Remote and low-cost intraocular pressure monitoring by deep learning of speckle patterns.

Significance: Glaucoma, a leading cause of global blindness, disproportionately affects low-income regions due to expensive diagnostic methods. Affordable intraocular pressure (IOP) measurement is crucial for early detection, especially in low- and middle-income countries. Aim: We developed a remote photonic IOP biomonitoring method by deep learning of the speckle patterns reflected from an eye sclera stimulated by a sound source. We aimed to achieve precise IOP measurements. Approach: IOP was artificially raised in 24 pig eyeballs, considered similar to human eyes, to apply our biomonitoring method. By deep learning of the speckle pattern videos, we analyzed the data for accurate IOP determination. Results: Our method demonstrated the possibility of high-precision IOP measurements. Deep learning effectively analyzed the speckle patterns, enabling accurate IOP determination, with the potential for global use. Conclusions: The novel, affordable, and accurate remote photonic IOP biomonitoring method for glaucoma diagnosis, tested on pig eyes, shows promising results. Leveraging deep learning and speckle pattern analysis, together with the development of a prototype for human eyes testing, could enhance diagnosis and management, particularly in resource-constrained settings worldwide.

RNFLT2Vec: Artifact-corrected representation learning for retinal nerve fiber layer thickness maps.

Optical coherence tomography imaging provides a crucial clinical measurement for diagnosing and monitoring glaucoma through the two-dimensional retinal nerve fiber layer (RNFL) thickness (RNFLT) map. Researchers have been increasingly using neural models to extract meaningful features from the RNFLT map, aiming to identify biomarkers for glaucoma and its progression. However, accurately representing the RNFLT map features relevant to glaucoma is challenging due to significant variations in retinal anatomy among individuals, which confound the pathological thinning of the RNFL. Moreover, the presence of artifacts in the RNFLT map, caused by segmentation errors in the context of degraded image quality and defective imaging procedures, further complicates the task. In this paper, we propose a general framework called RNFLT2Vec for unsupervised learning of vectorized feature representations from RNFLT maps. Our method includes an artifact correction component that learns to rectify RNFLT values at artifact locations, producing a representation reflecting the RNFLT map without artifacts. Additionally, we incorporate two regularization techniques to encourage discriminative representation learning. Firstly, we introduce a contrastive learning-based regularization to capture the similarities and dissimilarities between RNFLT maps. Secondly, we employ a consistency learning-based regularization to align pairwise distances of RNFLT maps with their corresponding thickness distributions. Through extensive experiments on a large-scale real-world dataset, we demonstrate the superiority of RNFLT2Vec in three different clinical tasks: RNFLT pattern discovery, glaucoma detection, and visual field prediction. Our results validate the effectiveness of our framework and its potential to contribute to a better understanding and diagnosis of glaucoma.

Peripapillary Atrophy Area as an Indicator of Glaucomatous Structural and Functional Progression.

Purpose: To determine whether peripapillary atrophy (PPA) area is an indicator of glaucomatous structural and functional damage and progression. Methods: In this retrospective longitudinal analysis from ongoing prospective study we qualified 71 eyes (50 subjects) with glaucoma. All subjects had a comprehensive ophthalmic examination, visual field (VF), and spectral-domain optical coherence tomography (OCT) testing in at least three visits. PPA was manually delineated on en face OCT optic nerve head scans, while observing the corresponding cross-sectional images, as the hyper-reflective area contiguous with the optic disc. Results: The mean follow-up duration was 4.4 ± 1.4 years with an average of 6.8 ± 2.2 visits. At baseline, PPA area was significantly associated only with VF's mean deviation (MD; P = 0.041), visual field index (VFI; P = 0.041), superior ganglion cell inner plexiform layer (GCIPL; P = 0.011), and disc area (P = 0.011). Longitudinally, PPA area was negatively and significantly associated with MD (P = 0.015), VFI (P = 0.035), GCIPL (P = 0.009), superior GCIPL (P = 0.034), and disc area (P = 0.007, positive association). Conclusions: Longitudinal change in PPA area is an indicator of glaucomatous structural and functional progression but PPA area at baseline cannot predict future progression. Translational Relevance: Longitudinal changes in peripapillary atrophy area measured by OCT can be an indicator of structural and functional glaucoma progression.

Hemifield-based analysis of pattern electroretinography in normal subjects and patients with preperimetric glaucoma.

This prospective cross-sectional study investigated the visual function of preperimetric glaucoma (PPG) patients based on hemifield (HF) pattern electroretinogram (PERG) amplitudes. Thirty-two (32) normal subjects and 33 PPG patients were enrolled in control and PPG groups, respectively. All of the participants had undergone full ophthalmic examinations, including spectral-domain optical coherence tomography (SD-OCT), visual field (VF) examination and pattern electroretinography (PERG). The PERG parameters along with the HF ratios of SD-OCT and PERG were compared between the control and PPG groups. Pairwise Pearson's correlation coefficients and linear regression models were fitted to investigate the correlations. The PERG N95 amplitudes were significantly lower in the PPG group (P < 0.001). The smaller/larger HF N95 amplitude ratio of the PPG group was found to be smaller than that of the control group (0.73 ± 0.20 vs. 0.86 ± 0.12; P = 0.003) and showed positive correlations with affected HF average ganglion cell-inner plexiform layer (GCIPL) thickness (r = 0.377, P = 0.034) and with average GCIPL thickness (r = 0.341, P = 0.005). The smaller/larger HF N95 amplitude ratio did not significantly change with age (ß = - 0.005, P = 0.195), whereas the full-field N95 amplitude showed a negative correlation with age (ß = - 0.081, P < 0.001). HF analysis of PERG N95 amplitudes might be particularly useful for patients with early glaucoma.

Deep Learning-based Glaucoma Detection Using CNN and Digital Fundus Images: A Promising Approach for Precise Diagnosis.

BACKGROUND: Glaucoma is a significant cause of irreversible blindness worldwide, with symptoms often going undetected until the patient's visual field starts shrinking. OBJECTIVE: To develop an AI-based glaucoma detection method to reduce glaucoma-related blindness and offer more precise diagnosis. METHODS: Discusses various methods and technologies, including Heidelberg Retinal Tomography (HRT), Optical Coherence Tomography (OCT), and Fundus Photography, for obtaining relevant information about the presence of glaucoma in a patient. Additionally, it mentions the use of Support Vector Machines (SVMs) and Convolutional Neural Networks (CNNs) for glaucoma detection. There are many limitations for existing methods as; Asymptomatic Progression, reliance on subjective feedback, multiple tests required, late detection, limited availability of preventive tests, influence of external factors. RESULTS: Findings reveal promising outcomes in terms of glaucoma detection accuracy, particularly in the analysis of the RIM-ONE-r3 dataset. By scrutinizing 20 images from the Healthy, Glaucoma, and Suspects categories through fundus image recognition, our developed AI model consistently achieved high diagnostic accuracy rates. Conclusion Our study suggests that further enhancements in glaucoma detection accuracy are attainable by augmenting the dataset with additional labeled images. We emphasize the significance of considering various application parameters when discussing the integration of computer-aided decision/management systems into healthcare frameworks.

Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images.

The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the Gravitational search optimization algorithm (GSOA) technique for metaheuristic-based FS. Glaucoma disease is selected as the subject of investigation as this disease is spreading worldwide at a very fast pace; 111 million instances of glaucoma are expected by 2040, up from 64 million in 2015. It causes widespread vision impairment. Optic nerve fibres can be degraded and cannot be replaced later in this disease. As a starting point, the retinal fundus images of glaucoma infected persons and healthy persons are used, and 36 features were retrieved from these images of public benchmark datasets and private dataset. Six ML models are trained for classification on the basis of the GSOA's returned subset of features. The suggested FS technique enhances classification performance with selection of most influential features. The eight statistical performance evaluating parameters along with execution time are calculated. The training and testing have been performed using a split approach (70:30), 5-fold cross validation (CV), as well as 10-fold CV. The suggested approach achieved 95.36 % accuracy. Due to its auspicious performance, doctors might use the suggested method to receive a second opinion, which would also help overburdened skilled medical practitioners and save patients from vision loss.

Automated vertical cup-to-disc ratio determination from fundus images for glaucoma detection.

Glaucoma is the leading cause of irreversible blindness worldwide. Often asymptomatic for years, this disease can progress significantly before patients become aware of the loss of visual function. Critical examination of the optic nerve through ophthalmoscopy or using fundus images is a crucial component of glaucoma detection before the onset of vision loss. The vertical cup-to-disc ratio (VCDR) is a key structural indicator for glaucoma, as thinning of the superior and inferior neuroretinal rim is a hallmark of the disease. However, manual assessment of fundus images is both time-consuming and subject to variability based on clinician expertise and interpretation. In this study, we develop a robust and accurate automated system employing deep learning (DL) techniques, specifically the YOLOv7 architecture, for the detection of optic disc and optic cup in fundus images and the subsequent calculation of VCDR. We also address the often-overlooked issue of adapting a DL model, initially trained on a specific population (e.g., European), for VCDR estimation in a different population. Our model was initially trained on ten publicly available datasets and subsequently fine-tuned on the REFUGE dataset, which comprises images collected from Chinese patients. The DL-derived VCDR displayed exceptional accuracy, achieving a Pearson correlation coefficient of 0.91 (P = 4.12 × 10-412) and a mean absolute error (MAE) of 0.0347 when compared to assessments by human experts. Our models also surpassed existing approaches on the REFUGE dataset, demonstrating higher Dice similarity coefficients and lower MAEs. Moreover, we developed an optimization approach capable of calibrating DL results for new populations. Our novel approaches for detecting optic discs and optic cups and calculating VCDR, offers clinicians a promising tool that significantly reduces manual workload in image assessment while improving both speed and accuracy. Most importantly, this automated method effectively differentiates between glaucoma and non-glaucoma cases, making it a valuable asset for glaucoma detection.

Individual vs simultaneous macular and optic disc measurements with spectral domain optical coherence tomography in glaucoma and healthy eyes.

We assessed the repeatability and agreement of ganglion cell complex (GCC) in the macular area and the peripapillary retinal nerve fiber layer (ppRNFL) with individual and combined macula and disc scans. The macular GCC and ppRNFL thicknesses from 34 control eyes and 43 eyes with glaucoma were measured with the Canon Optical Coherence Tomography (OCT) HS-100. Two repeated measurements were performed with both scan modes. The repeatability limit (Rlim) and agreement analysis were performed. The individual scan showed better repeatability than the combined scan in both groups. However, the differences in the Rlim for the GCC in most sectors were lower than 3 µm (axial resolution of the OCT), and this was larger than 3 µm for most of the ppRNFL sectors. The mean differences in the thickness between both scan modes for the GCC and ppRNFL measurements were less than 3 and 6 µm, respectively. The interval of the limits of agreement was about 10 µm in some sectors for the GCC, and about 40 and 60 µm in some sectors in controls and glaucoma eyes, respectively. Both scan modes showed good repeatability in both groups. The agreement results suggest that the scan modes cannot be used interchangeably.

Leuven-Haifa High-Resolution Fundus Image Dataset for Retinal Blood Vessel Segmentation and Glaucoma Diagnosis.

The Leuven-Haifa dataset contains 240 disc-centered fundus images of 224 unique patients (75 patients with normal tension glaucoma, 63 patients with high tension glaucoma, 30 patients with other eye diseases and 56 healthy controls) from the University Hospitals of Leuven. The arterioles and venules of these images were both annotated by master students in medicine and corrected by a senior annotator. All senior segmentation corrections are provided as well as the junior segmentations of the test set. An open-source toolbox for the parametrization of segmentations was developed. Diagnosis, age, sex, vascular parameters as well as a quality score are provided as metadata. Potential reuse is envisioned as the development or external validation of blood vessels segmentation algorithms or study of the vasculature in glaucoma and the development of glaucoma diagnosis algorithms. The dataset is available on the KU Leuven Research Data Repository (RDR).

Are Macula or Optic Nerve Head Structures Better at Diagnosing Glaucoma? An Answer Using Artificial Intelligence and Wide-Field Optical Coherence Tomography.

Purpose: We wanted to develop a deep-learning algorithm to automatically segment optic nerve head (ONH) and macula structures in three-dimensional (3D) wide-field optical coherence tomography (OCT) scans and to assess whether 3D ONH or macula structures (or a combination of both) provide the best diagnostic power for glaucoma. Methods: A cross-sectional comparative study was performed using 319 OCT scans of glaucoma eyes and 298 scans of nonglaucoma eyes. Scans were compensated to improve deep-tissue visibility. We developed a deep-learning algorithm to automatically label major tissue structures, trained with 270 manually annotated B-scans. The performance was assessed using the Dice coefficient (DC). A glaucoma classification algorithm (3D-CNN) was then designed using 500 OCT volumes and corresponding automatically segmented labels. This algorithm was trained and tested on three datasets: cropped scans of macular tissues, those of ONH tissues, and wide-field scans. The classification performance for each dataset was reported using the area under the curve (AUC). Results: Our segmentation algorithm achieved a DC of 0.94 ± 0.003. The classification algorithm was best able to diagnose glaucoma using wide-field scans, followed by ONH scans, and finally macula scans, with AUCs of 0.99 ± 0.01, 0.93 ± 0.06 and 0.91 ± 0.11, respectively. Conclusions: This study showed that wide-field OCT may allow for significantly improved glaucoma diagnosis over typical OCTs of the ONH or macula. Translational Relevance: This could lead to mainstream clinical adoption of 3D wide-field OCT scan technology.

Diagnostic performance of wide-field optical coherence tomography angiography for high myopic glaucoma.

Diagnosing and monitoring glaucoma in high myopic (HM) eyes are becoming very important; however, it is challenging to diagnose this condition. This study aimed to evaluate the diagnostic ability of wide-field optical coherence tomography angiography (WF-OCTA) maps for the detection of glaucomatous damage in eyes with HM and to compare the diagnostic ability of WF-OCTA maps with that of conventional imaging approaches, including swept-source optical coherence tomography (SS-OCT) wide-field maps. In this retrospective observational study, a total 62 HM-healthy eyes and 140 HM eyes with open-angle glaucoma were included. Patients underwent a comprehensive ocular examination, including SS-OCT wide-field and 12 × 12 WF-OCTA scans. The WF-OCTA map represents the peripapillary and macular superficial vascular density maps. Glaucoma specialists determined the presence of glaucomatous damage in HM eyes by reading the WF-OCTA map and comparing its sensitivity and specificity with those of conventional SS-OCT images. The sensitivity and specificity of 12 × 12 WF-OCTA scans for HM-glaucoma diagnosis were 87.28% and 86.94%, respectively, while, the sensitivity and specificity of SS-OCT wide-field maps for HM-glaucoma diagnosis were 87.49% and 80.51%, respectively. The specificity of the WF-OCTA map was significantly higher than that of the SS-OCT wide-field map (p < 0.05). The sensitivity of the WF-OCTA map was comparable with that of the SS-OCT wide-field map (p = 0.078). The WF-OCTA map showed good diagnostic ability for discriminating HM-glaucomatous eyes from HM-healthy eyes. As a complementary method to an alternative imaging modality, WF-OCTA mapping can be a useful tool for the detection of HM glaucoma.

Assessment of linear regression of peripapillary optical coherence tomography retinal nerve fiber layer measurements to forecast glacuoma trajectory.

Linear regression of optical coherence tomography measurements of peripapillary retinal nerve fiber layer thickness is often used to detect glaucoma progression and forecast future disease course. However, current measurement frequencies suggest that clinicians often apply linear regression to a relatively small number of measurements (e.g., less than a handful). In this study, we estimate the accuracy of linear regression in predicting the next reliable measurement of average retinal nerve fiber layer thickness using Zeiss Cirrus optical coherence tomography measurements of average retinal nerve fiber layer thickness from a sample of 6,471 eyes with glaucoma or glaucoma-suspect status. Linear regression is compared to two null models: no glaucoma worsening, and worsening due to aging. Linear regression on the first M ≥ 2 measurements was significantly worse at predicting a reliable M+1st measurement for 2 ≤ M ≤ 6. This range was reduced to 2 ≤ M ≤ 5 when retinal nerve fiber layer thickness measurements were first "corrected" for scan quality. Simulations based on measurement frequencies in our sample-on average 393 ± 190 days between consecutive measurements-show that linear regression outperforms both null models when M ≥ 5 and the goal is to forecast moderate (75th percentile) worsening, and when M ≥ 3 for rapid (90th percentile) worsening. If linear regression is used to assess disease trajectory with a small number of measurements over short time periods (e.g., 1-2 years), as is often the case in clinical practice, the number of optical coherence tomography examinations needs to be increased.

AIROGS: Artificial Intelligence for Robust Glaucoma Screening Challenge.

The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening.

Quantitative Optical Coherence Elastography of the Optic Nerve Head In Vivo.

OBJECTIVE: Optical coherence elastography (OCE) was used to demonstrate the relationship between the elasticity of the optic nerve head (ONH) and different intraocular pressure (IOP) levels in an in-vivo rabbit model for the first time. METHOD: Both ex-vivo and in-vivo rabbit ONH were imaged using OCE system. A mechanical shaker initiated the propagation of elastic waves, and the elasticity of the ONH was determined by tracking the wave propagation speed. The elasticity of the ONH under varying IOP levels was reconstructed based on the wave speed. Notably, the ONH exhibited increased stiffness with elevated IOP. RESULTS: In the in-vivo rabbit models, the Young's modulus of ONH increased from 14 kPa to 81 kPa with the IOP increased from 15 mmHg to 35 mmHg. This revealed a positive correlation between the Young's modulus of the ONH and intraocular pressure. CONCLUSION: The OCE system proved effective in measuring the mechanical properties of ONH at different IOP levels, with validation in an in-vivo rabbit model. SIGNIFICANCE: Considering ONH plays a critical role in vision and eye diseases, the capability to image and quantify in vivo ONH biomechanical properties has great potential to advance vision science research and improve the clinical management of glaucoma patients.

3-LbNets: Tri-Labeling Deep Convolutional Neural Network for the Automated Screening of Glaucoma, Glaucoma Suspect, and No Glaucoma in Fundus Images.

Early detection of glaucoma, a widespread visual disease, can prevent vision loss. Unfortunately, ophthalmologists are scarce and clinical diagnosis requires much time and cost. Therefore, we developed a screening Tri-Labeling deep convolutional neural network (3-LbNets) to identify no glaucoma, glaucoma suspect, and glaucoma cases in global fundus images. 3-LbNets extracts important features from 3 different labeling modals and puts them into an artificial neural network (ANN) to find the final result. The method was effective, with an AUC of 98.66% for no glaucoma, 97.54% for glaucoma suspect, and 97.19% for glaucoma when analysing 206 fundus images evaluated with unanimous agreement from 3 well-trained ophthalmologists (3/3). When analysing 178 difficult to interpret fundus images (with majority agreement (2/3)), this method had an AUC of 80.80% for no glaucoma, 69.52% for glaucoma suspect, and 82.74% for glaucoma cases.Clinical relevance-This establishes a robust global fundus image screening network based on the ensemble method that can optimize glaucoma screening to alleviate the toll on those with glaucoma and prevent glaucoma suspects from developing the disease.

Artificial intelligence in glaucoma: opportunities, challenges, and future directions.

Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

Capacidad diagnóstica de la densidad vascular papilar y macular frente a parámetros estructurales con tomografía de coherencia óptica en el glaucoma seudoexfoliativo / Diagnostic ability of vessel density of the optic nerve head and macula versus structural parameters with optical coherence tomography in pseudoexfoliative glaucoma

Propósito Evaluar la capacidad diagnóstica de la densidad de vasos (DV) papilar y macular mediante angiografía por tomografía de coherencia óptica (OCTA) y el grosor de la capa de fibras nerviosas de la retina (CFNR) y complejo de células ganglionares (CCG) maculares mediante tomografía de coherencia óptica (OCT) en los pacientes con glaucoma seudoexfoliativo (GPX). Métodos Estudio transversal que incluyó GPX y controles sanos. Se realizó OCT y OCTA de la papila y el área macular con el OCT RS-3000 Advance (Nidek Co., Gamagori, Japón). Se registró la DV macular del plexo capilar superficial (SCP) y la DV papilar del plexo capilar peripapilar radial (RPCP). Se empleó el área bajo la curva característica operativa del receptor (AUROC) para determinar el poder discriminatorio de cada parámetro. Resultados El grosor de la CFNR y del CCG, así como la DV a nivel papilar y macular, fueron significativamente menores en los pacientes con GPX que en los controles sanos (todos, p<0,05). El mejor parámetro discriminante fue el grosor medio de la CFNR (AUROC: 0,928). El AUROC de la DV papilar fue mejor que el de la DV macular (AUROC: 0,897 y 0,780, respectivamente). AUROC de la DV papilar fue comparable a la del grosor de la CFNR (p<0,001).Conclusiones La capacidad diagnóstica de la DV papilar en el GPS parece comparable a la de los parámetros estructurales, espesor de la CFNR y CCG, obtenidos mediante OCT, por lo que la OCTA podría ser una herramienta valiosa en el GPX. (AU)

PurposeTo evaluate the diagnostic ability of the vessel density (VD) of the optic nerve head (ONH) and the macula on optical coherence tomography (OCT) angiography and the retinal nerve layer thickness (RNFL) thickness and the macular ganglion cell complex (GCC) thickness on OCT in patients with pseudoexfoliative glaucoma (PXG). Methods Cross-sectional study including PXG patients and healthy controls. Demographic and clinical data were noted for all participants. Optical coherence tomography (OCT) and OCT angiography (OCTA) images of the ONH and macular area were obtained with the RS-3000 Advance OCT (Nidek Co., Gamagori, Japan). The RNFL and GCC thickness of different sectors was provided by the software. Macular VD of the superficial capillary plexus (SCP) and ONH VD of the radial peripapillary capillary plexus (RPCP) were registered. Groups were compared and area under the receiver operating characteristic (AUROC) curves were used to determine the power of discrimination of each parameter. Results RNFL and GCC thickness and ONH and macular VD were significantly lower in PXG patients compared with healthy controls (all, P<.05). The best discrimination parameter was the average RNFL thickness (AUROC: 0.928). ONH VD AUROC was better than that of macular VD (AUROC: 0.897 and 0.780, respectively). ONH VD AUROC was comparable to RNFL thickness (P<.001).Conclusions The diagnostic ability of ONH vessel density in PXG appears comparable to that of the structural parameters, RNFL and GCC thickness, obtained with OCT, and may be a valuable tool in clinical practice. (AU)

Topographic comparison of the retinal microvascular changes between patients with compressive and glaucomatous optic neuropathies.

We investigated the difference in optical coherence tomography angiography characteristics between the patients with compressive optic neuropathy (CON, n = 26) and glaucomatous optic neuropathy (GON, n = 26), who were matched for the severity of visual field defect. The peripapillary retinal nerve fiber layer (pRNFL) thickness in the nasal and temporal sectors was thinner in the CON group, whereas the inferior pRNFL thickness was thinner in the GON group. Accordingly, the CON group had lower peripapillary vessel density (pVD) in the nasal and temporal sectors, and the GON group in the inferior sector. In the macular area, the CON group had a thinner macular ganglion cell-inner plexiform layer in the superior and nasal sectors, whereas the GON group in the inferior sector. However, the CON group did not have a lower macular VD than the GON group in any sector, whereas the GON group exhibited lower superficial capillary plexus VD in the superior, inferior, and temporal sectors. Comparison of the structure-vasculature correlation revealed a significant difference in the nasal and temporal peripapillary areas and superior and nasal macular sectors; a decrease in VD was greater in the GON group than in the CON group when the comparable structural change occurred.

A comparative study of ultrasound cycloplasty and endoscopic cyclophotocoagulation in the treatment of secondary glaucoma.

To compare the clinical efficacy of ultrasound cycloplasty (UCP) and endoscopic cyclophotocoagulation (ECP) in the treatment of secondary glaucoma. In a 12-month prospective single-center study, 22 patients with secondary glaucoma were treated by high-intensity focused ultrasound (HIFU), and 23 patients with secondary glaucoma were treated by a semiconductor laser. At the final follow-up, the two groups' surgical outcomes were compared. A complete success was defined as an intraocular pressure (IOP) reduction of at least 20% from baseline and an IOP of > 5 mmHg and ≦ 21 mmHg, while a qualified success was defined as an IOP reduction of at least 20% from baseline and an IOP of > 5 mmHg. The secondary outcome was the average IOP, number of drugs, and complications at each follow-up compared with the baseline. The average preoperative IOPs in the UCP and ECP groups were 36.4 ± 9.5 mmHg (n = 2.3 drops, n = 0.2 tablets) and 34.5 ± 11.7 mmHg (n = 2.0 drops, n = 0.3 tablets), respectively. In the last follow-up, the success rate of UCP was 54% (with a decrease of 32%) and that of ECP was 65% (with a decrease of 35%), and the P-value between the two groups was > 0.05. However, there was a difference in the average IOP between these two groups 1 day and 1 week after the operation, and the IOP reduction efficiency in the ECP group was better. However, the amount of drug used after these two surgeries was significantly reduced. There were fewer postoperative complications in the UCP group (18 cases) than in the ECP group (35 cases). Both UCP and ECP can effectively reduce IOP in secondary glaucoma, and ECP has a better effect at the early stages. However, UCP has higher safety and tolerance for patients.