Glaucoma screening: where are we and where do we need to go?

Automatic retinal image analysis methods using colour fundus images for screening glaucomatous optic neuropathy

OBJECTIVES

Train an automatic retinal image analysis (ARIA) method to screen glaucomatous optic neuropathy (GON) on non-mydriatic retinal images labelled with the additional results of optical coherence tomography (OCT) and assess different models for the GON classification.

METHODS

All the images were obtained from the hospital for training and 10-fold cross-validation. Two methods were used to improve the classification performance: (1) using images labelled with the additional results of OCT as the reference standard and (2) generating models using retinal features from the entire images, the region of interest (ROI) of the optic disc, and the ROI of the macula, and the combination of all the features.

RESULTS

Overall, we collected 1338 images with paired OCT scans. In 10-fold validation, ARIA achieved sensitivities of 92.2 %, 92.7% and 85.7%, specificities of 88.8%, 86.7% and 80.2% and accuracies of 90.6%, 89.9% and 83.1% using the retinal features from the entire images, the ROI of the optic disc and the ROI of the macula, respectively. We found the model combining all the features has the best classification performance and obtained a sensitivity of 92.5%, a specificity of 92.1% and an accuracy of 92.4%, which is significantly different from other models (p<0.001).

CONCLUSION

We used two methods to improve the classification performance and found the best model to detect glaucoma on colour fundus retinal images. It can become a cost-effective and relatively more accurate glaucoma screening tool than conventional methods.

Barriers and Potential Solutions to Glaucoma Screening in the Developing World: A Review

PURPOSE

Glaucoma is a leading public health concern globally and its detection and management are way more complex and challenging in the developing world. This review article discusses barriers to glaucoma screening in developing countries from the perspective of different key stakeholders and proposes solutions.

METHODS/RESULTS

A literature search was carried out in the electronic catalogs of PubMed, Medline, and Cochrane database of systematic reviews to find studies that focused on barriers and enablers to glaucoma screening. The authors' interpretations were tabulated as descriptive and qualitative data and presented concisely from the point of view of key stakeholders such as the patients and their relatives, care providers, and system/governing bodies. Key barriers to glaucoma care identified are lack of awareness, poor accessibility to ophthalmic centers, inadequately trained human resources, unsatisfactory infrastructure, and nonavailability of financially viable screening programs. Educating care providers, as well as the public, providing care closer to where people live, and developing cost-effective screening strategies are needed to ensure proper identification of glaucoma patients in developing countries.

CONCLUSIONS

The logistics of glaucoma detection and management are complex. Hence, glaucoma detection programs should be implemented only when facilities for glaucoma management are in place. Understanding the importance of glaucoma screening and its future implications, addressing the various roadblocks, empowering and efficiently implementing the existing strategies, and incorporating novel ones using Artificial Intelligence (AI) and deep learning (DL) will help in establishing a robust glaucoma screening program in developing countries.

Telemedicine for Screening and Follow-Up of Glaucoma: A Descriptive Study

Introduction: Glaucoma is a leading cause of irreversible blindness. It is a prevalent disease worldwide, affecting ∼70 million people and expected to reach up to 112 million by 2040. Purpose: The aim of this study is to describe the implementation and initial experience of a telemedicine program to monitor glaucoma and glaucoma suspect patients in a large, integrated health care system during the COVID-19 pandemic. Methods: A retrospective chart review of established glaucoma or glaucoma suspect patients who participated in a telemedicine evaluation at the ophthalmic center of a large, Colombian health care system between June 2020 and April 2023 was conducted. Clinical and sociodemographic variables were analyzed. Generated clinical orders for additional testing, surgical procedures, follow-ups, and referrals, as well as changes in medical treatment, were evaluated. Results: A total of 11,034 telemedicine consults were included. The mean ± standard deviation age of this group was 63 ± 17.2 years and 67% were female. Of the patients who attended teleconsults, 49% were glaucoma suspects and 38.5% were followed with a diagnosis of open-angle glaucoma. After the consult, 25% of patients were referred to a glaucoma specialist, 40% had additional testing ordered, and 8% had a surgical procedure ordered, mainly laser iridotomy (409 cases). Almost a third of patients returned for subsequent telemedicine visits after the initial encounter. Despite some technical difficulties, 99.8% of patients attended and completed their scheduled telemedicine appointments. Conclusions: A telemedicine program aimed to monitor established glaucoma patients can be successfully implemented. Established patients within an integrated health care system have high adherence to the virtual model. Further research by health care institutions and government agencies will be key to expand coverage to additional populations. Clinical Trial Registration Number: CEIFUS 1026-24.

Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging

The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms.

A Novel Visual Field Screening Program for Glaucoma With a Head-Mounted Perimeter

PRCIS

A novel visual field screening program with a head-mounted perimeter 'imo' could detect glaucoma at all stages in a short time with high accuracy.

PURPOSE

The present study aimed to examine the accuracy and availability of a novel glaucoma visual field screening program using a head-mounted visual perimeter 'imo.'

PARTICIPANTS AND METHODS

Eyes of 76 non-glaucoma participants and 92 glaucoma patients were examined. All patients underwent visual field tests using the Humphrey Visual Field Analyzer (30-2 or 24-2 Swedish Interactive Thresholding Algorithm standard program) and imo (the visual field screening program). We evaluated five visual field screening program indicators: sensitivity, specificity, positive predictive value, negative predictive value, and testing time. We also evaluated the ability of this visual field screening program to differentiate between glaucoma patients and normal controls using the receiver operating characteristic curves and areas under the receiver operating characteristic curves.

RESULTS

The sensitivity, specificity, positive predictive value, and negative predictive value of the visual field screening program were 76%-100%, 91%-100%, 86%-89%, and 79%-100%, respectively. The visual field screening program test time was 46±13 seconds for normal controls and 61±18, 82±21, and 105±16 econds, respectively for mild, moderate, and advanced-stage patients. The areas under the receiver operating characteristic curves were 0.77, 0.97, and 1.0 in the mild, moderate, and advanced stages, respectively.

CONCLUSIONS

Visual field screening using a head-mounted perimeter 'imo' detected glaucoma at all stages in a short time with high accuracy.

Characteristics of a large, labeled dataset for the training of artificial intelligence for glaucoma screening with fundus photographs

Purpose

Significant visual impairment due to glaucoma is largely caused by the disease being detected too late.

Objective

To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to characterize the features of all eyes with referable glaucoma (RG).

Design

Cross-sectional study.

Subjects

Color fundus photographs (CFPs) of 113 893 eyes of 60 357 individuals were obtained from EyePACS, California, United States, from a population screening program for diabetic retinopathy.

Methods

Carefully selected graders (ophthalmologists and optometrists) graded the images. To qualify, they had to pass the European Optic Disc Assessment Trial optic disc assessment with ≥ 85% accuracy and 92% specificity. Of 90 candidates, 30 passed. Each image of the EyePACS set was then scored by varying random pairs of graders as "RG," "no referable glaucoma (NRG)," or "ungradable (UG)." In case of disagreement, a glaucoma specialist made the final grading. Referable glaucoma was scored if visual field damage was expected. In case of RG, graders were instructed to mark up to 10 relevant glaucomatous features.

Main Outcome Measures

Qualitative features in eyes with RG.

Results

The performance of each grader was monitored; if the sensitivity and specificity dropped below 80% and 95%, respectively (the final grade served as reference), they exited the study and their gradings were redone by other graders. In all, 20 graders qualified; their mean sensitivity and specificity (standard deviation [SD]) were 85.6% (5.7) and 96.1% (2.8), respectively. The 2 graders agreed in 92.45% of the images (Gwet's AC2, expressing the inter-rater reliability, was 0.917). Of all gradings, the sensitivity and specificity (95% confidence interval) were 86.0 (85.2-86.7)% and 96.4 (96.3-96.5)%, respectively. Of all gradable eyes (n = 111 183; 97.62%) the prevalence of RG was 4.38%. The most common features of RG were the appearance of the neuroretinal rim (NRR) inferiorly and superiorly.

Conclusions

A large data set of CFPs was put together of sufficient quality to develop AI screening solutions for glaucoma. The most common features of RG were the appearance of the NRR inferiorly and superiorly. Disc hemorrhages were a rare feature of RG.

Financial Disclosures

Proprietary or commercial disclosure may be found after the references.

DDLSNet: A Novel Deep Learning-Based System for Grading Funduscopic Images for Glaucomatous Damage

Purpose

To report an image analysis pipeline, DDLSNet, consisting of a rim segmentation (RimNet) branch and a disc size classification (DiscNet) branch to automate estimation of the disc damage likelihood scale (DDLS).

Design

Retrospective observational.

Participants

RimNet and DiscNet were developed with 1208 and 11 536 optic disc photographs (ODPs), respectively. DDLSNet performance was evaluated on 120 ODPs from the RimNet test set, for which the DDLS scores were graded by clinicians. Reproducibility was evaluated on a group of 781 eyes, each with 2 ODPs taken within 4 years apart.

Methods

Disc damage likelihood scale calculation requires estimation of optic disc size, provided by DiscNet (VGG19 network), and the minimum rim-to-disc ratio (mRDR) or absent rim width (ARW), provided by RimNet (InceptionV3/LinkNet segmentation model). To build RimNet's dataset, glaucoma specialists marked optic disc rim and cup boundaries on ODPs. The "ground truth" mRDR or ARW was calculated. For DiscNet's dataset, corresponding OCT images provided "ground truth" disc size. Optic disc photographs were split into 80/10/10 for training, validation, and testing, respectively, for RimNet and DiscNet. DDLSNet estimation was tested against manual grading of DDLS by clinicians with the average score used as "ground truth." Reproducibility of DDLSNet grading was evaluated by repeating DDLS estimation on a dataset of nonprogressing paired ODPs taken at separate times.

Main Outcome Measures

The main outcome measure was a weighted kappa score between clinicians and the DDLSNet pipeline with agreement defined as ± 1 DDLS score difference.

Results

RimNet achieved an mRDR mean absolute error (MAE) of 0.04 (± 0.03) and an ARW MAE of 48.9 (± 35.9) degrees when compared to clinician segmentations. DiscNet achieved 73% (95% confidence interval [CI]: 70%, 75%) classification accuracy. DDLSNet achieved an average weighted kappa agreement of 0.54 (95% CI: 0.40, 0.68) compared to clinicians. Average interclinician agreement was 0.52 (95% CI: 0.49, 0.56). Reproducibility testing demonstrated that 96% of ODP pairs had a difference of ≤ 1 DDLS score.

Conclusions

DDLSNet achieved moderate agreement with clinicians for DDLS grading. This novel approach illustrates the feasibility of automated ODP grading for assessing glaucoma severity. Further improvements may be achieved by increasing the number of incomplete rims sample size, expanding the hyperparameter search, and increasing the agreement of clinicians grading ODPs.

Real-Time Risk Score for Glaucoma Mass Screening by Spectral Domain Optical Coherence Tomography: Development and Validation

Purpose

To develop and validate a risk score assessable in real-time using only retinal thickness-related values measured by spectral domain optical coherence tomography alone for use in population-based glaucoma mass screenings.

Methods

A total of 7572 participants (aged 35-74 years) underwent spectral domain optical coherence tomography examination annually between 2016 to 2021 in a population-based setting. We selected 284 glaucoma cases and 284 controls, matched by age and sex, from 11,487 scans in 2016. We conducted multivariable logistic regression with backward stepwise selection of retinal thickness-related variables to develop the diagnostic models. The developed risk scores were applied to all participants in 2018 (9720 eyes), and we randomly selected 723 scans for validation. Additional validation using the Humphrey field analyzer was conducted on 129 eyes in 2020. We assessed the models using sensitivity, specificity, the area under the receiver operating characteristic curve and positive and negative predictive values.

Results

The best-predicting model achieved an area under the receiver operating characteristic curve of 0.97 (95% confidence interval, 0.96-0.98) with a sensitivity of 0.93 and specificity of 0.91. The validation dataset showed a positive predictive value of 90.8% for high-risk scorers, corresponding to 6.2% of the population, and negative predictive value of 88.2% for low-risk scorers, corresponding to 85.2%. Sensitivity and specificity for glaucoma diagnosis were 0.85 and 0.91, when we set the risk score cut-off at 90 points out of 100.

Conclusions

This risk score could be used as a valid index for glaucoma screening in a population-based setting.

Translational Relevance

The score is feasible by installing a simple computer application on an existing spectral domain optical coherence tomography and will help to improve the accuracy and efficiency of glaucoma screening.

Evaluating machine learning classifiers for glaucoma referral decision support in primary care settings

Several artificial intelligence algorithms have been proposed to help diagnose glaucoma by analyzing the functional and/or structural changes in the eye. These algorithms require carefully curated datasets with access to ocular images. In the current study, we have modeled and evaluated classifiers to predict self-reported glaucoma using a single, easily obtained ocular feature (intraocular pressure (IOP)) and non-ocular features (age, gender, race, body mass index, systolic and diastolic blood pressure, and comorbidities). The classifiers were trained on publicly available data of 3015 subjects without a glaucoma diagnosis at the time of enrollment. 337 subjects subsequently self-reported a glaucoma diagnosis in a span of 1–12 years after enrollment. The classifiers were evaluated on the ability to identify these subjects by only using their features recorded at the time of enrollment. Support vector machine, logistic regression, and adaptive boosting performed similarly on the dataset with F1 scores of 0.31, 0.30, and 0.28, respectively. Logistic regression had the highest sensitivity at 60% with a specificity of 69%. Predictive classifiers using primarily non-ocular features have the potential to be used for identifying suspected glaucoma in non-eye care settings, including primary care. Further research into finding additional features that improve the performance of predictive classifiers is warranted.

A Comprehensive Review of Methods and Equipment for Aiding Automatic Glaucoma Tracking

Glaucoma is a chronic optic neuropathy characterized by irreversible damage to the retinal nerve fiber layer (RNFL), resulting in changes in the visual field (VC). Glaucoma screening is performed through a complete ophthalmological examination, using images of the optic papilla obtained in vivo for the evaluation of glaucomatous characteristics, eye pressure, and visual field. Identifying the glaucomatous papilla is quite important, as optical papillary images are considered the gold standard for tracking. Therefore, this article presents a review of the diagnostic methods used to identify the glaucomatous papilla through technology over the last five years. Based on the analyzed works, the current state-of-the-art methods are identified, the current challenges are analyzed, and the shortcomings of these methods are investigated, especially from the point of view of automation and independence in performing these measurements. Finally, the topics for future work and the challenges that need to be solved are proposed.

Analysis of international publication trends in artificial intelligence in ophthalmology

PURPOSE

Artificial intelligence (AI) has entered the field of medicine, and ophthalmology is no exception. The objective of this study was to report on scientific production and publication trends, to identify journals, countries, international collaborations, and major MeSH terms involved in AI in ophthalmology research.

METHODS

Scientometric methods were used to evaluate global scientific production and development trends in AI in ophthalmology using PubMed and the Web of Science Core Collection.

RESULTS

A total of 1356 articles were retrieved over the period 1966-2019. The yearly growth of AI in ophthalmology publications has been 18.89% over the last ten years, indicating that AI in ophthalmology is a very attractive topic in science. Analysis of the most productive journals showed that most were specialized in computer and medical systems. No journal was found to specialize in AI in ophthalmology. The USA, China, and the UK were the three most productive countries. The study of international collaboration showed that, besides the USA, researchers tended to collaborate with peers from neighboring countries. Among the twenty most frequent MeSH terms retrieved, there were only four related to clinical topics, revealing the retina and glaucoma as the most frequently encountered subjects of interest in AI in ophthalmology. Analysis of the top ten Journal Citation Reports categories of journals and MeSH terms for articles confirmed that AI in ophthalmology research is mainly focused on engineering and computing and is mainly technical research related to computer methods.

CONCLUSIONS

This study provides a broad view of the current status and trends in AI in ophthalmology research and shows that AI in ophthalmology research is an attractive topic focusing on retinal diseases and glaucoma. This study may be useful for researchers in AI in ophthalmology such as clinicians, but also for scientists to better understand this research topic, know the main actors in this field (including journals and countries), and have a general overview of this research theme.

[Artificial intelligence and glaucoma: A literature review]

In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.

Artificial Intelligence and OCT Angiography in Full Thickness Macular Hole. New Developments for Personalized Medicine

Purpose: To evaluate the 1-year visual acuity predictive performance of an artificial intelligence (AI) based model applied to optical coherence tomography angiography (OCT-A) vascular layers scans from eyes with a full-thickness macular hole (FTMH). Methods: In this observational cross-sectional, single-center study, 35 eyes of 35 patients with FTMH were analyzed by OCT-A before and 1-year after surgery. Superficial vascular plexus (SVP) and deep vascular plexus (DVP) images were collected for the analysis. AI approach based on convolutional neural networks (CNN) was used to generate a continuous predictive variable based on both SVP and DPV. Different pre-trained CNN networks were used for feature extraction and compared for predictive accuracy. Results: Among the different tested models, the inception V3 network, applied on the combination of deep and superficial OCT-A images, showed the most significant differences between the two obtained image clusters defined in C1 and C2 (best-corrected visual acuity (BCVA) C1 = 66.67 (16.00 SD) and BCVA C2 = 49.10 (18.60 SD, p = 0.005)). Conclusions: The AI-based analysis of preoperative OCT-A images of eyes affected by FTMH may be a useful support system in setting up visual acuity recovery prediction. The combination of preoperative SVP and DVP images showed a significant morphological predictive performance for visual acuity recovery.

Deep learning on fundus images detects glaucoma beyond the optic disc

Although unprecedented sensitivity and specificity values are reported, recent glaucoma detection deep learning models lack in decision transparency. Here, we propose a methodology that advances explainable deep learning in the field of glaucoma detection and vertical cup-disc ratio (VCDR), an important risk factor. We trained and evaluated deep learning models using fundus images that underwent a certain cropping policy. We defined the crop radius as a percentage of image size, centered on the optic nerve head (ONH), with an equidistant spaced range from 10-60% (ONH crop policy). The inverse of the cropping mask was also applied (periphery crop policy). Trained models using original images resulted in an area under the curve (AUC) of 0.94 [95% CI 0.92-0.96] for glaucoma detection, and a coefficient of determination (R2) equal to 77% [95% CI 0.77-0.79] for VCDR estimation. Models that were trained on images with absence of the ONH are still able to obtain significant performance (0.88 [95% CI 0.85-0.90] AUC for glaucoma detection and 37% [95% CI 0.35-0.40] R2 score for VCDR estimation in the most extreme setup of 60% ONH crop). Our findings provide the first irrefutable evidence that deep learning can detect glaucoma from fundus image regions outside the ONH.

Comparison of Structural, Functional, Tonometric, and Visual Acuity Testing for Glaucoma: A Prospective Diagnostic Accuracy Study

PURPOSE

To determine the diagnostic accuracy of potential screening tests for moderate to advanced glaucoma.

DESIGN

Prospective diagnostic test accuracy study.

PARTICIPANTS

The study enrolled a consecutive series of patients aged ≥50 years who presented to a glaucoma clinic in South India without ever having received automated visual field testing.

METHODS

All participants underwent 8 index tests: OCT of the peripapillary retinal nerve fiber layer, optic disc photography, Moorfield's Motion Displacement Test (MDT), frequency doubling technique perimetry, noncontact tonometry, pneumatonometry, presenting visual acuity, and best-corrected visual acuity. Participants also underwent stereoscopic photographs and Humphrey visual fields, which were used by 2 ophthalmologists to arrive at the reference standard diagnosis of moderate to advanced glaucoma.

MAIN OUTCOME MEASURES

Sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio.

RESULTS

A total of 217 people were enrolled; 321 eyes from 180 participants had all tests performed. Of these, 127 eyes (40%) were classified as having moderate to advanced glaucoma. Among the 8 tests, OCT best optimized sensitivity (84%, 95% confidence interval [CI], 76-90) and specificity (75%, 95% CI, 68-81). Moorfield's Motion Displacement Test was the best perimetric test, with a sensitivity of 91% (95% CI, 85-96) and specificity of 53% (95% CI, 44-61). Pressure and vision tests were not sensitive (e.g., sensitivity of 16%, 95% CI, 9-23 for noncontact tonometry and 23%, 95% CI, 15-31 for best-corrected visual acuity). Moorfield's Motion Displacement Test identified 16 of 127 eyes (13%) with glaucoma that were not captured by OCT, but also had false-positive results in 65 of 194 eyes (34%) without glaucoma that OCT correctly classified as negative.

CONCLUSIONS

OCT had moderate sensitivity and fair specificity for diagnosing moderate to advanced glaucoma and should be prioritized during an initial assessment for glaucoma.

Comparison of the SITA Faster-a new visual field strategy with SITA Fast strategy

AIM

To compare visual field defects using the Swedish Interactive Thresholding Algorithm (SITA) Fast strategy with SITA Faster strategy, a newly developed time-saving threshold visual field strategy.

METHODS

Ninety-three participants (60 glaucoma patients and 33 normal controls) were enrolled. One eye from each participant was selected randomly for the study. SITA Fast and SITA Faster were performed using the 24-2 default mode for each test. The differences of visual field defects between the two strategies were compared using the test duration, false-positive response errors, mean deviation (MD), visual field index (VFI) and the numbers of depressed test points at the significant levels of P<5%, <2%, <1%, and <0.5% in probability plots. The correlation between strategies was analyzed. The agreement between strategies was acquired by Bland-Altman analysis.

RESULTS

Mean test durations were 246.0±60.9s for SITA Fast, and 156.3±46.3s for SITA Faster (P<0.001). The test duration of SITA Faster was 36.5% shorter than SITA Fast. The MD, VFI and numbers of depressed points at P<5%, <2%, <1%, and <0.5% in probability plots showed no statistically significant difference between two strategies (P>0.05). Correlation analysis showed a high correlation for MD (r=0.986, P<0.001) and VFI (r=0.986, P<0.001) between the two strategies. Bland-Altman analysis showed great agreement between the two strategies.

CONCLUSION

SITA Faster, which saves considerable test time, has a great test quality comparing to SITA Fast, but may be not directly interchangeable.

Accuracy of Using Generative Adversarial Networks for Glaucoma Detection During the COVID-19 Pandemic: A Systematic Review and Bibliometric Analysis

Background

Glaucoma leads to irreversible blindness. Globally, it is the second most common retinal disease that leads to blindness, slightly less common than cataracts. Therefore, there is a great need to avoid the silent growth of this disease using recently developed generative adversarial networks (GANs).

Objective

This paper aims to introduce a GAN technology for the diagnosis of eye disorders, particularly glaucoma. This paper illustrates deep adversarial learning as a potential diagnostic tool and the challenges involved in its implementation. This study describes and analyzes many of the pitfalls and problems that researchers will need to overcome to implement this kind of technology.

Methods

To organize this review comprehensively, articles and reviews were collected using the following keywords: (“Glaucoma,” “optic disc,” “blood vessels”) and (“receptive field,” “loss function,” “GAN,” “Generative Adversarial Network,” “Deep learning,” “CNN,” “convolutional neural network” OR encoder). The records were identified from 5 highly reputed databases: IEEE Xplore, Web of Science, Scopus, ScienceDirect, and PubMed. These libraries broadly cover the technical and medical literature. Publications within the last 5 years, specifically 2015-2020, were included because the target GAN technique was invented only in 2014 and the publishing date of the collected papers was not earlier than 2016. Duplicate records were removed, and irrelevant titles and abstracts were excluded. In addition, we excluded papers that used optical coherence tomography and visual field images, except for those with 2D images. A large-scale systematic analysis was performed, and then a summarized taxonomy was generated. Furthermore, the results of the collected articles were summarized and a visual representation of the results was presented on a T-shaped matrix diagram. This study was conducted between March 2020 and November 2020.

Results

We found 59 articles after conducting a comprehensive survey of the literature. Among the 59 articles, 30 present actual attempts to synthesize images and provide accurate segmentation/classification using single/multiple landmarks or share certain experiences. The other 29 articles discuss the recent advances in GANs, do practical experiments, and contain analytical studies of retinal disease.

Conclusions

Recent deep learning techniques, namely GANs, have shown encouraging performance in retinal disease detection. Although this methodology involves an extensive computing budget and optimization process, it saturates the greedy nature of deep learning techniques by synthesizing images and solves major medical issues. This paper contributes to this research field by offering a thorough analysis of existing works, highlighting current limitations, and suggesting alternatives to support other researchers and participants in further improving and strengthening future work. Finally, new directions for this research have been identified.

Code-free deep learning for multi-modality medical image classification

A number of large technology companies have created code-free cloud-based platforms that allow researchers and clinicians without coding experience to create deep learning algorithms. In this study, we comprehensively analyse the performance and featureset of six platforms, using four representative cross-sectional and en-face medical imaging datasets to create image classification models. The mean (s.d.) F1 scores across platforms for all model–dataset pairs were as follows: Amazon, 93.9 (5.4); Apple, 72.0 (13.6); Clarifai, 74.2 (7.1); Google, 92.0 (5.4); MedicMind, 90.7 (9.6); Microsoft, 88.6 (5.3). The platforms demonstrated uniformly higher classification performance with the optical coherence tomography modality. Potential use cases given proper validation include research dataset curation, mobile ‘edge models’ for regions without internet access, and baseline models against which to compare and iterate bespoke deep learning approaches.

Cluster-randomised trial of community-based screening for eye disease in adults in Nepal: the Village-Integrated Eye Worker Trial II (VIEW II) trial protocol

INTRODUCTION

The majority of blindness worldwide could be prevented or reversed with early diagnosis and treatment, yet identifying at-risk and prevalent cases of eye disease and linking them with care remain important obstacles to addressing this burden. Leading causes of blindness like glaucoma, diabetic retinopathy and age-related macular degeneration have detectable early asymptomatic phases and can cause irreversible vision loss. Mass screening for such diseases could reduce visual impairment at the population level.

METHODS AND ANALYSIS

This protocol describes a parallel-group cluster-randomised trial designed to determine whether community-based screening for glaucoma, diabetic retinopathy and age-related macular degeneration reduces population-level visual impairment in Nepal. A door-to-door population census is conducted in all study communities. All adults aged ≥60 years have visual acuity tested at the census visit, and those meeting referral criteria are referred to a local eye care facility for further diagnosis and management. Communities are subsequently randomised to a community-based screening programme or to no additional intervention. The intervention consists of a single round of screening including intraocular pressure and optical coherence tomography assessment of all adults ≥60 years old with enhanced linkage to care for participants meeting referral criteria. Four years after implementation of the intervention, masked outcome assessors conduct a repeat census to collect data on the primary outcome, visual acuity. Individuals with incident visual impairment receive a comprehensive ophthalmological examination to determine the cause of visual impairment. Outcomes are compared by treatment arm according to the originally assigned intervention.

ETHICS AND DISSEMINATION

The trial has received ethical approval from the University of California San Francisco Institutional Review Board, Nepal Netra Jyoti Sangh and the Nepal Health Research Council. Results of this trial will be disseminated through publication in peer-reviewed journals and presentation at local and international meetings.

TRIAL REGISTRATION NUMBER

NCT03752840.

Deep learning in glaucoma with optical coherence tomography: a review

Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has made significant breakthroughs in medical imaging, particularly for image classification and pattern recognition. In ophthalmology, applying DL for glaucoma assessment with optical coherence tomography (OCT), including OCT traditional reports, two-dimensional (2D) B-scans, and three-dimensional (3D) volumetric scans, has increasingly raised research interests. Studies have demonstrated that using DL for interpreting OCT is efficient, accurate, and with good performance for discriminating glaucomatous eyes from normal eyes, suggesting that incorporation of DL technology in OCT for glaucoma assessment could potentially address some gaps in the current practice and clinical workflow. However, further research is crucial in tackling some existing challenges, such as annotation standardization (i.e., setting a standard for ground truth labelling among different studies), development of DL-powered IT infrastructure for real-world implementation, prospective validation in unseen datasets for further evaluation of generalizability, cost-effectiveness analysis after integration of DL, the AI "black box" explanation problem. This review summarizes recent studies on the application of DL on OCT for glaucoma assessment, identifies the potential clinical impact arising from the development and deployment of the DL models, and discusses future research directions.

Telemedicine in ophthalmology in view of the emerging COVID-19 outbreak

Purpose

Technological advances in recent years have resulted in the development and implementation of various modalities and techniques enabling medical professionals to remotely diagnose and treat numerous medical conditions in diverse medical fields, including ophthalmology. Patients who require prolonged isolation until recovery, such as those who suffer from COVID-19, present multiple therapeutic dilemmas to their caregivers. Therefore, utilizing remote care in the daily workflow would be a valuable tool for the diagnosis and treatment of acute and chronic ocular conditions in this challenging clinical setting. Our aim is to review the latest technological and methodical advances in teleophthalmology and highlight their implementation in screening and managing various ocular conditions. We present them as well as potential diagnostic and treatment applications in view of the recent SARS-CoV-2 virus outbreak.

Methods

A computerized search from January 2017 up to March 2020 of the online electronic database PubMed was performed, using the following search strings: “telemedicine,” “telehealth,” and “ophthalmology.” More generalized complementary contemporary research data regarding the COVID-19 pandemic was also obtained from the PubMed database.

Results

A total of 312 records, including COVID-19-focused studies, were initially identified. After exclusion of non-relevant, non-English, and duplicate studies, a total of 138 records were found eligible. Ninety records were included in the final qualitative analysis.

Conclusion

Teleophthalmology is an effective screening and management tool for a range of adult and pediatric acute and chronic ocular conditions. It is mostly utilized in screening of retinal conditions such as retinopathy of prematurity, diabetic retinopathy, and age-related macular degeneration; in diagnosing anterior segment condition; and in managing glaucoma. With improvements in image processing, and better integration of the patient’s medical record, teleophthalmology should become a more accepted modality, all the more so in circumstances where social distancing is inflicted upon us.

Artificial intelligence and deep learning in glaucoma: Current state and future prospects

Over the past few years, there has been an unprecedented and tremendous excitement for artificial intelligence (AI) research in the field of Ophthalmology; this has naturally been translated to glaucoma-a progressive optic neuropathy characterized by retinal ganglion cell axon loss and associated visual field defects. In this review, we aim to discuss how AI may have a unique opportunity to tackle the many challenges faced in the glaucoma clinic. This is because glaucoma remains poorly understood with difficulties in providing early diagnosis and prognosis accurately and in a timely fashion. In the short term, AI could also become a game changer by paving the way for the first cost-effective glaucoma screening campaigns. While there are undeniable technical and clinical challenges ahead, and more so than for other ophthalmic disorders whereby AI is already booming, we strongly believe that glaucoma specialists should embrace AI as a companion to their practice. Finally, this review will also remind ourselves that glaucoma is a complex group of disorders with a multitude of physiological manifestations that cannot yet be observed clinically. AI in glaucoma is here to stay, but it will not be the only tool to solve glaucoma.

Personalising surgical treatments for glaucoma patients

Surgical treatments for glaucoma have relied for decades on traditional filtering surgery such as trabeculectomy and, in more challenging cases, tubes. Antifibrotics were introduced to improve surgical success in patients at increased risk of failure but have been shown to be linked to a greater incidence of complications, some being potentially vision-threatening. As our understanding of glaucoma and its early diagnosis have improved, a more individualised management has been suggested. Recently the term "precision medicine" has emerged as a new concept of an individualised approach to disease management incorporating a wide range of individual data in the choice of therapeutic modalities. For glaucoma surgery, this involves evaluation of the right timing, individual risk factors, targeting the correct anatomical and functional outflow pathways and appropriate prevention of scarring. As a consequence, there is an obvious need for better knowledge of anatomical and functional pathways and for more individualised surgical approaches with new, less invasive and safer techniques allowing for earlier intervention. With the recent advent of minimally invasive glaucoma surgery (MIGS) a large number of novel devices have been introduced targeting potential new sites of the outflow pathway for lowering intraocular pressure (IOP). Their popularity is growing in view of the relative surgical simplicity and apparent lack of serious side effects. However, these new surgical techniques are still in an era of early experiences, short follow-up and lack of evidence of their superiority in safety and cost-effectiveness over the traditional methods. Each year several new devices are introduced while others are withdrawn from the market. Glaucoma continues to be the primary cause of irreversible blindness worldwide and access to safe and efficacious treatment is a serious problem, particularly in the emerging world where the burden of glaucoma-related blindness is important and concerning. Early diagnosis, individualised treatment and, very importantly, safe surgical management should be the hallmarks of glaucoma treatment. However, there is still need for a better understanding of the disease, its onset and progression, the functional and structural elements of the outflow pathways in relation to the new devices as well as their long-term IOP-lowering efficacy and safety. This review discusses current knowledge and the future need for personalised glaucoma surgery.