines includes an updated ‘evidence based’ section with new clinical questions and evidence-based answers. The section ‘What matters to patients’ has also been updated, recognising that, because Guidelines are typically written by clinicians for clinicians, there have been gaps in understanding the patient perspective. The updated section now has direct input from the Expert by Experience patient advisors in the EGS Patient Involvement Project and includes eight Tips for Doctors in their communication with patients.The Guidelines team, led by Drs Pazos, Traverso and Viswanathan, is to be congratulated for the 6th Edition of the Guidelines, updating and enhancing the previous edition, while maintaining the highly success format which gives a framework for glaucoma care, based on evidence synthesis and consensus expert opinion, and presented as a ‘How to’ manual for patient diagnosis and management.David (Ted) Garway-HeathGlaucoma UK Professor of Ophthalmology, UCLIntroductionThe European Glaucoma Society Guidelines are at the heart of our educational mission serving not only our members, but all involved in glaucoma care. Previous editions have been among the most widely accessed documents in the field, helping navigate the growing complexity of glaucoma research and clinical practice.This sixth edition reaffirms our commitment to evidence-based, patient-centred care. It builds on the solid foundations of past editions while embracing key innovations that reflect new scientific insights, emerging technologies, and evolving priorities in glaucoma care.For the first time, we collaborated closely with patient representatives through the Experts by Experience (EbE) group. Their contributions helped shape clinical questions and emphasized the importance of empathy, communication, and psychological support in managing glaucoma.The Guidelines maintain a dual purpose: to answer clinically relevant questions through a rigorous evidence-based approach, and to offer comprehensive, practical knowledge for daily use. We have updated existing recommendations and added new topics, including artificial intelligence, cost-effectiveness, adherence strategies, and paediatric glaucoma. A new “Choosing Wisely” section provides clear guidance to avoid low-value interventions. We extend our heartfelt thanks to all contributors and collaborators. This includes the many colleagues who authored and reviewed chapters with great care and expertise, the patient representatives who generously shared their lived experiences, and the team at the University of Genoa, who worked tirelessly to refine content and layout, ensuring clarity and consistency throughout.A very special word of thanks goes to the Editors Marta Pazos, Carlo Traverso, and Ananth Viswanathan, whose relentless dedication, critical oversight, and countless hours of work were instrumental in shaping the high quality and coherence of this edition. Their leadership and perseverance have been extraordinary. Together, these collective efforts have made this edition a cornerstone of our vision: to foster innovation, strengthen education, enhance communication, and promote best practices in glaucoma care across Europe and beyond.Ingeborg Stalmans and Luis Abegao Pintowww.eugs.orgEditorsMarta PazosCarlo E. TraversoAnanth ViswanathanEvidence-Based Working GroupMethodological oversightAugusto Azuara-Blanco Manuele MichelessiLeaders of evidence reviewAugusto Azuara-Blanco Manuele Michelessi (EGS)Marta Pazos (EGS)Riaz Qureshi (US Cochrane Eyes and Vision Group)Evidence reviewAugusto Azuara-Blanco João Barbosa Breda Carlo Alberto CutoloGerhard GarhöferManuele MichelessiMarta PazosVerena ProkoshAndrew TathamEvidence consensus panelLuís Abegão PintoAugusto Azuara-BlancoGauti JóhannessonMarta PazosNorbert PfeifferIngeborg StalmansAndrew TathamCarlo E. TraversoAnanth ViswanathanEvidence patient’s feedback panelStelios GeorgoulasMarta PazosAndrew TathamThe Guidelines Task ForceLuís Abegão PintoEleftherios AnastasopoulosFlorent AptelAugusto Azuara-Blanco (advisor)João Barbosa BredaLuca BagnascoChiara BonzanoRupert BourneCarlo Alberto CutoloTheodoros FilippopoulosPanayiota FountiGerhard GarhöferGus GazzardDimitrios GiannoulisMichele IesterAndreas KatsanosAnthony KhawajaMiriam KolkoAntoine LabbéSophie LemmensManuele MichelessiAna MiguelFrancesco OddoneMarta Pazos (chair)Verena ProkoschAlessandro RabioloAlexander K SchusterCédric SchweitzerRiccardo ScottoAndrew TathamMarc Toeteberg-HarmsFotis TopouzisCarlo E. Traverso (chair)Ananth Viswanathan (chair)The Guidelines Writers, Authors and ContributorsLuís Abegão PintoIke AhmedZeynep AktasAugusto Azuara-BlancoAlessandro BagnisJoão Barbosa BredaChiara BonzanoRupert BournePaola CassotanaMaria Francesca CordeiroCarlo Alberto CutoloBarbara CvenkelPanayiota FountiJulian García-FeijooGerhard GarhöferTed Garway-HeathDaniele GaudenziGus GazzardStelios GeorgoulasFrancisco GoñiFranz GrehnAnders HejilEsther HoffmanMichele IesterGauti JóhannessonAnthony KhawajaAnthony KingMiriam KolkoEvgenia KonstantakopoulouYves LachkarSanna LeinonenSophie LemmensKarl MerciecaManuele MichelessiFrancesco OddoneMarta PazosDorothea PetersNorbert PfeifferVerena ProkoschLuca RossettiJohn SalmonLeopold SchmettererCédric SchweitzerRiccardo ScottoIngeborg StalmansGordana Sunaric-MégevandIan TapplyAndrew TathamJohn ThygessenFotis TopouzisCarlo E. TraversoNeeru VallabhAnanth ViswanathanThe Guidelines Internal ReviewersLuís Abegão PintoElefterios AnastasopoulosFlorent AptelJoão Barbosa BredaHenny BeckersChiara BonzanoRupert Bourne (ccordinator)Alain BronCarlo A. CutoloBarbara CvenkelTheodoros FilippopoulosPanayiota FountiGerhard GarhöferDimitrios GiannoulisFranz GrehnIngrida JanulevicieneStylianos KandarakisAndreas KatsanosAnthony KhawajaAnthony KingJames KirwanMiriam KolkoAntoine LabbéSanna LeinonenSophie LemmensKeith MartinJose Maria Martinez de la CasaFrances Meier-GibbonsStefano MigliorAna MiguelGiovanni MontesanoMarta PazosSergei PetrovVerena ProkoschAlessandro RabioloCédric SchweitzerAlexander K SchusterIngeborg StalmansMarc Toeteberg-HarmsFotis TopouzisCarlo E. TraversoAnja TuulonenAnanth ViswanathanZoya VeselovskayaIlgaz YalvaçMia Zoric GeberThe Experts by Experience Group (patients’ panel)Kate BackhausCarol BronzeAsle HaaukasMona KriegDeborah LoiDora RoloSarah TaylerTeam of Clinica Oculistica - University of Genova, Policlinico Ospedale San martino IRCCS - for medical editing and graphicsLuca BagnascoAlessandro BagnisChiara BonzanoPaola Cassottana (EGS Fellow)Carlo CattiCarlo Alberto CutoloDaniele Gaudenzi (EGS Fellow)Michele IesterMaria MusolinoRiccardo ScottoCarlo E. TraversoExternal Reviews from Glaucoma Societies AGSAPGSWGALAGSPAGSThe EGS Executive CommitteeIngeborg Stalmans (President)Luís Abegão Pinto (Vice President)Norbert Pfeiffer (Treasurer)Fotis Topouzis (Past President)Gauti Jóhannesson (Secretary)Marta Pazos (Adjunct Secretary)Panayiota FountiSanna LeinonenLuca RosettiThe Board of The EGS FoundationCarlo E. TraversoFotis TopuzisJohn ThygesenFranz GrehnAnders HeijlAll contributors have provided the appropriate COI visible in detail at www.eugs.org/pages/guidesurgical/Glossary5-FU 5-fluorouracilAAC Acute angle closureACG Angle closure glaucomaAGIS Advanced glaucoma intervention studyAH Aqueous humourAI Artificial intelligenceALT Argon laser trabeculoplastyANA-LIS Singapore asymptomatic narrow angles laser iridotomy studyAS-OCT Anterior Segment OCTBAC Benzalkalonium chlorideCCT Central corneal thicknessCDR Cup to disc ratioCGRN Childhood glaucoma research networkCIGTS Collaborative initial glaucoma treatment studyCNTGS Collaborative normal tension glaucoma studyDCT Dynamic contour tonometryEAGLE Effectiveness of early lens extraction for the treatment of primary angle closure glaucomaEbE Expert by experienceEGPS European glaucoma prevention studyEGS European glaucoma societyEMA The European Medicines AgencyEMGT Early manifest glaucoma trialFC Fixed combinationFc Flow chartFDT Frequency doubling technologyFL Fixation lossesFN False ne

Purpose The Glaucoma Stereo Analysis Study (GSAS), a cross-sectional multicenter collaborative study, used a stereo fundus camera (nonmyd WX) to assess various morphological parameters of the optic nerve head (ONH) in glaucoma patients. We examined the associations between the Disc Damage Likelihood Scale (DDLS), a grading system for estimating glaucomatous ONH damage, and each parameter. Methods The study included 187 eyes of 187 patients with primary open-angle glaucoma or normal-tension glaucoma. ONH morphological parameters including the DDLS stage were calculated with prototype analysis software. Three independent graders classified each optic disc appearance into four different types: focal ischemic, myopic glaucomatous, senile sclerotic, and generalized enlargement. The correlations between the DDLS and patient characteristics or each ONH parameter were analyzed with Spearman’s rank correlation coefficient. Results The DDLS was correlated positively with baseline intraocular pressure and visual field pattern standard deviation, and negatively with visual field mean deviation. The DDLS was strongly correlated with vertical cup-to-disc ratio and horizontal cup-to-disc ratio positively, and with minimum rim-disc ratio negatively. The mean DDLS stage in the myopic glaucomatous type tended to be higher than the scores in other types. Conclusion The DDLS obtained through three-dimensional ONH analysis correlates well with the severity of glaucomatous ONH and visual field damage.

The study aimed to develop machine learning models that have strong prediction power and interpretability for diagnosis of glaucoma based on retinal nerve fiber layer (RNFL) thickness and visual field (VF). We collected various candidate features from the examination of retinal nerve fiber layer (RNFL) thickness and visual field (VF). We also developed synthesized features from original features. We then selected the best features proper for classification (diagnosis) through feature evaluation. We used 100 cases of data as a test dataset and 399 cases of data as a training and validation dataset. To develop the glaucoma prediction model, we considered four machine learning algorithms: C5.0, random forest (RF), support vector machine (SVM), and k-nearest neighbor (KNN). We repeatedly composed a learning model using the training dataset and evaluated it by using the validation dataset. Finally, we got the best learning model that produces the highest validation accuracy. We analyzed quality of the models using several measures. The random forest model shows best performance and C5.0, SVM, and KNN models show similar accuracy. In the random forest model, the classification accuracy is 0.98, sensitivity is 0.983, specificity is 0.975, and AUC is 0.979. The developed prediction models show high accuracy, sensitivity, specificity, and AUC in classifying among glaucoma and healthy eyes. It will be used for predicting glaucoma against unknown examination records. Clinicians may reference the prediction results and be able to make better decisions. We may combine multiple learning models to increase prediction accuracy. The C5.0 model includes decision rules for prediction. It can be used to explain the reasons for specific predictions.

This review describes a scheme for diagnosis of glaucoma in population based prevalence surveys. Cases are diagnosed on the grounds of both structural and functional evidence of glaucomatous optic neuropathy. The scheme also makes provision for diagnosing glaucoma in eyes with severe visual loss where formal field testing is impractical, and for blind eyes in which the optic disc cannot be seen because of media opacities.

This study evaluates the performance of a machine learning model in classifying glaucoma severity using color fundus photographs. Glaucoma severity grading was based on the Hodapp-Parrish-Anderson (HPA) criteria incorporating the mean deviation value, defective points in the pattern deviation probability map, and defect proximity to the fixation point. The dataset of 2,940 fundus photographs from 1,789 patients was matched with visual field tests and equally classified into three classes: normal, mild-moderate, and severe glaucoma stages. The EfficientNetB7, a convolutional neural network model, was trained on these images using transfer learning and fine-tuning techniques. The model achieved an overall accuracy of 0.871 (95% CI, 0.822–0.919). For normal, mild-moderate, and severe classes, the area under the curve (AUC) values were 0.988, 0.932, and 0.963; sensitivity 0.903, 0.823, and 0.887; and specificity 0.960, 0.911, and 0.936, respectively. Analysis of the confusion matrix revealed the impact of structural-functional relationships in glaucoma on model performance. In conclusion, the EfficientNetB7 demonstrated high accuracy in classifying glaucoma severity based on the HPA criteria using fundus photographs, offering potential for clinical application in glaucoma diagnosis and management.

Background Glaucoma is the leading cause of irreversible blindness worldwide. There tends to be a lower reporting of glaucoma in Africa compared to other blinding conditions in global burden data. Research findings of glaucoma in Nigeria will significantly increase our understanding of glaucoma in Nigeria, in people of the West African diaspora and similar population groups. We determined the prevalence and types of glaucoma in Nigeria from the Nigeria National Blindness and Visual Impairment cross-sectional Survey of adults aged ≥40 years. Methods Multistage stratified cluster random sampling with probability-proportional-to-size procedures were used to select a nationally representative sample of 15,027 persons aged ≥40 years. Participants had logMAR visual acuity measurement, FDT visual function testing, autorefraction, A-scan biometry and optic disc assessment. Participants with visual acuity of worse than 6/12 or suspicious optic discs had detailed examination including Goldmann applanation tonometry, gonioscopy and fundus photography. Disc images were graded by Moorfields Eye Hospital Reading Centre. Glaucoma was defined using International Society of Geographical and Epidemiological Ophthalmology criteria; and classified into primary open-angle or primary angle-closure or secondary glaucoma. Diagnosis of glaucoma was based on ISGEO classification. The type of glaucoma was determined by gonioscopy. Results A total of 13,591 participants in 305 clusters were examined (response rate 90.4 %). Optic disc grading was available for 25,289 (93 %) eyes of 13,081 (96 %) participants. There were 682 participants with glaucoma; a prevalence of 5.02 % (95 % CI 4.60–5.47). Among those with definite primary glaucoma that had gonioscopy ( n  = 243), open-angle glaucoma was more common (86 %) than angle-closure glaucoma (14 %). 8 % of glaucoma was secondary with the commonest causes being couching (38 %), trauma (21 %) and uveitis (19 %). Only 5.6 % (38/682) of participants with glaucoma knew they had the condition. One in every 5 persons with glaucoma (136;20 %) was blind i.e., visual acuity worse than 3/60. Conclusion Nigeria has a high prevalence of glaucoma which is largely open-angle glaucoma. A high proportion of those affected are blind. Secondary glaucoma was mostly as a consequence of procedures for cataract. Public health control strategies and high quality glaucoma care service will be required to reduce morbidity and blindness from glaucoma.