Detection Of Glaucoma Research Articles

Glaucoma Detection Using Explainable AI and Deep Learning

INTRODUCTION: Glaucoma is an incurable eye syndrome and the second leading reason of vision loss. A retinal scan is usually used to detect it. Glaucoma poses a challenge to predict in its nascent stages because the side effects of glaucoma are not recognized until the advanced stages of the disease are reached. Therefore, regular eye examinations are important and recommended. Manual glaucoma screening methods are labour-intensive and time-consuming processes. However, deep learning-based glaucoma detection methods reduce the need for manual work and improve accuracy and speed.
 OBJECTIVES: conduct a literature analysis of latest technical publications using various AI, Machine learning, and Deep learning methodologies for automated glaucoma detection.
 RESULTS: There are 329 Scopus articles on glaucoma detection using retinal images. The quantitative review presented state-of-art methods from different research publications and articles and the usage of a fundus image database for qualitative and quantitative analysis. This paper presents the execution of Explainable AI for Glaucoma prediction Analysis. Explainable AI (XAI) is artificial intelligence (AI) that allows humans to understand AI decisions and predictions. This contrasts with the machine learning “black box” concept, where even the designer cannot explain why the AI made certain decisions. XAI is committed to improving user performance. To provide reliable explanations for Glaucoma forecasting from unhealthy and diseased photos, XAI primarily employs an Adaptive Neuro-fuzzy Inference System (ANFIS).
 CONCLUSION: This article proposes and compares the performance metrics of ANFIS & SNN fuzzy layers, VGG19, AlexNet, ResNet, and MobileNet.

Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB - retrained AlexNet convolutional neural network.

Glaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus imaging serves as the principal method for diagnosing glaucoma and DR. Consequently, automated detection of eye diseases represents a significant application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) exhibits potential for effective eye disease detection. This paper proposes the use of MATLAB - retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique was employed to retrain the AlexNet CNN for non-disease (Non_D), glaucoma (Sus_G) and diabetic retinopathy (Sus_R) classification. Moreover, model benchmarking was conducted using ResNet50 and GoogLeNet architectures. A Grad-CAM analysis is also incorporated for each eye condition examined. Metrics for validation accuracy, false positives, false negatives, precision, and recall were reported. Validation accuracies for the NetTransfer (I-V) and netAlexNet ranged from 89.7% to 94.3%, demonstrating varied effectiveness in identifying Non_D, Sus_G, and Sus_R categories, with netAlexNet achieving a 93.2% accuracy in the benchmarking of models against netResNet50 at 93.8% and netGoogLeNet at 90.4%. This study demonstrates the efficacy of using a MATLAB-retrained AlexNet CNN for detecting glaucoma and diabetic retinopathy. It emphasizes the need for automated early detection tools, proposing CNNs as accessible solutions without replacing existing technologies.

Trish: an efficient activation function for CNN models and analysis of its effectiveness with optimizers in diagnosing glaucoma

Glaucoma is an eye disease that spreads over time without showing any symptoms at an early age and can result in vision loss in advanced ages. The most critical issue in this disease is to detect the symptoms of the disease at an early age. Various researches are carried out on machine learning approaches that will provide support to the expert for this diagnosis. The activation function plays a pivotal role in deep learning models, as it introduces nonlinearity, enabling neural networks to learn complex patterns and relationships within data, thus facilitating accurate predictions and effective feature representations. In this study, it is focused on developing an activation function that can be used in CNN architectures using glaucoma disease datasets. The developed function (Trish) was compared with ReLU, LReLU, Mish, Swish, Smish, and Logish activation functions using SGD, Adam, RmsProp, AdaDelta, AdaGrad, Adamax, and Nadam optimizers in CNN architectures. Datasets consisting of retinal fundus images named ACRIMA and HRF were used within the scope of the experiments. These datasets are widely known and currently used in the literature. To strengthen the test validity, the proposed function was also tested on the CIFAR-10 dataset. As a result of the study, 97.22% validation accuracy performance was obtained. It should be stated that the acquired performance value is at a significant level for the detection of glaucoma.

Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism.

Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural network model for optic disc and cup disc segmentation. The U-Net backbone is used to infer the basic position information of optic disc and cup disc, the attention module enhances the model's ability to represent and extract features of optic disc and cup disc, and the residual module alleviates gradient disappearance or explosion that may occur during feature representation of the neural network. The proposed model is trained and tested on the DRISHTI-GS1 dataset. Results show that compared with the original U-Net method, our model can more effectively separate optic disc and cup disc in terms of overlap error, sensitivity, and specificity.

Diagnosis of Glaucoma Based on Few-Shot Learning with Wide-Field Optical Coherence Tomography Angiography.

This study evaluated the utility of incorporating deep learning into the relatively novel imaging technique of wide-field optical coherence tomography angiography (WF-OCTA) for glaucoma diagnosis. To overcome the challenge of limited data associated with this emerging imaging, the application of few-shot learning (FSL) was explored, and the advantages observed during its implementation were examined. A total of 195 eyes, comprising 82 normal controls and 113 patients with glaucoma, were examined in this study. The system was trained using FSL instead of traditional supervised learning. Model training can be presented in two distinct ways. Glaucoma feature detection was performed using ResNet18 as a feature extractor. To implement FSL, the ProtoNet algorithm was utilized to perform task-independent classification. Using this trained model, the performance of WF-OCTA through the FSL technique was evaluated. We trained the WF-OCTA validation method with 10 normal and 10 glaucoma images and subsequently examined the glaucoma detection effectiveness. FSL using the WF-OCTA image achieved an area under the receiver operating characteristic curve (AUC) of 0.93 (95% confidence interval (CI): 0.912-0.954) and an accuracy of 81%. In contrast, supervised learning using WF-OCTA images produced worse results than FSL, with an AUC of 0.80 (95% CI: 0.778-0.823) and an accuracy of 50% (p-values < 0.05). Furthermore, the FSL method using WF-OCTA images demonstrated improvement over the conventional OCT parameter-based results (all p-values < 0.05). This study demonstrated the effectiveness of applying deep learning to WF-OCTA for glaucoma diagnosis, highlighting the potential of WF-OCTA images in glaucoma diagnostics. Additionally, it showed that FSL could overcome the limitations associated with a small dataset and is expected to be applicable in various clinical settings.

Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia.

Glaucoma is the commonest cause of irreversible blindness worldwide, with over 70% of people affected remaining undiagnosed. Early detection is crucial for halting progressive visual impairment in glaucoma patients, as there is no cure available. This narrative review aims to: identify reasons for the significant under-diagnosis of glaucoma globally, particularly in Australia, elucidate the role of primary healthcare in glaucoma diagnosis using Australian healthcare as an example, and discuss how recent advances in artificial intelligence (AI) can be implemented to improve diagnostic outcomes. Glaucoma is a prevalent disease in ageing populations and can have improved visual outcomes through appropriate treatment, making it essential for general medical practice. In countries such as Australia, New Zealand, Canada, USA, and the UK, optometrists serve as the gatekeepers for primary eye care, and glaucoma detection often falls on their shoulders. However, there is significant variation in the capacity for glaucoma diagnosis among eye professionals. Automation with Artificial Intelligence (AI) analysis of optic nerve photos can help optometrists identify high-risk changes and mitigate the challenges of image interpretation rapidly and consistently. Despite its potential, there are significant barriers and challenges to address before AI can be deployed in primary healthcare settings, including external validation, high quality real-world implementation, protection of privacy and cybersecurity, and medico-legal implications. Overall, the incorporation of AI technology in primary healthcare has the potential to reduce the global prevalence of undiagnosed glaucoma cases by improving diagnostic accuracy and efficiency.

Novel Technologies in Artificial Intelligence and Telemedicine for Glaucoma Screening.

To provide an overview of novel technologies in telemedicine and artificial intelligence (AI) approaches for cost-effective glaucoma screening. A narrative review was performed by summarizing research results, recent developments in glaucoma detection and care, and considerations related to telemedicine and AI in glaucoma screening. Telemedicine and AI approaches provide the opportunity for novel glaucoma screening programs in primary care, optometry, portable, and home-based settings. These approaches offer several advantages for glaucoma screening, including increasing access to care, lowering costs, identifying patients in need of urgent treatment, and enabling timely diagnosis and early intervention. However, challenges remain in implementing these systems, including integration into existing clinical workflows, ensuring equity for patients, and meeting ethical and regulatory requirements. Leveraging recent work towards standardized data acquisition as well as tools and techniques developed for automated diabetic retinopathy screening programs may provide a model for a cost-effective approach to glaucoma screening. Leveraging novel technologies and advances in telemedicine and AI-based approaches to glaucoma detection show promise for improving our ability to detect moderate and advanced glaucoma in primary care settings and target higher individuals at high risk for having the disease.

Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis.

To quantify the performance of artificial intelligence (AI) in detecting glaucoma with spectral-domain optical coherence tomography (SD-OCT) images. Electronic databases including PubMed, Embase, Scopus, ScienceDirect, ProQuest and Cochrane Library were searched before May 31, 2023 which adopted AI for glaucoma detection with SD-OCT images. All pieces of the literature were screened and extracted by two investigators. Meta-analysis, Meta-regression, subgroup, and publication of bias were conducted by Stata16.0. The risk of bias assessment was performed in Revman5.4 using the QUADAS-2 tool. Twenty studies and 51 models were selected for systematic review and Meta-analysis. The pooled sensitivity and specificity were 0.91 (95%CI: 0.86-0.94, I2=94.67%), 0.90 (95%CI: 0.87-0.92, I2=89.24%). The pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 8.79 (95%CI: 6.93-11.15, I2=89.31%) and 0.11 (95%CI: 0.07-0.16, I2=95.25%). The pooled diagnostic odds ratio (DOR) and area under curve (AUC) were 83.58 (95%CI: 47.15-148.15, I2=100%) and 0.95 (95%CI: 0.93-0.97). There was no threshold effect (Spearman correlation coefficient=0.22, P>0.05). There is a high accuracy for the detection of glaucoma with AI with SD-OCT images. The application of AI-based algorithms allows together with "doctor+artificial intelligence" to improve the diagnosis of glaucoma.

An interventional study on awareness, knowledge and services provided for the diagnosis and treatment of glaucoma by optometrists in primary healthcare centre of Kolar district

Aim of this study was to assess the awareness, knowledge services provided for glaucoma among optometrists at primary health care.A cross-sectional questionnaire based study involving 36 Optometrists from Primary Healthcare Centre and government hospitals of Kolar and Chikkaballapur district, who participated in the ‘World Glaucoma awareness program’. The responses were compiled and analyzed for each category of questions using paired t test.Out of 36 participants 31 (86.11%) were males and 5 (13.89%) were females with an average age of 39.42+10.5 years and average duration of service in PHC/CHC/Taluk Hospital was 14.08 years. Among them 88.9% were aware about glaucoma being a disease due to increased intra ocular pressure (IOP). Although 69.4% knew that it leads to irreversible blindness, 75% were unaware of the symptoms that glaucoma presents with. 53% were unaware of the normal IOP of the eye and 41.7% the test to detect the same. 33.3% of hospitals had Shiotz tonometer but only 22.2% were aware of testing the IOP and peripheral visual field for diagnosing glaucoma. 61.1% were not familiar with any antiglaucoma drug and 52.8% about the duration of treatment. Post-test analysis was statistically significant with respect to the Awareness, Knowledge and Services (P=0.001, P=0.001, P=0.001).This study reveals the lacunae and highlights the importance of routine eye examination of rural population by Optometrists for early detection of glaucoma through improved services and timely referral to Ophthalmologists for further evaluation, thereby reducing the burden of visual impairment.

Diagnosis of glaucoma using multi‐scale attention block in convolution neural network and data augmentation techniques

AbstractGlaucoma is defined as an eye disease leading to vision loss due to the optic nerve damage. It is often asymptomatic, thus, timely diagnosis and treatment is crucial. In this article, we propose a novel approach for diagnosing glaucoma using deep neural networks, trained on fundus images. Our proposed approach involves several key steps, including data sampling, pre‐processing, and classification. To address the data imbalance issue, we employ a combination of suitable image augmentation techniques and Multi‐Scale Attention Block (MAS Block) architecture in our deep neural network model. The MAS Block is a specific architecture design for CNNs that allows multiple convolutional filters of various sizes to capture features at several scales in parallel. This will prevent the over‐fitting problem and increases the detection accuracy. Through extensive experiments with the ACRIMA dataset, we demonstrate that our proposed approach achieves high accuracy in diagnosing glaucoma. Notably, we recorded the highest accuracy (97.18%) among previous studies. The results from this study reveal the potential of our approach to improve early detection of glaucoma and offer more effective treatment strategies for doctors and clinicians in the future. Timely diagnosis plays a crucial role in managing glaucoma since it is often asymptomatic. Our proposed method utilizing deep neural networks shows promise in enhancing diagnostic accuracy and aiding healthcare professionals in making informed decisions.

A Review on the Detection and Classification of Glaucoma Disease Based on Transfer Learning

An eye infection is a condition affecting the eyes that can be caused by a bacterium, virus, or fungus. Numerous eye infections exist, such as glaucoma, cellulitis, keratitis, and conjunctivitis. A few of the symptoms may be itching, discharge, altered eyesight and others. Antibiotics are not effective in treating viral infections. Antibiotics treat infections caused by bacteria exclusively. A class of eye infection known as glaucoma can result in blindness and visual loss by harming the optic nerve, a nerve located at the back of the eye. You might not notice the symptoms at first because they can appear so slowly. A thorough dilated eye exam is the only way to determine if you have glaucoma . Efforts have been done to automate the procedures for the recognition and classification of glaucoma. In this paper, we have proposed a transfer learning model by reviewing pre-trained models and the model is able to provide a better accuracy. Our model is classifying the datasets into positive and negative cases during testing and validation. We utilize different prêt rained models, that are ResNet50 (90%), EffiecintNet (78%) and CNN(79%) evaluate how well they perform when trained using various optimizers. Our results show differences in accuracy and provide important information about the possibility of these models for the detection of glaucoma. An important first step towards improving the precision and dependability of glaucoma detection models in clinical settings is represented by this work.

Quantitative Measurement and Analysis of the Radial Peripapillary and Central Macular Microvasculature Density in Early Open-Angle Glaucoma Using OCT Angiography

Background Glaucoma is a prevalent cause of visual impairment, and early detection is critical to prevent progressive vision loss. Optical coherence tomography angiography (OCT-A) enables visualization of retinal and optic nerve microvasculature and holds promise for early glaucoma detection. Aims This study aims to assess microvasculature density alterations in glaucoma using OCT-A. Methods An observational cross-sectional case–control study was conducted at Benghazi Eye Hospital in Libya from December 2022 to August 2023. It included 160 eyes of 126 patients diagnosed with primary open-angle glaucoma and 120 eyes of 96 control subjects. OCT-A was performed using the Swept-Source OCT DRI Triton. Vessel density measurements were obtained from OCT-A images using new built-in software within the device. The data presented as mean ± standard deviation and percentages were analyzed using SPSS. The statistical significance of the differences between different groups was determined using independent t-tests, and the level of significance was set at p-value less than 0.05. Results OCT-A demonstrated significantly reduced microvasculature density in glaucoma eyes across all optic nerve sectors including the inferior (29.0%), superior (25.2%), central (23.5%), nasal (9.9%), and temporal sectors (9.3%; p &lt; 0.02). Moreover, glaucoma patients exhibited a significant reduction in vessel density in the superior macula (17% reduction), the temporal (15.7%,), nasal (12.9%), and inferior sectors (12.6%) (p &lt; 0.002). No statistical difference was seen with vessel density of the central sector (0.49%, p &gt;0.05). Conclusion OCT-A demonstrated significant reductions in microvasculature density in glaucoma patients compared with healthy controls. These findings support the association between glaucoma and vascular changes. Additionally, the TOPCON Swept-Source OCT DRI Triton new software shows promise as a valuable tool for early detection and monitoring of glaucoma-related vascular changes.

Improving Glaucoma Detection: Harnessing the Power of Ensemble Semantic Segmentation for Optic Disc and Optic Cup with Deep Learning

Improving Glaucoma Detection: Harnessing the Power of Ensemble Semantic Segmentation for Optic Disc and Optic Cup with Deep Learning

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.

Multi-Dataset Convolutional Neural Network Model for Glaucoma Prediction in OCT Fundus Scans

Glaucoma, a major factor in permanent blindness across the globe, necessitates accurate and efficient diagnostic methods. This article presents a comprehensive approach to glaucoma prediction by combining three diverse datasets: ORIGA, ACRIMA, and REFUGE. A novel multi-Datasets CNN (MD-CNN) architecture is proposed, specifically tailored to effectively handle combined data comprising diverse image characteristics across multiple datasets. This innovative approach demonstrates remarkable robustness in accommodating variations in image attributes, including lighting, zoom levels, and other disparate features, thus showcasing its potency in addressing glaucoma prediction across different datasets. The approach demonstrates improved accuracy (96.88%), sensitivity (94.34%), specificity (97.20%), precision (94.34%), and AUC (99.02%) compared to individual dataset-based models, addressing challenges in glaucoma detection. This research showcases the potential of combining diverse datasets for more effective CNN-based glaucoma detection.

Evaluation of corneal densitometry values with Pentacam in cases of ocular hypertension and pseudoexfoliative glaucoma

BackgroundExamination of corneal densitometry measurements using the Pentacam Scheimpflug imaging system in cases of pseudoexfoliative glaucoma (PEXG) and ocular hypertension (OHT). MethodsThe study included 50 eyes of 29 PEXG patients, 25 eyes of 16 OHT patients, and 76 eyes of 38 healthy control subjects followed in the glaucoma clinic. Corneal densitometry values of all cases were measured using the Scheimpflug imaging system (Pentacam, Oculus, Germany). Corneal densitometry was assessed based on 4 concentric radial zones (0–2 mm, 2–6 mm, 6–10 mm, and 10–12 mm) and depths (anterior, central, posterior, and total) within the Scheimpflug imaging system. The results were statistically analyzed. ResultsCorneal densitometry values examined between the OHT and control groups were higher in OHT and statistically significant (p < 0.05). Corneal densitometry values examined between the PEXG and control groups were higher in PEXG and statistically significant (p < 0.05). In comparison between the PEXG and OHT groups, corneal densitometry values in the central 0–2 mm, 2–6 mm, and 6–10 mm; posterior 0–2 mm and 2–6 mm radial zones were higher in PEXG and statistically significant (p < 0.05). ConclusionsIt was observed that elevated intraocular pressure levels in OHT cases could lead to changes in the cornea, consequently increasing corneal densitometry values. The higher corneal densitometry values in PEXG cases compared to OHT were attributed to the accumulation of pseudoexfoliative material in the cornea. Based on our study, corneal densitometry could serve as a potential biomarker for early glaucoma detection in OHT cases and could be employed to assess corneal transparency during the follow-up of PEXG cases.

Improved machine learning-based glaucoma detection from fundus images using texture features in FAWT and LS-SVM classifier

Improved machine learning-based glaucoma detection from fundus images using texture features in FAWT and LS-SVM classifier

Glaucoma detection model by exploiting multi-region and multi-scan-pattern OCT images with dynamical region score.

Currently, deep learning-based methods have achieved success in glaucoma detection. However, most models focus on OCT images captured by a single scan pattern within a given region, holding the high risk of the omission of valuable features in the remaining regions or scan patterns. Therefore, we proposed a multi-region and multi-scan-pattern fusion model to address this issue. Our proposed model exploits comprehensive OCT images from three fundus anatomical regions (macular, middle, and optic nerve head regions) being captured by four scan patterns (radial, volume, single-line, and circular scan patterns). Moreover, to enhance the efficacy of integrating features across various scan patterns within a region and multiple regional features, we employed an attention multi-scan fusion module and an attention multi-region fusion module that auto-assign contribution to distinct scan-pattern features and region features adapting to characters of different samples, respectively. To alleviate the absence of available datasets, we have collected a specific dataset (MRMSG-OCT) comprising OCT images captured by four scan patterns from three regions. The experimental results and visualized feature maps both demonstrate that our proposed model achieves superior performance against the single scan-pattern models and single region-based models. Moreover, compared with the average fusion strategy, our proposed fusion modules yield superior performance, particularly reversing the performance degradation observed in some models relying on fixed weights, validating the efficacy of the proposed dynamic region scores adapted to different samples. Moreover, the derived region contribution scores enhance the interpretability of the model and offer an overview of the model's decision-making process, assisting ophthalmologists in prioritizing regions with heightened scores and increasing efficiency in clinical practice.

Application of artificial intelligence in glaucoma care: An updated review.

The application of artificial intelligence (AI) in ophthalmology has been increasingly explored in the past decade. Numerous studies have shown promising results supporting the utility of AI to improve the management of ophthalmic diseases, and glaucoma is of no exception. Glaucoma is an irreversible vision condition with insidious onset, complex pathophysiology, and chronic treatment. Since there remain various challenges in the clinical management of glaucoma, the potential role of AI in facilitating glaucoma care has garnered significant attention. In this study, we reviewed the relevant literature published in recent years that investigated the application of AI in glaucoma management. The main aspects of AI applications that will be discussed include glaucoma risk prediction, glaucoma detection and diagnosis, visual field estimation and pattern analysis, glaucoma progression detection, and other applications.

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.

To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations. Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC). The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88). The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.