Retinal Nerve Fiber Layer Damage Assessment in Glaucomatous Eyes Using Retinal Retardance Measured by Polarization-Sensitive Optical Coherence Tomography.

Purpose: To assess the diagnostic performance and structure-function association of retinal retardance (RR), a customized metric measured by a prototype polarization-sensitive optical coherence tomography (PS-OCT), across various stages of glaucoma. Methods: This cross-sectional pilot study analyzed 170 eyes from 49 healthy individuals and 68 patients with glaucoma. The patients underwent PS-OCT imaging and conventional spectral-domain optical coherence tomography (SD-OCT), as well as visual field (VF) tests. Parameters including RR and retinal nerve fiber layer thickness (RNFLT) were extracted from identical circumpapillary regions of the fundus. Glaucomatous eyes were categorized into early, moderate, or severe stages based on VF mean deviation (MD). The diagnostic performance of RR and RNFLT in discriminating glaucoma from controls was assessed using receiver operating characteristic (ROC) curves. Correlations among VF-MD, RR, and RNFLT were evaluated and compared within different groups of disease severity. Results: The diagnostic performance of both RR and RNFLT was comparable for glaucoma detection (RR AUC = 0.98, RNFLT AUC = 0.97; P = 0.553). RR showed better structure-function association with VF-MD than RNFLT (RR VF-MD = 0.68, RNFLT VF-MD = 0.58; z = 1.99; P = 0.047) in glaucoma cases, especially in severe glaucoma, where the correlation between VF-MD and RR (r = 0.73) was significantly stronger than with RNFLT (r = 0.43, z = 1.96, P = 0.050). In eyes with early and moderate glaucoma, the structure-function association was similar when using RNFLT and RR. Conclusions: RR and RNFLT have similar performance in glaucoma diagnosis. However, in patients with glaucoma especially severe glaucoma, RR showed a stronger correlation with VF test results. Further research is needed to validate RR as an indicator for severe glaucoma evaluation and to explore the benefits of using PS-OCT in clinical practice. Translational Relevance: We demonstrated that PS-OCT has the potential to evaluate the status of RNFL structural damage in eyes with severe glaucoma, which is currently challenging in clinics.

A hybrid framework for glaucoma detection through federated machine learning and deep learning models.

BACKGROUND: Glaucoma, the second leading cause of global blindness, demands timely detection due to its asymptomatic progression. This paper introduces an advanced computerized system, integrates Machine Learning (ML), convolutional neural networks (CNNs), and image processing for accurate glaucoma detection using medical imaging data, surpassing prior research efforts. METHOD: Developing a hybrid glaucoma detection framework using CNNs (ResNet50, VGG-16) and Random Forest. Models analyze pre-processed retinal images independently, and post-processing rules combine predictions for an overall glaucoma impact assessment. RESULT: The hybrid framework achieves a significant 95.41% accuracy, with precision and recall at 99.37% and 88.37%, respectively. The F1 score, balancing precision and recall, reaches a commendable 93.52%. These results highlight the robustness and effectiveness of the hybrid framework in accurate glaucoma diagnosis. CONCLUSION: In summary, our research presents an innovative hybrid framework combining CNNs and traditional ML models for glaucoma detection. Using ResNet50, VGG-16, and Random Forest in an ensemble approach yields remarkable accuracy, precision, recall, and F1 score. These results showcase the methodology's potential to enhance glaucoma diagnosis, emphasizing its promising role in early detection and preventing irreversible vision loss. The integration of ML and DNNs in medical imaging analysis suggests a valuable path for future advancements in ophthalmic healthcare.

Glaucoma detection using non-perfused areas in OCTA.

Multiple ophthalmic diseases lead to decreased capillary perfusion that can be visualized using optical coherence tomography angiography images. To quantify the decrease in perfusion, past studies have often used the vessel density, which is the percentage of vessel pixels in the image. However, this method is often not sensitive enough to detect subtle changes in early pathology. More recent methods are based on quantifying non-perfused or intercapillary areas between the vessels. These methods rely upon the accuracy of vessel segmentation, which is a challenging task and therefore a limiting factor for reliability. Intercapillary areas computed from perfusion-distance measures are less sensitive to errors in the vessel segmentation since the distance to the next vessel is only slightly changing if gaps are present in the segmentation. We present a novel method for distinguishing between glaucoma patients and healthy controls based on features computed from the probability density function of these perfusion-distance areas. The proposed approach is evaluated on different capillary plexuses and outperforms previously proposed methods that use handcrafted features for classification. Moreover the results of the proposed method are in the same range as the ones of convolutional neural networks trained on the raw input images and is therefore a computationally efficient, simple to implement and explainable alternative to deep learning-based approaches.

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.

Chromatic vision and structural assessment in primary congenital glaucoma.

Primary congenital glaucoma is a rare disease that occurs in early birth and can lead to low vision. Evaluating affected children is challenging and there is a lack of studies regarding color vision in pediatric glaucoma patients. This cross-sectional study included 21 eyes of 13 children with primary congenital glaucoma who were assessed using the Farnsworth D-15 test to evaluate color vision discrimination and by spectral domain optical coherence tomography to measure retinal fiber layer thickness. Age, visual acuity, cup-to-disc ratio and spherical equivalent data were also collected. Global and sectional circumpapillary and macular retinal fiber layer thicknesses were measured and compared based on color vision test performance. Four eyes (19%) failed the color vision test with diffuse dyschromatopsia patterns. Only age showed statistical significance in color vision test performance. Global and sectional circumpapillary and macular retinal fiber layer thicknesses were similar between the color test outcomes dyschromatopsia and normal. While the color vision test could play a role in assessing children with primary congenital glaucoma, further studies are needed to correlate it with damage to retinal fiber layer thickness.

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.

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.

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.

Bleb characteristics assessed by ultrasound biomicroscopy correlate with intraocular pressure after Ahmed glaucoma valve surgery in dogs.

OBJECTIVE: To assess the characteristics of blebs formed after Ahmed glaucoma valve (AGV) surgery in dogs using ultrasound biomicroscopy (UBM) and to analyze their correlation with postoperative intraocular pressure (IOP). ANIMALS: 16 eyes (13 dogs) were diagnosed with primary angle-closure glaucoma and were followed up after AGV surgery from June 2021 to September 2023. METHODS: In this prospective study, UBM examinations were performed to assess bleb characteristics, including bleb wall thickness and reflectivity. IOP at the time of UBM imaging and the duration from AGV surgery to UBM imaging were recorded. Histological examination of an enucleated eye removed due to uncontrolled IOP leading to blindness was also conducted. RESULTS: A significant correlation was observed between IOP and relative reflectivity (Pearson r = 0.60; P = .01), and a negative correlation was observed between bleb wall thickness and relative reflectivity (Pearson r = -0.72; P = .002). No significant correlation was observed between the duration from AGV surgery to UBM imaging and either bleb wall thickness or relative reflectivity, respectively. Histological examination of the enucleated eye revealed collagen-rich fibrous encapsulation of the bleb wall, including myofibroblasts that exhibited positive α-smooth muscle actin immunostaining. CLINICAL RELEVANCE: In dogs that underwent AGV surgery, less dense, thick-walled blebs on UBM tended to maintain IOP within the normal range. However, denser, thinner-walled blebs showed IOP levels above the normal range despite the use of antiglaucoma medications. UBM is a useful tool for evaluating bleb characteristics and their influence on IOP regulation after AGV surgery in dogs.

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).

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.

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.

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.

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.