Neural Network-Based Retinal Nerve Fiber Layer Profile Compensation for Glaucoma Diagnosis in Myopia: Model Development and Validation

Evaluating glaucoma in myopic eyes: Challenges and opportunities

The increasing global prevalence of myopia presents a significant public health concern, and growing evidence has demonstrated that myopia is a major risk factor for the development of open-angle glaucoma. Therefore, timely detection and management of glaucoma in myopic patients are crucial; however, identifying the structural alterations of glaucoma in the optic nerve head (ONH) and retinal tissues of myopic eyes using standard diagnostic tools such as fundus photography, optical coherence tomography (OCT), and OCT angiography (OCTA) presents challenges. Additionally, myopia-related perimetric defects can be confounded with glaucoma-related defects. We comprehensively examine the challenges encountered in evaluating glaucoma in myopic eyes through various diagnostic tools, including fundus photography, OCT of the ONH, retinal nerve fiber layer, and macular ganglion cell layer, OCTA, and perimetry. We also explore potential opportunities to address these challenges, providing insights for clinicians to effectively manage myopic glaucoma patients in clinical practice.

Machine Learning Approaches in High Myopia: Systematic Review and Meta-Analysis

BACKGROUND

In recent years, with the rapid development of machine learning (ML), it has gained widespread attention from researchers in clinical practice. ML models appear to demonstrate promising accuracy in the diagnosis of complex diseases, as well as in predicting disease progression and prognosis. Some studies have applied it to ophthalmology, primarily for the diagnosis of pathologic myopia and high myopia-associated glaucoma, as well as for predicting the progression of high myopia. ML-based detection still requires evidence-based validation to prove its accuracy and feasibility.

OBJECTIVE

This study aims to discern the performance of ML methods in detecting high myopia and pathologic myopia in clinical practice, thereby providing evidence-based support for the future development and refinement of intelligent diagnostic or predictive tools.

METHODS

PubMed, Cochrane, Embase, and Web of Science were thoroughly retrieved up to September 3, 2023. The prediction model risk of bias assessment tool was leveraged to appraise the risk of bias in the eligible studies. The meta-analysis was implemented using a bivariate mixed-effects model. In the validation set, subgroup analyses were conducted based on the ML target events (diagnosis and prediction of high myopia and diagnosis of pathological myopia and high myopia-associated glaucoma) and modeling methods.

RESULTS

This study ultimately included 45 studies, of which 32 were used for quantitative meta-analysis. The meta-analysis results unveiled that for the diagnosis of pathologic myopia, the summary receiver operating characteristic (SROC), sensitivity, and specificity of ML were 0.97 (95% CI 0.95-0.98), 0.91 (95% CI 0.89-0.92), and 0.95 (95% CI 0.94-0.97), respectively. Specifically, deep learning (DL) showed an SROC of 0.97 (95% CI 0.95-0.98), sensitivity of 0.92 (95% CI 0.90-0.93), and specificity of 0.96 (95% CI 0.95-0.97), while conventional ML (non-DL) showed an SROC of 0.86 (95% CI 0.75-0.92), sensitivity of 0.77 (95% CI 0.69-0.84), and specificity of 0.85 (95% CI 0.75-0.92). For the diagnosis and prediction of high myopia, the SROC, sensitivity, and specificity of ML were 0.98 (95% CI 0.96-0.99), 0.94 (95% CI 0.90-0.96), and 0.94 (95% CI 0.88-0.97), respectively. For the diagnosis of high myopia-associated glaucoma, the SROC, sensitivity, and specificity of ML were 0.96 (95% CI 0.94-0.97), 0.92 (95% CI 0.85-0.96), and 0.88 (95% CI 0.67-0.96), respectively.

CONCLUSIONS

ML demonstrated highly promising accuracy in diagnosing high myopia and pathologic myopia. Moreover, based on the limited evidence available, we also found that ML appeared to have favorable accuracy in predicting the risk of developing high myopia in the future. DL can be used as a potential method for intelligent image processing and intelligent recognition, and intelligent examination tools can be developed in subsequent research to provide help for areas where medical resources are scarce.

TRIAL REGISTRATION

PROSPERO CRD42023470820; https://tinyurl.com/2xexp738.

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

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

Optic disc tilt and rotation effects on positions of superotemporal and inferotemporal retinal nerve fibre layer peaks in myopic Caucasians

CLINICAL RELEVANCE

In myopic eyes, the optic disc may become tilted and rotated, making glaucoma diagnosis more difficult.

BACKGROUND

To determine the presence of tilted optic disc, the degree of optic disc rotation, and their effects on the angular location of superotemporal and inferotemporal retinal nerve fibre layer (RNFL) peaks in healthy myopic Caucasians.

METHODS

Non-glaucomatous healthy myopic Caucasian eyes with an axial length > 24 mm were evaluated. ImageJ was used to quantify optic disc tilt and torsion on red-free fundus photography. The RNFL was scanned using spectral-domain optical coherence tomography. The angle of the superotemporal and inferotemporal peaks with the vertical-horizontal meridian was measured.

RESULTS

Fifty-four eyes of 54 individuals were evaluated. The axial length was correlated with the angular location for both the superotemporal (r = -0.549, p < 0.001) and inferotemporal (r = -0.415, p = 0.002) RNFL peaks; they were placed more temporally in eyes with higher axial lengths. For each 1 mm increase in axial length, the angle between the superotemporal peak and the temporal horizontal meridian decreased by 3.976°, and the angle between the inferotemporal apex and the temporal horizontal meridian decreased by 3.028°. The angle between the inferotemporal peak and the temporal horizontal meridian decreased by 0.231° for each 1° increase in optical disc torsion (R2 = 0.09 Regression coefficient = -0.231, p = 0.027).

CONCLUSIONS

The temporal shift of superior and inferior peaks, the thickening of temporal and nasal RNFL, the presence of tilted optic disc, and optic disc rotation may cause misinterpretation of the RNFL in myopic Caucasians. When evaluating peripapillary RNFL thickness in myopic individuals, it would be better to consider these to avoid misinterpretation.

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

Measures of disease activity in glaucoma

Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future.