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

[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.

One-Year Anti-VEGF Therapy Outcomes in Diabetic Macular Edema Based on Treatment Intensity: Data from the Fight Retinal Blindness! Registry

PURPOSE

To compare 1-year outcomes of eyes with diabetic macular edema (DME) treated in routine clinical practice based on the proportion of visits where intravitreal VEGF inhibitor injections were delivered.

DESIGN

Cohort study.

PARTICIPANTS

There were 2288 treatment-naive eyes with DME starting intravitreal VEGF inhibitor therapy from October 31, 2015 to October 31, 2021 from the Fight Retinal Blindness! international outcomes registry.

METHODS

Eyes were grouped according to the proportion of visits at which an injection was received, Group A with less than the median of 67% (n = 1172) versus Group B with greater than the median (n = 1116).

MAIN OUTCOME MEASURES

Mean visual acuity (VA) change after 12 months of treatment.

RESULTS

The mean (95% confidence interval [CI]) VA change after 12 months of treatment was 3.6 (2.8-4.4) letters for eyes in Group A versus 5.2 (4.4-5.9) letters for eyes in Group B (P = 0.005). The mean (95% CI) central subfield thickness (CST) change was -69 (-76 to -61) μm and -85 (-92 to -78) μm for eyes in Group A versus Group B, respectively (P = 0.002). A moderate positive correlation was observed between the number of injections received over 12 months of treatment and the change in VA (P < 0.001). Additionally, eyes that received more injections had a moderately greater CST reduction.

CONCLUSIONS

This registry analysis found that overall VA and anatomic outcomes tended to be better in DME eyes treated at a greater proportion of visits in the first year of intravitreal VEGF inhibitor therapy.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

From real world data to real world evidence to improve outcomes in neuro-ophthalmology

Real-world data (RWD) can be defined as all data generated during routine clinical care. This includes electronic health records, disease-specific registries, imaging databanks, and data linkage to administrative databases. In the field of neuro-ophthalmology, the intersection of RWD and clinical practice offers unprecedented opportunities to understand and treat rare diseases. However, translating RWD into real-world evidence (RWE) poses several challenges, including data quality, legal and ethical considerations, and sustainability of data sources. This review explores existing RWD sources in neuro-ophthalmology, such as patient registries and electronic health records, and discusses the challenges of data collection and standardisation. We focus on research questions that need to be answered in neuro-ophthalmology and provide an update on RWE generated from various RWD sources. We review and propose solutions to some of the key barriers that can limit translation of a collection of data into impactful clinical evidence. Careful data selection, management, analysis, and interpretation are critical to generate meaningful conclusions.

Smart Devices in Optometry: Current and Future Perspectives to Clinical Optometry

There is a huge unmet need for eye care with more than a hundred million people living without basic eye care services and facilities. There is an exigency to deploy adequate resources in terms of manpower and equipment to address this. The usage of smart devices in optometry and eye care practice has been gaining momentum for last half a decade, due to the COVID-19 pandemic and technological advancements in telemedicine. These smart devices will help facilitate remote monitoring of important visual functions, ocular signs and symptoms, thus providing better eye care services and facilities and promoting outreach services. Smart devices in optometry exist in the form of gadgets that can be worn in the wrist, and spectacle-mounted or head-mounted devices. On the other hand, with the ubiquitous nature of smartphones, a large number of smartphone applications have been developed and tested for advanced optometry and primary eye care practice, which may potentially reduce the burden of inadequate resources and the unmet need for eye care. This article aims to give an overview of the current trends and future perspectives on the application of such smart devices in optometric practice.

Artificial intelligence for retinal diseases

PURPOSE

To discuss the worldwide applications and potential impact of artificial intelligence (AI) for the diagnosis, management and analysis of treatment outcomes of common retinal diseases.

METHODS

We performed an online literature review, using PubMed Central (PMC), of AI applications to evaluate and manage retinal diseases. Search terms included AI for screening, diagnosis, monitoring, management, and treatment outcomes for age-related macular degeneration (AMD), diabetic retinopathy (DR), retinal surgery, retinal vascular disease, retinopathy of prematurity (ROP) and sickle cell retinopathy (SCR). Additional search terms included AI and color fundus photographs, optical coherence tomography (OCT), and OCT angiography (OCTA). We included original research articles and review articles.

RESULTS

Research studies have investigated and shown the utility of AI for screening for diseases such as DR, AMD, ROP, and SCR. Research studies using validated and labeled datasets confirmed AI algorithms could predict disease progression and response to treatment. Studies showed AI facilitated rapid and quantitative interpretation of retinal biomarkers seen on OCT and OCTA imaging. Research articles suggest AI may be useful for planning and performing robotic surgery. Studies suggest AI holds the potential to help lessen the impact of socioeconomic disparities on the outcomes of retinal diseases.

CONCLUSIONS

AI applications for retinal diseases can assist the clinician, not only by disease screening and monitoring for disease recurrence but also in quantitative analysis of treatment outcomes and prediction of treatment response. The public health impact on the prevention of blindness from DR, AMD, and other retinal vascular diseases remains to be determined.

Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema

BACKGROUND

Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges.

MAIN TEXT

This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications.

CONCLUSION

AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.

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.

AI-based support for optical coherence tomography in age-related macular degeneration

Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.

Home OCT Imaging for Newly Diagnosed Neovascular Age-Related Macular Degeneration: A Feasibility Study

OBJECTIVE

To assess the feasibility of daily Home OCT imaging among patients with neovascular age-related macular degeneration (nAMD).

DESIGN

Prospective observational study.

PARTICIPANTS

Participants with ≥ 1 eye with previously untreated nAMD and visual acuity 20/20 to 20/320.

METHODS

Participants meeting the ocular eligibility criteria were considered for enrollment; those who provided consent received a Notal Vision Home OCT device. Participants were instructed to scan both eyes daily. Retina specialists managed treatment according to their standard practice, without access to the Home OCT data. The presence of fluid detected by a reading center (RC) from in-office OCT scans was compared with fluid volumes measured by the Notal OCT Analyzer (NOA) on Home OCT images.

MAIN OUTCOME MEASURES

Proportion of participants meeting ocular eligibility criteria who participated in daily scanning, frequency and duration of scanning, proportion of scans eligible for fluid quantification, participant experience with the device, agreement between the RC and NOA fluid determinations, and characteristics of fluid dynamics.

RESULTS

Among 40 participants meeting ocular eligibility criteria, 14 (35%) initiated self-scanning. Planned travel (n = 7, 17.5%) and patient-reported inadequate cell reception for the upload of images (n = 5, 12.5%) were the most frequent reasons for not participating. Considering scans of the study eye only, the mean (standard deviation) was 6.3 (0.6) for weekly scanning frequency and 47 (17) seconds for scan duration per eye. Among 2304 scans, 86.5% were eligible for fluid quantification. All participants agreed that scanning became easier over time, and only 1 did not want to continue daily scanning. For 35 scan pairs judged as having fluid by in-office OCT, the NOA detected fluid on 31 scans (89%). For 14 scan pairs judged as having no fluid on in-office OCT, the NOA did not detect fluid on 10 scans (71%). Daily fluid patterns after treatment initiation varied considerably between patients.

CONCLUSIONS

For patients with nAMD who initiated home scanning, frequency and quality of scanning and accuracy of fluid detection were sufficient to assess the monitoring of fluid at home. Accommodations for travel and Wi-Fi connectivity could improve uptake of the Home OCT device.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Barriers to care in neovascular age-related macular degeneration: Current understanding, developments, and future directions

Neovascular age-related macular degeneration is the advanced and irreversible stage of age-related macular degeneration, the leading cause of severe vision loss in older adults. While anti-vascular endothelial growth factor injections have been shown to preserve or improve vision quality in eyes with neovascular age-related macular degeneration, the treatment regimen can be demanding of patients and caregivers, leading to lower rates of adherence. Therefore, it is crucial that disparities and obstacles in neovascular age-related macular degeneration care are identified to improve access to treatment. Review of the current literature revealed 7 major categories of barriers: travel burden, psychological barriers, financial burden and socioeconomic status, treatment regimen, other comorbidities, provider-level barriers, and system-level barriers. We provide an overview of the major barriers to neovascular age-related macular degeneration care that have been reported, as well as gaps in research that need to be investigated further.

Optical coherence tomography in the management of diabetic macular oedema

Diabetic macular oedema (DMO) is the major cause of visual impairment in people with diabetes. Optical coherence tomography (OCT) is now the most widely used modality to assess presence and severity of DMO. DMO is currently broadly classified based on the involvement to the central 1 mm of the macula into non-centre or centre involved DMO (CI-DMO) and DMO can occur with or without visual acuity (VA) loss. This classification forms the basis of management strategies of DMO. Despite years of research on quantitative and qualitative DMO related features assessed by OCT, these do not fully inform physicians of the prognosis and severity of DMO relative to visual function. Having said that, recent research on novel OCT biomarkers development and re-defined classification of DMO show better correlation with visual function and treatment response. This review summarises the current evidence of the association of OCT biomarkers in DMO management and its potential clinical importance in predicting VA and anatomical treatment response. The review also discusses some future directions in this field, such as the use of artificial intelligence to quantify and monitor OCT biomarkers and retinal fluid and identify phenotypes of DMO, and the need for standardisation and classification of OCT biomarkers to use in future clinical trials and clinical practice settings as prognostic markers and secondary treatment outcome measures in the management of DMO.

Remote Monitoring of Visual Function in Patients with Maculopathy: The Aphelion Study

INTRODUCTION

Remote monitoring of vision, using tools such as the shape discrimination hyperacuity (SDH) test, can detect disease activity in patients with maculopathy. We determined the in-clinic accuracy and repeatability of three myVisionTrack expanded version (mVTx) tests for self-testing of visual acuity (VA) and contrast sensitivity.

METHODS

Aphelion, a single-arm, prospective study conducted at two sites in the USA, included adults with any maculopathy and a baseline VA of 0.7 log of minimum angle of resolution (logMAR) (Snellen 20/100) or better. Participants completed the mVTx tests (tumbling E, Landolt C, contrast sensitivity, and SDH) and standard clinical tests (near and distance Early Treatment Diabetic Retinopathy Study [ETDRS] charts and the Pelli-Robson contrast sensitivity chart). Test-retest repeatability and agreement between the mVTx tests and the corresponding clinical test were assessed by Bland-Altman analyses. Participants also completed a usability survey.

RESULTS

The mean age of the 122 participants was 67 years. The most common diagnosis was age-related macular degeneration (42% of patients). The tumbling E test had a test-retest 95% limit of agreement (LoA) of ± 0.18 logMAR; the Landolt C test, ± 0.23 logMAR; the SDH test, ± 0.24 logMAR; and the contrast sensitivity test, ± 0.32 log contrast threshold (logCT). Compared with the distance ETDRS chart, the LoA was ± 0.35 logMAR for the tumbling E test (mean difference, - 0.07 logMAR) and ± 0.39 logMAR for the Landolt C test (mean difference, 0.03 logMAR). For the contrast sensitivity test, the LoA compared with the Pelli-Robson chart was ± 0.30 logCT (mean difference, - 0.25 logCT). Most participants (85%) reported that they learned the tests quickly. The tumbling E test scored the highest on ease of use.

CONCLUSION

The mVTx tests of VA are accurate and repeatable, supporting their potential use alongside the SDH test to detect disease progression remotely between clinic visits.

Teleophthalmology and retina: a review of current tools, pathways and services

Telemedicine, the use of telecommunication and information technology to deliver healthcare remotely, has evolved beyond recognition since its inception in the 1970s. Advances in telecommunication infrastructure, the advent of the Internet, exponential growth in computing power and associated computer-aided diagnosis, and medical imaging developments have created an environment where telemedicine is more accessible and capable than ever before, particularly in the field of ophthalmology. Ever-increasing global demand for ophthalmic services due to population growth and ageing together with insufficient supply of ophthalmologists requires new models of healthcare provision integrating telemedicine to meet present day challenges, with the recent COVID-19 pandemic providing the catalyst for the widespread adoption and acceptance of teleophthalmology. In this review we discuss the history, present and future application of telemedicine within the field of ophthalmology, and specifically retinal disease. We consider the strengths and limitations of teleophthalmology, its role in screening, community and hospital management of retinal disease, patient and clinician attitudes, and barriers to its adoption.

Multimodal imaging and deep learning in geographic atrophy secondary to age-related macular degeneration

Geographic atrophy (GA) secondary to age-related macular degeneration is among the most common causes of irreversible vision loss in industrialized countries. Recently, two therapies have been approved by the US FDA. However, given the nature of their treatment effect, which primarily involves a relative decrease in disease progression, discerning the individual treatment response at the individual level may not be readily apparent. Thus, clinical decision-making may have to rely on the quantification of the slope of GA progression before and during treatment. A panel of imaging modalities and artificial intelligence (AI)-based algorithms are available for such quantifications. This article aims to provide a comprehensive overview of the fundamentals of GA imaging, the procedures for diagnosis and classification using these images, and the cutting-edge role of AI algorithms in automatically deriving diagnostic and prognostic insights from imaging data.

Home vision monitoring in patients with maculopathy: current and future options for digital technologies

Patients with macular pathology, including that caused by age-related macular degeneration and diabetic macular oedema, must attend frequent in-clinic monitoring appointments to detect onset of disease activity requiring treatment and to monitor progression of existing disease. In-person clinical monitoring places a significant burden on patients, caregivers and healthcare systems and is limited in that it only provides clinicians with a snapshot of the patient's disease status. The advent of remote monitoring technologies offers the potential for patients to test their own retinal health at home in collaboration with clinicians, reducing the need for in-clinic appointments. In this review we discuss visual function tests, both existing and novel, that have the potential for remote use and consider their suitability for discriminating the presence of disease and progression of disease. We then review the clinical evidence supporting the use of mobile applications for monitoring of visual function from clinical development through to validation studies and real-world implementation. This review identified seven app-based visual function tests: four that have already received some form of regulatory clearance and three under development. The evidence included in this review shows that remote monitoring offers great potential for patients with macular pathology to monitor their condition from home, reducing the need for burdensome clinic visits and expanding clinicians' understanding of patients' retinal health beyond traditional clinical monitoring. In order to instil confidence in the use of remote monitoring in both patients and clinicians further longitudinal real-world studies are now warranted.

The impact of artificial intelligence on retinal disease management: Vision Academy retinal expert consensus

PURPOSE OF REVIEW

The aim of this review is to define the "state-of-the-art" in artificial intelligence (AI)-enabled devices that support the management of retinal conditions and to provide Vision Academy recommendations on the topic.

RECENT FINDINGS

Most of the AI models described in the literature have not been approved for disease management purposes by regulatory authorities. These new technologies are promising as they may be able to provide personalized treatments as well as a personalized risk score for various retinal diseases. However, several issues still need to be addressed, such as the lack of a common regulatory pathway and a lack of clarity regarding the applicability of AI-enabled medical devices in different populations.

SUMMARY

It is likely that current clinical practice will need to change following the application of AI-enabled medical devices. These devices are likely to have an impact on the management of retinal disease. However, a consensus needs to be reached to ensure they are safe and effective for the overall population.

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.

Current status and practical considerations of artificial intelligence use in screening and diagnosing retinal diseases: Vision Academy retinal expert consensus

PURPOSE OF REVIEW

The application of artificial intelligence (AI) technologies in screening and diagnosing retinal diseases may play an important role in telemedicine and has potential to shape modern healthcare ecosystems, including within ophthalmology.

RECENT FINDINGS

In this article, we examine the latest publications relevant to AI in retinal disease and discuss the currently available algorithms. We summarize four key requirements underlining the successful application of AI algorithms in real-world practice: processing massive data; practicability of an AI model in ophthalmology; policy compliance and the regulatory environment; and balancing profit and cost when developing and maintaining AI models.

SUMMARY

The Vision Academy recognizes the advantages and disadvantages of AI-based technologies and gives insightful recommendations for future directions.

Symmetry of Macular Fundus Features in Age-Related Macular Degeneration

PURPOSE

The symmetry of major macular fundus features in both eyes of the same patient with age-related macular degeneration (AMD) was investigated using swept-source(SS)-OCT.

DESIGN

Retrospective review of a prospective study.

PARTICIPANTS

Patients with AMD.

METHODS

Grading was performed on the first SS-OCT images obtained on the patients. Two graders diagnosed the presence of drusen, geographic atrophy (GA), and exudative AMD (eAMD) in each eye. Medical records were reviewed to assess prior exudation. To assess symmetry, 1 eye of each patient was randomly selected as the index eye and compared with the fellow eye. The kappa statistic (κ) was used to assess the symmetry of diagnosis. The intraclass correlation coefficient (ICC) was used to assess the symmetry of drusen area and volume.

MAIN OUTCOME MEASURES

Interocular symmetry of the AMD stages: drusen, GA, and eAMD.

RESULTS

A total of 1310 patients with AMD were included. The average age was 78 years (range, 50-102; 60% women). Of the 1310 subjects, 54% (701) presented with symmetric disease: 20% with bilateral drusen, 11% with bilateral GA, and 22% with bilateral eAMD. Only 0.5% of the subjects had both GA and eAMD in both eyes. Of the randomly selected index eyes, 825 (47%) were right eyes. Overall, limited interocular agreement was observed between the index and fellow eyes (54%; κ = 0.29). Kappa coefficients were poor (< 0.4) for index eyes diagnosed with drusen (κ = 0.27), eAMD (κ = 0.17), and mixed disease (κ = 0.03). There was moderate agreement between the index and fellow eyes for GA (κ = 0.50). Of the 265 patients with bilateral drusen, the symmetry of drusen area measurements had moderate ICC values of 0.70, 0.71, and 0.70 in the 3- and 5-mm diameter foveal-centered circles and in the total scan area, respectively. The ICC values for the drusen volumes were 0.65, 0.66, and 0.64, respectively.

CONCLUSIONS

Interocular symmetry was poor for eyes with drusen, eAMD, and mixed disease, but moderate for GA. Although the diagnosis of drusen was not very symmetric between eyes, when present in both eyes, the drusen area and volume measurements were moderately symmetric.

FINANCIAL DISCLOSURE(S)

Proprietary or commercial disclosure may be found after the references.

Towards a population pharmacokinetic/pharmacodynamic model of anti-VEGF therapy in patients with age-related macular degeneration

PURPOSE

To develop a population pharmacokinetic/pharmacodynamic model (popPKPD) of intravitreal bevacizumab treatment for neovascular age-related macular degeneration (nAMD) patients to learn about the PK/PD relationship and utilise it for dosing regimen decisions on future nAMD patients.

METHODS

The Greater Manchester Avastin for Neovascularisation (GMAN) randomised clinical trial data was retrospectively utilised, and the best-corrected visual acuity (BCVA) and central macular retinal thickness (CRT, measured by optical coherence tomography) were the PD inputs to the model. Using the nonlinear mixed-effects method, the best PKPD structural model was investigated, and the clinical significance of the two different dosing treatment regimens (as-needed versus routine) was evaluated.

RESULTS

A structural model to describe the change of BCVA from the baseline of nAMD patients was successfully obtained based on the turnover PD model concept (drug stimulates the "visual acuity response production"). The popPKPD model and simulation indicate that the routine regimen protocol improves patient visual outcome compared to the as-needed protocol. For the change in CRT, the turnover structural PKPD model was too demanding to fit to the given clinical data.

CONCLUSIONS

This is the first popPKPD attempt in nAMD treatment that shows the potential of this strategy to understand/inform the dosing regimen. Clinical trials with richer PD data will provide the means to build more robust models.

Prognostic Optical Coherence Tomography Biomarkers in Neovascular Age-Related Macular Degeneration

Optical coherence tomography has revolutionized the diagnosis and management of neovascular age-related macular degeneration. OCT-derived biomarkers have the potential to further guide therapeutic advancements with anti-vascular endothelial growth factor; however, the clinical convergence between these two tools remains suboptimal. Therefore, the aim of this review of literature was to examine the current data on OCT biomarkers and their prognostic value. Thirteen biomarkers were analyzed, and retinal fluid had the strongest-reported impact on clinical outcomes, including visual acuity, clinic visits, and anti-VEGF treatment regimens. In particular, intra-retinal fluid was shown to be associated with poor visual outcomes. Consistencies in the literature with regard to these OCT prognostic biomarkers can lead to patient-specific clinical decision making, such as early-initiated treatment and proactive monitoring. An integrated analysis of all OCT components in combination with new efforts toward automated analysis with artificial intelligence has the potential to further improve the role of OCT in nAMD therapy.

Multitask Learning for Activity Detection in Neovascular Age-Related Macular Degeneration

Purpose

The purpose of this study was to provide a comparison of performance and explainability of a multitask convolutional deep neuronal network to single-task networks for activity detection in neovascular age-related macular degeneration (nAMD).

Methods

From 70 patients (46 women and 24 men) who attended the University Eye Hospital Tübingen, 3762 optical coherence tomography B-scans (right eye = 2011 and left eye = 1751) were acquired with Heidelberg Spectralis, Heidelberg, Germany. B-scans were graded by a retina specialist and an ophthalmology resident, and then used to develop a multitask deep learning model to predict disease activity in neovascular age-related macular degeneration along with the presence of sub- and intraretinal fluid. We used performance metrics for comparison to single-task networks and visualized the deep neural network (DNN)-based decision with t-distributed stochastic neighbor embedding and clinically validated saliency mapping techniques.

Results

The multitask model surpassed single-task networks in accuracy for activity detection (94.2% vs. 91.2%). The area under the curve of the receiver operating curve was 0.984 for the multitask model versus 0.974 for the single-task model. Furthermore, compared to single-task networks, visualizations via t-distributed stochastic neighbor embedding and saliency maps highlighted that multitask networks' decisions for activity detection in neovascular age-related macular degeneration were highly consistent with the presence of both sub- and intraretinal fluid.

Conclusions

Multitask learning increases the performance of neuronal networks for predicting disease activity, while providing clinicians with an easily accessible decision control, which resembles human reasoning.

Translational Relevance

By improving nAMD activity detection performance and transparency of automated decisions, multitask DNNs can support the translation of machine learning research into clinical decision support systems for nAMD activity detection.

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.

Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions

BACKGROUND

Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review.

MATERIALS AND METHODS

We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease.

RESULTS

Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria.

CONCLUSION

In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.

Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans

Aims

Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications.

Methods

The app comprises a convolutional neural network-Vision Transformer (CNN-ViT)-based segmentation deep learning (DL) network, trained on a small dataset of 100 training images (augmented to 992 images) from the Singapore Epidemiology of Eye Diseases (SEED) study, together with a CNN-based classification network trained on 8497 images, that can detect fluid vs. non-fluid optical coherence tomography (OCT) scans. Both networks are validated on external datasets.

Results

Internal testing for our segmentation network produced an IoU score of 83.0% (95% CI = 76.7-89.3%) and a DICE score of 90.4% (86.3-94.4%); for external testing, we obtained an IoU score of 66.7% (63.5-70.0%) and a DICE score of 78.7% (76.0-81.4%). Internal testing of our classification network produced an area under the receiver operating characteristics curve (AUC) of 99.18%, and a Youden index threshold of 0.3806; for external testing, we obtained an AUC of 94.55%, and an accuracy of 94.98% and an F1 score of 85.73% with Youden index.

Conclusion

We have developed an AI-based app with an alternative transformer-based segmentation algorithm that could potentially be applied in the clinic with a PPPM approach for serial monitoring, and could allow for the generation of retrospective data to research into the varied use of treatments for AMD and DR. The modular system of our app can be scaled to add more iterative features based on user feedback for more efficient monitoring. Further study and scaling up of the algorithm dataset could potentially boost its usability in a real-world clinical setting.

Supplementary information

The online version contains supplementary material available at 10.1007/s13167-022-00301-5.

Artificial intelligence at the national eye institute

PURPOSE OF REVIEW

This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Institutes of Health (NIH) and the National Eye Institute (NEI) and calls attention to activities and goals defined in the NEI Strategic Plan as well as opportunities for future activities and breakthroughs in ophthalmology.

RECENT FINDINGS

Ophthalmology is at the forefront of artificial intelligence-based innovations in biomedical research that may lead to improvement in early detection and surveillance of ocular disease, prediction of progression, and improved quality of life. Technological advances have ushered in an era where unprecedented amounts of information can be linked that enable scientific discovery. However, there remains an unmet need to collect, harmonize, and share data in a machine actionable manner. Similarly, there is a need to ensure that efforts promote health and research equity by expanding diversity in the data and workforce.

SUMMARY

The NIH/NEI has supported the development artificial intelligence-based innovations to advance biomedical research. The NIH/NEI has defined activities to achieve these goals in the NIH Strategic Plan for Data Science and the NEI Strategic Plan and have spearheaded initiatives to facilitate research in these areas.

Advances in Optical Coherence Tomography Imaging Technology and Techniques for Choroidal and Retinal Disorders

Optical coherence tomography (OCT) imaging has played a pivotal role in the field of retina. This light-based, non-invasive imaging modality provides high-quality, cross-sectional analysis of the retina and has revolutionized the diagnosis and management of retinal and choroidal diseases. Since its introduction in the early 1990s, OCT technology has continued to advance to provide quicker acquisition times and higher resolution. In this manuscript, we discuss some of the most recent advances in OCT technology and techniques for choroidal and retinal diseases. The emerging innovations discussed include wide-field OCT, adaptive optics OCT, polarization sensitive OCT, full-field OCT, hand-held OCT, intraoperative OCT, at-home OCT, and more. The applications of these rising OCT systems and techniques will allow for a closer monitoring of chorioretinal diseases and treatment response, more robust analysis in basic science research, and further insights into surgical management. In addition, these innovations to optimize visualization of the choroid and retina offer a promising future for advancing our understanding of the pathophysiology of chorioretinal diseases.