Real-World Performance of a Self-Operated Home Monitoring System for Early Detection of Neovascular Age-Related Macular Degeneration

Oculomics: Current concepts and evidence

The eye provides novel insights into general health, as well as pathogenesis and development of systemic diseases. In the past decade, growing evidence has demonstrated that the eye's structure and function mirror multiple systemic health conditions, especially in cardiovascular diseases, neurodegenerative disorders, and kidney impairments. This has given rise to the field of oculomics-the application of ophthalmic biomarkers to understand mechanisms, detect and predict disease. The development of this field has been accelerated by three major advances: 1) the availability and widespread clinical adoption of high-resolution and non-invasive ophthalmic imaging ("hardware"); 2) the availability of large studies to interrogate associations ("big data"); 3) the development of novel analytical methods, including artificial intelligence (AI) ("software"). Oculomics offers an opportunity to enhance our understanding of the interplay between the eye and the body, while supporting development of innovative diagnostic, prognostic, and therapeutic tools. These advances have been further accelerated by developments in AI, coupled with large-scale linkage datasets linking ocular imaging data with systemic health data. Oculomics also enables the detection, screening, diagnosis, and monitoring of many systemic health conditions. Furthermore, oculomics with AI allows prediction of the risk of systemic diseases, enabling risk stratification, opening up new avenues for prevention or individualized risk prediction and prevention, facilitating personalized medicine. In this review, we summarise current concepts and evidence in the field of oculomics, highlighting the progress that has been made, remaining challenges, and the opportunities for future research.

[Digital remote monitoring of chronic retinal conditions-A clinical future tool? : Remote monitoring of chronic retinal conditions]

BACKGROUND

In view of the predicted increase in incidence and prevalence of chronic retinal diseases and undersupply of care in the population, telemedicine could contribute to reducing access barriers to healthcare and improving the results of treatment.

OBJECTIVE

A literature review on remote monitoring of chronic retinal diseases was carried out.

MATERIAL AND METHODS

The medical literature was searched for publications on remote monitoring of chronic retinal diseases. The results were compiled in a narrative overview.

RESULTS

The four main topics in the literature are: validation studies, implementation strategies, acceptance/target group analyses and health economic analyses. Remote monitoring systems are based on visual function tests, imaging or patient reports and have been particularly investigated in age-related macular degeneration (AMD) and diabetic eye disease (DED). Studies indicate positive effects regarding an optimization of clinical care and a favorable safety profile but randomized controlled trials are lacking for the majority of monitoring tools.

CONCLUSION

Remote monitoring could complement existing care structures for patients with chronic retinal diseases, especially AMD and DED. Promising systems are based on hyperacuity or optical coherence tomography, while patient-reported data are not commonly used; however, there is currently insufficient evidence justifying the use of remote monitoring systems in chronic retinal diseases in Europe and more research on the validation of remote monitoring systems is needed.

Home Optical Coherence Tomography Monitoring for Neovascular Age-Related Macular Degeneration: Transformative Technology or Cool Toy?

The pending introduction of home-based optical coherence tomography (OCT) in managing neovascular age-related macular degeneration (nAMD) has sparked interesting debates. Advocates assert that home-based OCT will revolutionize care of patients with nAMD, while skeptics question its real-world viability and point out its potential drawbacks. This article delves into the dichotomy, presenting the "pro" argument highlighting the transformative potential of home OCT and the "con" perspective, which scrutinizes the limitations and challenges to adapting the technology to the real-world setting. By exploring both sides of the discourse, we aim to address the promises and complexities surrounding the role of home OCT in the management of nAMD.

Save our Sight (SOS): a collective call-to-action for enhanced retinal care across health systems in high income countries

With a growing aging population, the prevalence of age-related eye disease and associated eye care is expected to increase. The anticipated growth in demand, coupled with recent medical advances that have transformed eye care for people living with retinal diseases, particularly neovascular age-related macular degeneration (nAMD) and diabetic eye disease, has presented an opportunity for health systems to proactively manage the expected burden of these diseases. To do so, we must take collective action to address existing and anticipated capacity limitations by designing and implementing sustainable strategies that enable health systems to provide an optimal standard of care. Sufficient capacity will enable us to streamline and personalize the patient experience, reduce treatment burden, enable more equitable access to care and ensure optimal health outcomes. Through a multi-modal approach that gathered unbiased perspectives from clinical experts and patient advocates from eight high-income countries, substantiated perspectives with evidence from the published literature and validated findings with the broader eye care community, we have exposed capacity challenges that are motivating the community to take action and advocate for change. Herein, we propose a collective call-to-action for the future management of retinal diseases and potential strategies to achieve better health outcomes for individuals at-risk of, or living with, retinal disease.

Artificial intelligence for home monitoring devices

PURPOSE OF REVIEW

Home monitoring in ophthalmology is appropriate for disease stages requiring frequent monitoring or rapid intervention, for example, neovascular age-related macular degeneration (AMD) and glaucoma, where the balance between frequent hospital attendance versus risk of late detection is a constant challenge. Artificial intelligence approaches are well suited to address some challenges of home monitoring.

RECENT FINDINGS

Ophthalmic data collected at home have included functional (e.g. perimetry), biometric (e.g. intraocular pressure), and imaging [e.g. optical coherence tomography (OCT)] data. Potential advantages include early detection/intervention, convenience, cost, and visual outcomes. Artificial intelligence can assist with home monitoring workflows by handling large data volumes from frequent testing, compensating for test quality, and extracting useful metrics from complex data. Important use cases include machine learning applied to hyperacuity self-testing for detecting neovascular AMD and deep learning applied to OCT data for quantifying retinal fluid.

SUMMARY

Home monitoring of health conditions is useful for chronic diseases requiring rapid intervention or frequent data sampling to decrease risk of irreversible vision loss. Artificial intelligence may facilitate accurate, frequent, large-scale home monitoring, if algorithms are integrated safely into workflows. Clinical trials and economic evaluations are important to demonstrate the value of artificial intelligence-based home monitoring, towards improved visual outcomes.

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

Diabetic macular edema (DME) is the primary cause of central vision impairment in patients with diabetes and the leading cause of preventable blindness in working-age people. With the advent of optical coherence tomography and antivascular endothelial growth factor (anti-VEGF) therapy, the diagnosis, evaluation, and treatment of DME were greatly revolutionized in the last decade. However, there is tremendous heterogeneity among DME patients, and 30%-50% of DME patients do not respond well to anti-VEGF agents. In addition, there is no evidence-based and universally accepted administration regimen. The identification of DME patients not responding to anti-VEGF agents and the determination of the optimal administration interval are the 2 major challenges of DME, which are difficult to achieve with the coarse granularity of conventional health care modality. Therefore, more and more retina specialists have pointed out the necessity of introducing precision medicine into the management of DME and have conducted related studies in recent years. One of the most frontier methods is the targeted extraction of individualized disease features from optical coherence tomography images based on artificial intelligence technology, which provides precise evaluation and risk classification of DME. This review aims to provide an overview of the progress of artificial intelligence-enabled precision medicine in automated screening, precise evaluation, prognosis prediction, and follow-up monitoring of DME. Further, the challenges ahead of real-world applications and the future development of precision medicine in DME will be discussed.

Mechanistic Insight into Age-Related Macular Degeneration (AMD): Anatomy, Epidemiology, Genetics, Pathogenesis, Prevention, Implications, and Treatment Strategies to Pace AMD Management

One of the most complicated eye disorders is age-related macular degeneration (AMD) which is the leading cause of irremediable blindness all over the world in the elderly. AMD is classified as early stage to late stage (advanced AMD), in which this stage is divided into the exudative or neovascular form (wet AMD) and the nonexudative or atrophic form (dry AMD). Clinically, AMD primarily influences the central area of retina known as the macula. Importantly, the wet form is generally associated with more severe vision loss. AMD has a systemic component, where many factors, like aging, genetic, environment, autoimmune and non-autoimmune disorders are associated with this disease. Additionally, healthy lifestyles, regular exercise, maintaining a normal lipid profile and weight are crucial to decreasing the risk of AMD. Furthermore, therapeutic strategies for limiting AMD should encompass a variety of factors to avoid and improve drug interventions, and also need to take into account personalized genetic information. In conclusion, with the development of technology and research progress, visual impairment and legal blindness from AMD have been substantially reduced in incidence. This review article is focused on identifying and developing the knowledge about the association between genetics, and etiology with AMD. We hope that this review will encourage researchers and lecturers, open new discussions, and contribute to a better understanding of AMD that improves patients' visual acuity, and upgrades the quality of life of AMD patients.

Transforming ophthalmology in the digital century-new care models with added value for patients

Ophthalmology faces many challenges in providing effective and meaningful eye care to an ever-increasing group of people. Even health systems that have so far been able to cope with the quantitative patient increase, due to their funding and the availability of highly qualified professionals, and improvements in practice routine efficiency, will be pushed to their limits. Further pressure on care will also be caused by new active substances for the largest group of patients with AMD, the so-called dry form. Treatment availability for this so far untreated group will increase the volume of patients 2-3 times. Without the adaptation of the care structures, this quantitative and qualitative expansion in therapy will inevitably lead to an undersupply.There is increasing scientific evidence that significant efficiency gains in the care of chronic diseases can be achieved through better networking of stakeholders in the healthcare system and greater patient involvement. Digitalization can make an important contribution here. Many technological solutions have been developed in recent years and the time is now ready to exploit this potential. The exceptional setting during the SARS-CoV-2 pandemic has shown many that new technology is available safely, quickly, and effectively. The emergency has catalyzed innovation processes and shown for post-pandemic time after that we are equipped to tackle the challenges in ophthalmic healthcare - ultimately for the benefit of patients and society.

Age-related macular degeneration

Age-related macular degeneration is an increasingly important public health issue due to ageing populations and increased longevity. Age-related macular degeneration affects individuals older than 55 years and threatens high-acuity central vision required for important tasks such as reading, driving, and recognising faces. Advances in retinal imaging have identified biomarkers of progression to late age-related macular degeneration. New treatments for neovascular age-related macular degeneration offer potentially longer-lasting effects, and progress is being made towards a treatment for atrophic late age-related macular degeneration. An effective intervention to slow progression in the earlier stages of disease, or to prevent late age-related macular degeneration development remains elusive, and our understanding of underlying mechanistic pathways continues to evolve.

Performance of a Smart Device over 12-Months for Home Monitoring of Patients with Intermediate Age-Related Macular Degeneration

Background: To determine the 12-month compliance with and retention of home monitoring (HM) with Melbourne Rapid Fields (MRFh) for patients with intermediate age-related macular degeneration (iAMD) and compare visual acuity (VA) and retinal sensitivity (RS) results to clinical measures. Methods: Participants were recruited to a 12-month HM study with weekly testing of vision with MRFh. Inclusion criteria were a diagnosis of iAMD, understand English instructions, VA ≥ 20/40, and access to an iPad. Supervised in-clinic testing of high contrast VA (HVA, ETDRS), low-luminance VA (LLVA, ETDRS with ND2 filter), and RS (Macular Integrity Assessment, MAIA, and MRF in-clinic, MRFc) was conducted every 6-months. Results: A total of 54 participants (67 ± 6.8 years) were enrolled. Compliance to weekly HM was 61% and study retention at 12-months was 50% of those with uptake (n = 46). No difference was observed between MRFc and MRFh across all RS and VA outcomes (p > 0.05). MRFh RS was higher than MAIA (29.1 vs. 27.1 dB, p < 0.001). MRFh HVA was not different from ETDRS (p = 0.08), but LLVA was 9 letters better (81.5 vs. 72.4 letters, p < 0.001). Conclusions: Over 12-months, MRFh yields a moderate level of compliance with (61%) and retention (50%) of weekly testing. Further studies are required to assess the ability of MRFh to detect early progression to nAMD.

Automated large-scale prediction of exudative AMD progression using machine-read OCT biomarkers

Age-related Macular Degeneration (AMD) is a major cause of irreversible vision loss in individuals over 55 years old in the United States. One of the late-stage manifestations of AMD, and a major cause of vision loss, is the development of exudative macular neovascularization (MNV). Optical Coherence Tomography (OCT) is the gold standard to identify fluid at different levels within the retina. The presence of fluid is considered the hallmark to define the presence of disease activity. Anti-vascular growth factor (anti-VEGF) injections can be used to treat exudative MNV. However, given the limitations of anti-VEGF treatment, as burdensome need for frequent visits and repeated injections to sustain efficacy, limited durability of the treatment, poor or no response, there is a great interest in detecting early biomarkers associated with a higher risk for AMD progression to exudative forms in order to optimize the design of early intervention clinical trials. The annotation of structural biomarkers on optical coherence tomography (OCT) B-scans is a laborious, complex and time-consuming process, and discrepancies between human graders can introduce variability into this assessment. To address this issue, a deep-learning model (SLIVER-net) was proposed, which could identify AMD biomarkers on structural OCT volumes with high precision and without human supervision. However, the validation was performed on a small dataset, and the true predictive power of these detected biomarkers in the context of a large cohort has not been evaluated. In this retrospective cohort study, we perform the largest-scale validation of these biomarkers to date. We also assess how these features combined with other EHR data (demographics, comorbidities, etc) affect and/or improve the prediction performance relative to known factors. Our hypothesis is that these biomarkers can be identified by a machine learning algorithm without human supervision, in a way that they preserve their predictive nature. The way we test this hypothesis is by building several machine learning models utilizing these machine-read biomarkers and assessing their added predictive power. We found that not only can we show that the machine-read OCT B-scan biomarkers are predictive of AMD progression, we also observe that our proposed combined OCT and EHR data-based algorithm outperforms the state-of-the-art solution in clinically relevant metrics and provides actionable information which has the potential to improve patient care. In addition, it provides a framework for automated large-scale processing of OCT volumes, making it possible to analyze vast archives without human supervision.

Perspectives on the Home Monitoring of Macular Disease

Recent advancements in imaging technology have led to increasing interest in home monitoring of macular disease. The prevalence of macular disease is projected to rise considerably over time, leading to a significant burden on hospital services for age-related macular degeneration and diabetic macular edema. Home monitoring has the potential to augment conventional hospital assessment and so enable improved access to clinical care for low- and moderate-risk patients, while also allowing sensitive detection of early signs of disease that may require prompt intervention. Despite this, there are significant considerations before large-scale implementation could be possible. These are related to both the current availability of home monitoring technology and the logistical barriers to its widespread introduction. Access to home monitoring is also likely to be more challenging in lower-income communities and countries, with subsequent implications for health inequality that will need to be considered and addressed appropriately.

Prospective, Longitudinal Study: Daily Self-Imaging with Home OCT for Neovascular Age-Related Macular Degeneration

OBJECTIVE

To validate the performance of the Notal Vision Home OCT (NVHO) system for daily self-imaging at home and characterize the retinal fluid dynamics of patients with neovascular age-related macular degeneration (nAMD).

DESIGN

Prospective observational study.

SUBJECTS

Fifteen participants who had at least 1 eye with nAMD and underwent anti-VEGF treatments.

METHODS

The participants performed daily self-imaging at home using NVHO for 3 months. The scans were uploaded to the cloud, analyzed using the Notal OCT Analyzer (NOA), evaluated by human experts for fluid presence, and compared with in-office OCT scans.

MAIN OUTCOME MEASURES

Weekly self-scan rate, image quality, scan duration, agreement between NOA and human expert grading for fluid presence, agreement between NVHO and in-office OCT scans for fluid presence, central subfield thickness (CST) and retinal fluid volume, and the characteristics of fluid dynamics during the study and in response to treatments.

RESULTS

The mean weekly scan frequency was 5.7 ± 0.9 scans per week, and 93% of the scans were eligible for NOA analyses. The median scan time was 42 seconds. The NOA and human experts agreed on the fluid status in 83% of the scans, and discrepancies were limited to trace amounts of fluid. The NVHO scans analyzed using NOA and the in-office OCT scans graded by human experts agreed on the fluid status in 96% of the cases. The CST and retinal fluid volume measurements using the home OCT and in-office OCT scans demonstrated a Pearson correlation coefficient of r = 0.90 and r = 0.92, respectively. Novel parameters, such as retinal fluid volume and area under the curve (AUC) of retinal fluid volume, demonstrated wide variations in fluid exudation and fluid load over time among the patients. Parameters such as the rate of reduction in fluid volume in the first week after treatment and AUC between treatments captured the speed and duration of the response to anti-VEGF agents.

CONCLUSIONS

Daily home OCT imaging is feasible among patients with nAMD. It demonstrated good agreement with human expert grading for retinal fluid identification and excellent agreement with the in-clinic OCT scans. Home OCT allows for detailed graphical and mathematical analyses of retinal fluid volume trajectories, including novel parameters to inform clinical decision making.

From Data to Deployment: The Collaborative Community on Ophthalmic Imaging Roadmap for Artificial Intelligence in Age-Related Macular Degeneration

OBJECTIVE

Health care systems worldwide are challenged to provide adequate care for the 200 million individuals with age-related macular degeneration (AMD). Artificial intelligence (AI) has the potential to make a significant, positive impact on the diagnosis and management of patients with AMD; however, the development of effective AI devices for clinical care faces numerous considerations and challenges, a fact evidenced by a current absence of Food and Drug Administration (FDA)-approved AI devices for AMD.

PURPOSE

To delineate the state of AI for AMD, including current data, standards, achievements, and challenges.

METHODS

Members of the Collaborative Community on Ophthalmic Imaging Working Group for AI in AMD attended an inaugural meeting on September 7, 2020, to discuss the topic. Subsequently, they undertook a comprehensive review of the medical literature relevant to the topic. Members engaged in meetings and discussion through December 2021 to synthesize the information and arrive at a consensus.

RESULTS

Existing infrastructure for robust AI development for AMD includes several large, labeled data sets of color fundus photography and OCT images; however, image data often do not contain the metadata necessary for the development of reliable, valid, and generalizable models. Data sharing for AMD model development is made difficult by restrictions on data privacy and security, although potential solutions are under investigation. Computing resources may be adequate for current applications, but knowledge of machine learning development may be scarce in many clinical ophthalmology settings. Despite these challenges, researchers have produced promising AI models for AMD for screening, diagnosis, prediction, and monitoring. Future goals include defining benchmarks to facilitate regulatory authorization and subsequent clinical setting generalization.

CONCLUSIONS

Delivering an FDA-authorized, AI-based device for clinical care in AMD involves numerous considerations, including the identification of an appropriate clinical application; acquisition and development of a large, high-quality data set; development of the AI architecture; training and validation of the model; and functional interactions between the model output and clinical end user. The research efforts undertaken to date represent starting points for the medical devices that eventually will benefit providers, health care systems, and patients.

OCT and OCT Angiography Offer New Insights and Opportunities in Schizophrenia Research and Treatment

The human retina and retinal imaging technologies continue to increasingly gain the attention of schizophrenia researchers. With the same embryologic origin as the brain, the retina offers a window into neurovascular changes that may underlie disease. Recently, two technologies that have already revolutionized the field of ophthalmology, optical coherence tomography (OCT), and a functional extension of this, optical coherence tomography angiography (OCTA), have gained traction. Together, these non-invasive technologies allow for microscopic imaging of both structural and vascular features of the retina. With ease of use and no side effects, these devices are likely to prove powerful digital health tools in the study and treatment of schizophrenia. They may also prove key to discovering disease relevant biomarkers that underly neurodevelopmental and neurodegenerative aspects of conditions such as schizophrenia.

Age-Related Macular Degeneration: New Insights in Diagnosis, Treatment, and Prevention

Age-related macular degeneration (AMD) is an aging-related ocular disease that can be responsible for severe loss of visual acuity and loss of autonomy in patients [...]