[Three-component tear substitute as preoperative support in glaucoma surgery]. / Primenenie trekhkomponentnogo slezozamenitelya v meditsinskom soprovozhdenii khirurgii glaukomy.

PURPOSE: This study investigates the effect of preoperative preparation with the three-component tear substitute Stillavit on the outcomes of glaucoma surgery in patients on long-term topical glaucoma therapy. MATERIAL AND METHODS: The study included 63 patients (63 eyes) with glaucoma, among them 30 (30 eyes) were women and 33 (33 eyes) were men. Group 1 consisted of 33 patients (33 eyes) receiving long-term topical antihypertensive therapy with preservatives and tear substitute Stillavit 3 times a day for 1 month as preoperative preparation. Group 2 included 30 patients (30 eyes) receiving similar antihypertensive therapy who were not prescribed preoperative medications. All patients underwent a comprehensive ophthalmological examination and computer tomography of the filtering blebs (FB) on Visante system (Carl Zeiss Meditec, Germany) before surgery, as well as 1 week, 1, 3, and 6 months after surgery. RESULTS: Mean intraocular pressure (IOP) elevation at 6 months postoperatively was significantly higher in the group that did not undergo preoperative preparation with a tear substitute, compared to the group of patients who received the tear substitute Stillavit preoperatively. According to anterior segment optical coherence tomography, a greater number of favorable diffuse FB were noted in the group that received the tear substitute Stillavit in preparation for surgery - 87.9%, while in group 2 (without preparation) - 66.7%. Odds ratio calculation between the two groups revealed that preoperative preparation with the tear substitute Stillavit reduced the odds of IOP elevation to 20 mm Hg or higher at 3 months postoperatively by more than 10 times. CONCLUSION: The use of tear substitute Stillavit in preparation for anti-glaucoma surgery reduces the chances of IOP elevation in the early postoperative period by more than 10 times, which may indicate a reduction in the rate of FB scarring due to the beneficial effect of the drug's components on the precorneal tear film.

[Application of artificial intelligence in glaucoma. Part 1. Neural networks and deep learning in glaucoma screening and diagnosis]. / Primenenie metodov iskusstvennogo intellekta pri glaukome. Chast' 1. Neiroseti i glubokoe obuchenie v skrininge i diagnostike glaukomy.

This article reviews literature on the use of artificial intelligence (AI) for screening, diagnosis, monitoring and treatment of glaucoma. The first part of the review provides information how AI methods improve the effectiveness of glaucoma screening, presents the technologies using deep learning, including neural networks, for the analysis of big data obtained by methods of ocular imaging (fundus imaging, optical coherence tomography of the anterior and posterior eye segments, digital gonioscopy, ultrasound biomicroscopy, etc.), including a multimodal approach. The results found in the reviewed literature are contradictory, indicating that improvement of the AI models requires further research and a standardized approach. The use of neural networks for timely detection of glaucoma based on multimodal imaging will reduce the risk of blindness associated with glaucoma.

Accuracy of a novel method for IOP measurement without applying pressure on the cornea.

PURPOSE: To assess the accuracy of a prototype novel instrument for intra ocular pressure (IOP) measurements not involving corneal pressure application. DESIGN: Prospective case control study. METHODS: An institutional study including 16 healthy volunteers without ocular pathology. IOP in both eyes of the participants was measured four times in different body positions with the novel prototype and reference instrument (Goldmann applanation tonometer (GAT) or iCare (iCare Finland OY, Vantaa, Finland)). IOP results were compared between the prototype and the reference instruments in 116 pairs of measurement. RESULTS: Overall no statistically significant difference was found between the presented prototype and the reference instrument. Stratifying measurements by instrument used revealed no significant difference for GAT and statistical significant (yet clinically insignificant) difference for iCare. CONCLUSIONS: The presented prototype demonstrates good clinical agreement of IOP measuring results with reference instruments Further large-scale studies assessing this instrument in glaucoma patients are warranted.

[Economic index - predictor of premature aging.]

It is widely known that in economically developed countries there is an increase in the proportion of older people. However, the problem of the influence of territorial features of economic development on the rate of population aging is not sufficiently covered. The goal was to study the impact of economic development indicators (EDI) on the processes of premature aging of the population. The materials were statistical collections of the Ministry of Health of Russia and Russian Statistics Service for 2011-2019. The highest incidence was characteristic of cataracts and glaucoma. A direct correlation has been established between the EDI and the age-specific incidence index (ASII) of cataracts (r=0,31; p=0,00436). A group of regions with a high level of economic development was characterized by a higher value of ASII, which, as a rule, corresponds to the later development of the disease.

Research on segmentation model of optic disc and optic cup in fundus.

BACKGROUND: Glaucoma is a worldwide eye disease that can cause irreversible vision loss. Early detection of glaucoma is important to reduce vision loss, and retinal fundus image examination is one of the most commonly used solutions for glaucoma diagnosis due to its low cost. Clinically, the cup-disc ratio of fundus images is an important indicator for glaucoma diagnosis. In recent years, there have been an increasing number of algorithms for segmentation and recognition of the optic disc (OD) and optic cup (OC), but these algorithms generally have poor universality, segmentation performance, and segmentation accuracy. METHODS: By improving the YOLOv8 algorithm for segmentation of OD and OC. Firstly, a set of algorithms was designed to adapt the REFUGE dataset's result images to the input format of the YOLOv8 algorithm. Secondly, in order to improve segmentation performance, the network structure of YOLOv8 was improved, including adding a ROI (Region of Interest) module, modifying the bounding box regression loss function from CIOU to Focal-EIoU. Finally, by training and testing the REFUGE dataset, the improved YOLOv8 algorithm was evaluated. RESULTS: The experimental results show that the improved YOLOv8 algorithm achieves good segmentation performance on the REFUGE dataset. In the OD and OC segmentation tests, the F1 score is 0.999. CONCLUSIONS: We improved the YOLOv8 algorithm and applied the improved model to the segmentation task of OD and OC in fundus images. The results show that our improved model is far superior to the mainstream U-Net model in terms of training speed, segmentation performance, and segmentation accuracy.

A Practical Framework for the Integration of Structural Data Into Perimetric Examinations.

Purpose: We sought to develop and evaluate a practical framework that supports structurally enhanced perimetric examinations. Methods: Two perimetric strategies were compared: standard Zippy Estimation through Sequential Testing (ZEST) procedure, a traditional visual field test with population-based prior distributions, and structural-ZEST (S-ZEST), enhanced with individual optical coherence tomography data to determine the starting parameters. The integration and collection of data was facilitated by a bespoke application developed in Shiny R (R Studio). The test was implemented using the Open Perimetry Interface on the Compass perimeter (CentreVue-iCare, Italy). The strategies were evaluated via simulations and on 10 visually healthy participants. The usability of the application was assessed in a simulated environment with 10 test users. Results: In simulations, the S-ZEST improved test speed in patients with glaucoma. In the practical implementation, there was a statistically significant decrease in the testing time (approximately 26%) and in the number of presentations per test with S-ZEST (P < 0.001). The structure-function relationship was similar between the two strategies. The time taken for users to complete the sequence of actions on the application was 52.9 ± 11.5 seconds (mean ± standard deviation). Conclusions: Structurally enhanced perimetric examination can significantly improve test time in healthy subjects and can be delivered through a user-friendly interface. Further testing will need to assess feasibility and performance of S-ZEST in patients with glaucoma. Translational Relevance: We have developed a user-friendly web application based within the Shiny environment for R, which implements an automated extraction of optical coherence tomography data from raw files and performs real-time calculations of structural features to inform the perimetric strategy.

[Virtual reality perimetry and home perimetry]. / "Virtual reality perimetry" und "home perimetry".

In this article virtual reality (VR)-based procedures for home perimetry (HP) are described and an overview is given of which procedures can already be used today.

An eternal hunt for glaucoma.

In the first issue of Graefe's Archive from 1854, Albrecht von Graefe wrote about glaucoma. Glaucoma comes from the Greek word "glaukos," gleaming, which was first used by Homer around 800 BCE. Since then, glaukos and glaucoma have taken on many different meanings. The terms blindness, cataract and glaucoma were used interchangeably and twisted together in incomprehensible contexts. Over 2500 years of glaucoma theories were upset by the discovery of the ophthalmoscope in 1851. The first reports of increased intraocular pressure appeared in the mid-seventeenth century, but it took over 200 years for this elevated pressure to be accepted by the ophthalmological community. The discovery of glaucoma simplex in 1861 was an important step forward. What did doctors know about glaucoma before 1850 and why did it take so long to classify glaucoma in its various categories? And why is it that we still do not know what the cause is for primary open angle glaucoma? I will try to answer some of these questions after a historical overview.

Agreement of tonometer for measuring intraocular pressure in Wistar rats: a systematic review.

Glaucoma is the most common cause of irreversible blindness in the world. It is associated with elevated intraocular pressure (IOP). Fluctuations in tonometer readings have implications for glaucoma research, where accurate IOP measurements are vital for evaluating disease progression and treatment efficacy. Researchers should carefully select the appropriate tonometer and consider biases associated with different tonometers. Validation against standard measurements can improve IOP measurement accuracy in rat models. In conclusion, this systematic review will emphasize on the importance of selecting the appropriate tonometer for IOP measurement in rat models, considering potential biases and their implications for glaucoma research. Accurate and consistent IOP measurement in rat models is crucial for understanding glaucoma pathophysiology and developing effective treatments. This systematic review aims to assess agreement among tonometers used for measuring IOP in Wistar rat models primarily focusing on TonoLab, TonoVet, and Tono-pen. The review was conducted using PRISMA guidelines. Two articles were included for qualitative synthesis. The studies compared manometric IOP with TonoLab, rebound tonometer, and Tono-pen XL readings. It was observed that TonoLab consistently underestimated IOP, while Tono-pen XL tended to overestimate IOP compared to manometric measurements. The study's findings will help researchers in making decisions about tonometer selection, leading to more reliable outcomes in glaucoma research using rat models. Further research, specifically RCT's (randomized controlled trial) is needed to confirm the results and enhance IOP measurement precision in rat models.

Assessing the Efficacy of Synthetic Optic Disc Images for Detecting Glaucomatous Optic Neuropathy Using Deep Learning.

Purpose: Deep learning architectures can automatically learn complex features and patterns associated with glaucomatous optic neuropathy (GON). However, developing robust algorithms requires a large number of data sets. We sought to train an adversarial model for generating high-quality optic disc images from a large, diverse data set and then assessed the performance of models on generated synthetic images for detecting GON. Methods: A total of 17,060 (6874 glaucomatous and 10,186 healthy) fundus images were used to train deep convolutional generative adversarial networks (DCGANs) for synthesizing disc images for both classes. We then trained two models to detect GON, one solely on these synthetic images and another on a mixed data set (synthetic and real clinical images). Both the models were externally validated on a data set not used for training. The multiple classification metrics were evaluated with 95% confidence intervals. Models' decision-making processes were assessed using gradient-weighted class activation mapping (Grad-CAM) techniques. Results: Following receiver operating characteristic curve analysis, an optimal cup-to-disc ratio threshold for detecting GON from the training data was found to be 0.619. DCGANs generated high-quality synthetic disc images for healthy and glaucomatous eyes. When trained on a mixed data set, the model's area under the receiver operating characteristic curve attained 99.85% on internal validation and 86.45% on external validation. Grad-CAM saliency maps were primarily centered on the optic nerve head, indicating a more precise and clinically relevant attention area of the fundus image. Conclusions: Although our model performed well on synthetic data, training on a mixed data set demonstrated better performance and generalization. Integrating synthetic and real clinical images can optimize the performance of a deep learning model in glaucoma detection. Translational Relevance: Optimizing deep learning models for glaucoma detection through integrating DCGAN-generated synthetic and real-world clinical data can be improved and generalized in clinical practice.

Applicability of Oculomics for Individual Risk Prediction: Repeatability and Robustness of Retinal Fractal Dimension Using DART and AutoMorph.

Purpose: To investigate whether fractal dimension (FD)-based oculomics could be used for individual risk prediction by evaluating repeatability and robustness. Methods: We used two datasets: "Caledonia," healthy adults imaged multiple times in quick succession for research (26 subjects, 39 eyes, 377 color fundus images), and GRAPE, glaucoma patients with baseline and follow-up visits (106 subjects, 196 eyes, 392 images). Mean follow-up time was 18.3 months in GRAPE; thus it provides a pessimistic lower bound because vasculature could change. FD was computed with DART and AutoMorph. Image quality was assessed with QuickQual, but no images were initially excluded. Pearson, Spearman, and intraclass correlation (ICC) were used for population-level repeatability. For individual-level repeatability, we introduce measurement noise parameter λ, which is within-eye standard deviation (SD) of FD measurements in units of between-eyes SD. Results: In Caledonia, ICC was 0.8153 for DART and 0.5779 for AutoMorph, Pearson/Spearman correlation (first and last image) 0.7857/0.7824 for DART, and 0.3933/0.6253 for AutoMorph. In GRAPE, Pearson/Spearman correlation (first and next visit) was 0.7479/0.7474 for DART, and 0.7109/0.7208 for AutoMorph (all P < 0.0001). Median λ in Caledonia without exclusions was 3.55% for DART and 12.65% for AutoMorph and improved to up to 1.67% and 6.64% with quality-based exclusions, respectively. Quality exclusions primarily mitigated large outliers. Worst quality in an eye correlated strongly with λ (Pearson 0.5350-0.7550, depending on dataset and method, all P < 0.0001). Conclusions: Repeatability was sufficient for individual-level predictions in heterogeneous populations. DART performed better on all metrics and might be able to detect small, longitudinal changes, highlighting the potential of robust methods.

SALUS-A Study on Self-Tonometry for Glaucoma Patients: Design and Implementation of the Electronic Case File.

BACKGROUND: In times of omnipresent digitization and big data, telemedicine and electronic case files (ECFs) are gaining ground for networking between players in the health care sector. In the context of the SALUS study, this approach is applied in practice in the form of electronic platforms to display and process disease-relevant data of glaucoma patients. OBJECTIVES: The SALUS ECF is designed and implemented to support data acquisition and presentation, monitoring, and outcome control for patients suffering from glaucoma in a clinical setting. Its main aim is to provide a means for out- and inpatient exchange of information between various stakeholders with an intuitive user interface in ophthalmologic care. Instrument data, anamnestic data, and diagnostic assessments need to be accessible and historic data stored for patient monitoring. Quality control of the data is ensured by a reading center. METHODS: Based on an intensive requirement analysis, we implemented the ECF as a web-based application in React with a Datomic back-end exposing REST and GraphQL APIs for data access and import. A flexible role management was developed, which addresses the various tasks of multiple stakeholders in the SALUS study. Data security is ensured by a comprehensive encryption concept. We evaluated the usability and efficiency of the ECF by measuring the durations medical doctors need to enter and work with the data. RESULTS: The evaluation showed that the ECF is time-saving in comparison to paper-based assessments and offers supportive monitoring and outcome control for numerical and imaging-related data. By allowing patients and physicians to access the digital ECF, data connectivity as well as patient autonomy were enhanced. CONCLUSION: ECFs have a great potential to efficiently support all patients and stakeholders involved in the care of glaucoma patients. They benefit from the efficient management and view of the data tailored to their specific role.

The accuracy of self-reported family history in glaucoma in Brazil.

PURPOSE: To evaluate the accuracy of a positive self-reported glaucoma family history. MATERIAL AND METHODS: Cross-sectional study. Each subject was asked if they had a first-degree relative diagnosed with glaucoma. If their answer was affirmative, the relative was invited to attend on ophthalmic evaluation and underwent complementary exams to confirm or exclude the glaucoma diagnosis. Only one relative was included per subject. RESULTS: We included 204 subjects in the study (102 subjects and their respective relatives). The accuracy of family history of glaucoma was 76.96% of the cases. In the univariable analysis, subjects with college degree had 2.34 [(P = 0.010; 95% confidence interval (CI) 1.18-4.63)], with higher family income 3.72 (P = 0.003; 95% CI 1.57-8.85) and those with health insurance 3.42 (P = 0.001; 95% CI 1.67-6.98) more chances to have a true positive family history for glaucoma. In the multivariable logistic regression analysis, none of the variables presented significant association. CONCLUSION: Around 24% of patients may not provide reliable information about family history for glaucoma. When asking about a glaucoma family history, clinicians should consider the real accuracy of this self-reported data.

Repeatability of a Virtual Reality Headset Perimeter in Glaucoma and Ocular Hypertensive Patients.

Purpose: The VisuALL S is an automated, static threshold, virtual reality-based perimeter for mobile evaluation of the visual field. We examined same-day and 3-month repeatability. Methods: Adult participants with a diagnosis of glaucoma or ocular hypertension underwent two VisuALL 24-2 Normal T- Full threshold strategy tests at baseline and one additional exam at 3 months for each eligible eye. Spearman, intraclass correlation coefficients (ICCs), and Bland-Altman plots were used to assess the correlation of individual point sensitivities and mean deviation (MD) among three tests. Results: Eighty-eight eyes (44 participants) were included. Average age was 68.1 ± 14.3 years, and 60.7% were male. VisuALL MD was highly correlated between tests (intravisit: r = 0.89, intervisit: r = 0.82; P < 0.001 for both). Bland-Altman analysis showed an average difference in intravisit MD of -0.67 dB (95% confidence interval [CI], -6.04 to 4.71 dB) and -0.15 dB (95% CI, -8.04 to 7.73 dB) for intervisit exams. Eight-five percent of pointwise intravisit ICCs were above 0.75 (range, 0.63 to 0.93), and 65% of pointwise intervisit ICCs were above 0.75 (range, 0.55 to 0.91). Conclusions: VisuALL demonstrated high correlation of MD between tests and good repeatability for individual point sensitivities among three tests in 3 months, except at the points around the blind spot and superiorly. Translational Relevance: The preliminary reproducibility results for VisuALL are encouraging. Its portable design makes it a potentially useful tool for patients with glaucoma, enabling more frequent assessments both at home and in clinical settings.

Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records.

Purpose: To develop machine learning (ML) and deep learning (DL) models to predict glaucoma surgical outcomes, including postoperative intraocular pressure, use of ocular antihypertensive medications, and need for repeat surgery. Methods: We identified glaucoma surgeries performed at Stanford from 2013-2024, with two or more postoperative visits with intraocular pressure (IOP) measurement. Patient features were identified from the electronic health record (EHR), including demographics, prior diagnosis and procedure codes, medications and eye exam findings. Classical ML and DL models were developed to predict which glaucoma surgeries would result in surgical failure, defined as (1) IOP not reduced by more than 20% of preoperative baseline on two consecutive postoperative visits, (2) increased classes of glaucoma medications, and (3) need for additional glaucoma surgery or revision of original surgery. Results: A total of 2398 glaucoma surgeries of 1571 patients were included, of which 1677 surgeries met failure criteria. Random forest performed best for prediction of overall surgical failure, with accuracy of 75.5% and area under the receiver operator curve (AUROC) of 76.7%, similar to the deep learning model (accuracy 75.5%, AUROC 76.6%). Across all models, prediction performance was better for IOP outcomes (AUROC 86%) than need for an additional surgery (AUROC 76%) or need for additional glaucoma medication (AUC 70%). Conclusions: ML and DL algorithms can predict glaucoma surgery outcomes using structured data inputs from EHRs. Translational Relevance: Models that predict outcomes of glaucoma surgery may one day provide the basis for clinical decision support tools supporting surgeons in personalizing glaucoma treatment plans.

Bridging gaps in ophthalmology residency programs: the link between practice, training and confidence in ocular examination and gonioscopy for diagnosing glaucoma, a blinding disease.

BACKGROUND: To evaluate real-world utilization of gonioscopy for diagnosing glaucoma among ophthalmologists with diverse subspecialties, and understand current perceptions of teaching, training, and confidence in gonioscopy. METHODS: A nationwide anonymous online survey was conducted among practicing ophthalmologists, querying about demographics, professional experience, practice of routine ocular examination for glaucoma and perceptions of confidence in performing them. RESULTS: 136 ophthalmologists participated in the survey, with various levels of experience from residency to over twenty years of ophthalmology practice. Glaucoma specialists comprised 23 (16.9%) of the participants. Of the non-glaucoma-specialist respondents, only 33 (29.2%) expressed being highly confident in interpreting gonioscopic findings, which correlated significantly with their self-reported inadequate level of training in gonioscopy during residency (p < 0.001) and even more so with their low frequency of implementing gonioscopy in routine examinations (p < 0.001). The commonly cited reasons for the low practice of gonioscopy were insufficient time allotted to examinations and lack of experience, knowledge or equipment. CONCLUSIONS: Gonioscopy is fundamental to the detection of glaucoma. This study reveals underutilization of gonioscopy in the practice of ophthalmology and its association with lower training opportunities, calling for expedited changes in the residency's curriculum, alongside measures to promote its use in clinical practice.

Evaluation of lamina cribrosa curvature index in different types of glaucoma.

OBJECTIVE: The aim of this study was to evaluate the lamina cribrosa curvature index in different types of glaucoma in comparison with clinical findings and conventional measurement methods. MATERIAL AND METHOD: Patients older than 18 years who were followed up in Glaucoma Unit of Department of Ophthalmology at Firat University Faculty of Medicine, whose disease had been under control at least for 1 year, who had at least three reliable visual fields, whose refractive error was between - 6 and + 5 diopter and who did not have any disease other than glaucoma that would affect the visual field, were included in the study. Clinical and demographic characteristics, visual field, optical coherence tomography and lamina cribrosa curvature index (LCCI) results were evaluated. The study patients were divided into six groups: early-stage primary open-angle glaucoma (POAG) as group 1 and intermediate-advanced stage POAG as group 2, pseudo-exfoliation glaucoma (PEXG) as group 3, normal tension glaucoma (NTG) as group 4, ocular hypertension patients whom subsequently developed POAG as group 5 and healthy control as group 6. RESULTS: A total of 189 eyes of 101 patients were included in our study. Forty-seven patients were male (46.5%) and 54 were female (53.5%). The mean age was 62.43 ± 1.49 years. LCCI, mean deviation (MD), visual field index (VFI), pattern standard deviation (PSD) and retinal nerve fiber layer thickness (RNFL) values were analyzed in all groups and Pearson correlation analysis showed statistically significant correlation between PSD and RNFL measurements with LCCI values in all groups. MD value was correlated with LCCI in groups 2, 3 and 4, while VFI value was correlated with LCCI in all groups except group 5. When the groups were compared with each other according to the Post-Hoc Tamhane test, LCCI measurement showed statistically significant results in accordance with MD, VFI, PSD and RNFL values. CONCLUSION: The LCCI assessment is mostly consistent with conventional tests. In this study, in which different types of glaucoma and healthy subjects were examined simultaneously, LCCI shows promise as a detailed and reliable assessment method.

Exploring the Feasibility of Estimating Intraocular Pressure Using Vibrational Response of the Eye: A Methodological Approach.

This study addresses the limitations of current tonometry techniques by exploring vibroacoustic properties for estimating intraocular pressure (IOP), a key diagnostic parameter for monitoring glaucoma-a significant risk factor for vision loss. Utilizing vivo porcine eyeballs, we investigated the relationship between IOP and the nonlinear vibration transfer function ratio (NVTFR). Through applying varying vibration levels and analyzing responses with transfer function analysis and univariate regression, we identified a strong negative correlation between NVTFR and IOP, evidenced by a Pearson correlation coefficient of -0.8111 and significant results from generalized linear model (GLM) regression (p-value < 0.001). These findings indicate the potential of NVTFR as a vital indicator of IOP changes. Our study highlights the feasibility of using vibroacoustic properties, specifically NVTFR, to measure IOP. While further refinement is necessary for in vivo application, this approach opens new possibilities for non-invasive and patient-friendly IOP monitoring, potentially enhancing ophthalmology diagnostic techniques and providing a foundation for future research and development in this critical area.

A comparison of iCare and Goldmann applanation tonometry measurements during the COVID-19 pandemic: a retrospective study.

PURPOSE: To evaluate factors associated with differences in intraocular pressure (IOP) readings between iCare and Goldmann applanation tonometry (GAT) in established glaucoma patients. METHODS: This retrospective comparative study included clinical data of 350 eyes from 350 established glaucoma patients who had iCare and GAT IOP measured by an ophthalmic technician and a glaucoma specialist, respectively. The main outcome measure was the difference in IOP measurements of the right eyes with iCare and GAT. RESULTS: The intraclass correlation coefficient (ICC) between GAT and iCare was 0.90. The mean IOP difference between tonometers was - 0.18 ± 2.89 mmHg. Bland-Altman plots indicated a 95% limit of agreement of - 5.8 to 5.5 mmHg. Central corneal thickness (CCT) and age were significantly correlated with the difference in IOPs of the iCare and GAT. GAT-IOP and age were significantly associated with the absolute difference in measured IOP of the two tonometers. The difference in measurements was not significantly associated with prior glaucoma surgery, average global index of optical coherence tomography, axial length, technician years of experience and certification, and IOP range. CONCLUSION: Although there is good agreement between the iCare and GAT mean values, these devices are not interchangeable in glaucoma patients due to the wide range of the limit of agreement.