Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence

The Extraordinary Phenotypic and Genetic Variability of Retinal and Macular Degenerations: The Relevance to Therapeutic Developments

Inherited retinal diseases (IRDs) are a clinically and genetically heterogeneous group of rare conditions leading to various degrees of visual handicap and to progressive blindness in more severe cases. Besides visual rehabilitation, educational, and socio-professional support, there are currently limited therapeutic options, but the approval of the first gene therapy product for RPE65-related IRDs raised hope for therapeutic innovations. Such developments are facing obstacles intrinsic to the disease and the affected tissue including the extreme phenotypic and genetic variability of IRDs and the fine tuning of visual processing through the complex architecture of the postmitotic neural retina. A precise phenotypic characterization is required prior to genetic testing, which now relies on high-throughput sequencing. Their challenges will be discussed within this article as well as their implications in clinical trial design.

Artificial intelligence in retinal disease: clinical application, challenges, and future directions

Retinal diseases are a leading cause of blindness in developed countries, accounting for the largest share of visually impaired children, working-age adults (inherited retinal disease), and elderly individuals (age-related macular degeneration). These conditions need specialised clinicians to interpret multimodal retinal imaging, with diagnosis and intervention potentially delayed. With an increasing and ageing population, this is becoming a global health priority. One solution is the development of artificial intelligence (AI) software to facilitate rapid data processing. Herein, we review research offering decision support for the diagnosis, classification, monitoring, and treatment of retinal disease using AI. We have prioritised diabetic retinopathy, age-related macular degeneration, inherited retinal disease, and retinopathy of prematurity. There is cautious optimism that these algorithms will be integrated into routine clinical practice to facilitate access to vision-saving treatments, improve efficiency of healthcare systems, and assist clinicians in processing the ever-increasing volume of multimodal data, thereby also liberating time for doctor-patient interaction and co-development of personalised management plans.

Color Vision in Blue Cone Monochromacy: Outcome Measures for a Clinical Trial

Purpose

Blue cone monochromacy (BCM) is an X-linked retinopathy due to mutations in the OPN1LW/OPN1MW gene cluster. Symptoms include reduced visual acuity and disturbed color vision. We studied BCM color vision to determine outcome measures for future clinical trials.

Methods

Patients with BCM and normal-vision participants were examined with Farnsworth-Munsell (FM) arrangement tests and the Color Assessment and Diagnosis (CAD) test. A retrospective case series in 36 patients with BCM (ages 6-70) was performed with the FM D-15 test. A subset of six patients also had Roth-28 Hue and CAD tests.

Results

All patients with BCM had abnormal results for D-15, Roth-28, and CAD tests. With D-15, there was protan-deutan confusion and no bimodal tendency. Roth-28 results reinforced that finding. There was symmetry in color vision metrics between the two eyes and coherence between sessions with the arrangement tests and CAD. Severe abnormalities in red-green sensitivity with CAD were expected. Unexpected were different levels of yellow-blue results with two patterns of abnormal thresholds: moderate elevation in two younger patients and severe elevation in four patients ≥35 years. Coefficients of repeatability and intersession means were tabulated for all test modalities.

Conclusions

Given understanding of advantages, disadvantages, and complexities of interpretation of results, both an arrangement test and CAD should be useful monitors of color vision through a clinical trial in BCM.

Translational Relevance

Our pilot studies in BCM of arrangement and CAD tests indicated both were clinically feasible and interpretable in the context of this cone gene disease.

Deep learning: applications in retinal and optic nerve diseases

Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software to develop its own set of rules (i.e. learn) by adjusting the parameters inside the model (network) during a training process in order to complete the task on its own. One major limitation of traditional programming is that, with complex tasks, it may require an extensive set of rules to accurately complete the assignment. Additionally, traditional programming can be susceptible to human bias from programmer experience. With the dramatic increase in the amount and the complexity of clinical data, DL has been utilised to automate data analysis and thus to assist clinicians in patient management. This review will present the latest advances in DL, for managing posterior eye diseases as well as DL-based solutions for patients with vision loss.

A Systematic Review of Artificial Intelligence Applications Used for Inherited Retinal Disease Management

Nowadays, Artificial Intelligence (AI) and its subfields, Machine Learning (ML) and Deep Learning (DL), are used for a variety of medical applications. It can help clinicians track the patient’s illness cycle, assist with diagnosis, and offer appropriate therapy alternatives. Each approach employed may address one or more AI problems, such as segmentation, prediction, recognition, classification, and regression. However, the amount of AI-featured research on Inherited Retinal Diseases (IRDs) is currently limited. Thus, this study aims to examine artificial intelligence approaches used in managing Inherited Retinal Disorders, from diagnosis to treatment. A total of 20,906 articles were identified using the Natural Language Processing (NLP) method from the IEEE Xplore, Springer, Elsevier, MDPI, and PubMed databases, and papers submitted from 2010 to 30 October 2021 are included in this systematic review. The resultant study demonstrates the AI approaches utilized on images from different IRD patient categories and the most utilized AI architectures and models with their imaging modalities, identifying the main benefits and challenges of using such methods.

AI-based structure-function correlation in age-related macular degeneration

Sensitive and robust outcome measures of retinal function are pivotal for clinical trials in age-related macular degeneration (AMD). A recent development is the implementation of artificial intelligence (AI) to infer results of psychophysical examinations based on findings derived from multimodal imaging. We conducted a review of the current literature referenced in PubMed and Web of Science among others with the keywords ‘artificial intelligence’ and ‘machine learning’ in combination with ‘perimetry’, ‘best-corrected visual acuity (BCVA)’, ‘retinal function’ and ‘age-related macular degeneration’. So far AI-based structure-function correlations have been applied to infer conventional visual field, fundus-controlled perimetry, and electroretinography data, as well as BCVA, and patient-reported outcome measures (PROM). In neovascular AMD, inference of BCVA (hereafter termed inferred BCVA) can estimate BCVA results with a root mean squared error of ~7–11 letters, which is comparable to the accuracy of actual visual acuity assessment. Further, AI-based structure-function correlation can successfully infer fundus-controlled perimetry (FCP) results both for mesopic as well as dark-adapted (DA) cyan and red testing (hereafter termed inferred sensitivity). Accuracy of inferred sensitivity can be augmented by adding short FCP examinations and reach mean absolute errors (MAE) of ~3–5 dB for mesopic, DA cyan and DA red testing. Inferred BCVA, and inferred retinal sensitivity, based on multimodal imaging, may be considered as a quasi-functional surrogate endpoint for future interventional clinical trials in the future.