Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment

[Complement inhibition treatment for geographic atrophy (GA): functional and morphological efficacy and relevant biomarkers in clinical practice]

The approval of complement inhibitory therapeutic agents for the treatment of geographic atrophy (GA) has highlighted the need for reliable and reproducible measurement of disease progression and therapeutic efficacy. Due to its availability and imaging characteristics optical coherence tomography (OCT) is the method of choice. Using OCT analysis based on artificial intelligence (AI), the therapeutic efficacy of pegcetacoplan was demonstrated at the levels of both the retinal pigment epithelium (RPE) and photoreceptors (PR). Cloud-based solutions that enable monitoring of GA are already available.

Imaging geographic atrophy: integrating structure and function to better understand the effects of new treatments

Geographic atrophy (GA) is an advanced and irreversible form of age-related macular degeneration (AMD). Chronic low grade inflammation is thought to act as an initiator of this degenerative process, resulting in loss of photoreceptors (PRs), retinal pigment epithelium (RPE) and the underlying choriocapillaris. This review examined the challenges of clinical trials to date which have sought to treat GA, with particular reference to the successful outcome of C3 complement inhibition. Currently, optical coherence tomography (OCT) seems to be the most suitable method to detect GA and monitor the effect of treatment. In addition, the merits of using novel anatomical endpoints in detecting GA expansion are discussed. Although best-corrected visual acuity is commonly used to monitor disease in GA, other tests to determine visual function are explored. Although not widely available, microperimetry enables quantification of retinal sensitivity (RS) and macular fixation behaviour related to fundus characteristics. There is a spatial correlation between OCT/fundus autofluorescence evaluation of PR damage outside the area of RPE loss and RS on microperimetry, showing important associations with visual function. Standardisation of testing by microperimetry is necessary to enable this modality to detect AMD progression. Artificial intelligence (AI) analysis has shown PR layers integrity precedes and exceeds GA loss. Loss of the ellipsoid zone has been recognised as a primary outcome parameter in therapeutic trials for GA. The integrity of the PR layers imaged by OCT at baseline has been shown to be an important prognostic indicator. AI has the potential to be invaluable in personalising care and justifying treatment intervention.

Deep Learning Approaches for Detecting of Nascent Geographic Atrophy in Age-Related Macular Degeneration

Purpose

Nascent geographic atrophy (nGA) refers to specific features seen on OCT B-scans, which are strongly associated with the future development of geographic atrophy (GA). This study sought to develop a deep learning model to screen OCT B-scans for nGA that warrant further manual review (an artificial intelligence [AI]-assisted approach), and to determine the extent of reduction in OCT B-scan load requiring manual review while maintaining near-perfect nGA detection performance.

Design

Development and evaluation of a deep learning model.

Participants

One thousand eight hundred and eighty four OCT volume scans (49 B-scans per volume) without neovascular age-related macular degeneration from 280 eyes of 140 participants with bilateral large drusen at baseline, seen at 6-monthly intervals up to a 36-month period (from which 40 eyes developed nGA).

Methods

OCT volume and B-scans were labeled for the presence of nGA. Their presence at the volume scan level provided the ground truth for training a deep learning model to identify OCT B-scans that potentially showed nGA requiring manual review. Using a threshold that provided a sensitivity of 0.99, the B-scans identified were assigned the ground truth label with the AI-assisted approach. The performance of this approach for detecting nGA across all visits, or at the visit of nGA onset, was evaluated using fivefold cross-validation.

Main Outcome Measures

Sensitivity for detecting nGA, and proportion of OCT B-scans requiring manual review.

Results

The AI-assisted approach (utilizing outputs from the deep learning model to guide manual review) had a sensitivity of 0.97 (95% confidence interval [CI] = 0.93-1.00) and 0.95 (95% CI = 0.87-1.00) for detecting nGA across all visits and at the visit of nGA onset, respectively, when requiring manual review of only 2.7% and 1.9% of selected OCT B-scans, respectively.

Conclusions

A deep learning model could be used to enable near-perfect detection of nGA onset while reducing the number of OCT B-scans requiring manual review by over 50-fold. This AI-assisted approach shows promise for substantially reducing the current burden of manual review of OCT B-scans to detect this crucial feature that portends future development of GA.

Financial Disclosures

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

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.

Non-Neovascular Age-Related Macular Degeneration Assessment: Focus on Optical Coherence Tomography Biomarkers

The imagistic evaluation of non-neovascular age-related macular degeneration (AMD) is crucial for diagnosis, monitoring progression, and guiding management of the disease. Dry AMD, characterized primarily by the presence of drusen and retinal pigment epithelium atrophy, requires detailed visualization of the retinal structure to assess its severity and progression. Several imaging modalities are pivotal in the evaluation of non-neovascular AMD, including optical coherence tomography, fundus autofluorescence, or color fundus photography. In the context of emerging therapies for geographic atrophy, like pegcetacoplan, it is critical to establish the baseline status of the disease, monitor the development and expansion of geographic atrophy, and to evaluate the retina's response to potential treatments in clinical trials. The present review, while initially providing a comprehensive description of the pathophysiology involved in AMD, aims to offer an overview of the imaging modalities employed in the evaluation of non-neovascular AMD. Special emphasis is placed on the assessment of progression biomarkers as discerned through optical coherence tomography. As the landscape of AMD treatment continues to evolve, advanced imaging techniques will remain at the forefront, enabling clinicians to offer the most effective and tailored treatments to their patients.

Phase 2 Trial Evaluating Minocycline for Geographic Atrophy in Age-Related Macular Degeneration: A Nonrandomized Controlled Trial

Importance

Existing therapies to slow geographic atrophy (GA) enlargement in age-related macular degeneration (AMD) have relatively modest anatomic efficacy, require intravitreal administration, and increase the risk of neovascular AMD. Additional therapeutic approaches are desirable.

Objective

To evaluate the safety and possible anatomic efficacy of oral minocycline, a microglial inhibitor, for the treatment of GA in AMD.

Design, Setting, and Participants

This was a phase 2, prospective, single-arm, 45-month, nonrandomized controlled trial conducted from December 2016 to April 2023. Patients with GA from AMD in 1 or both eyes were recruited from the National Institutes of Health (Bethesda, Maryland) and Bristol Eye Hospital (Bristol, UK). Study data were analyzed from September 2022 to May 2023.

Intervention

After a 9-month run-in phase, participants began oral minocycline, 100 mg, twice daily for 3 years.

Main Outcomes and Measures

The primary outcome measure was the difference in rate of change of square root GA area on fundus autofluorescence between the 24-month treatment phase and 9-month run-in phase.

Results

Of the 37 participants enrolled (mean [SD] age, 74.3 [7.6] years; 21 female [57%]), 36 initiated the treatment phase. Of these participants, 21 (58%) completed at least 33 months, whereas 15 discontinued treatment (8 by request, 6 for adverse events/illness, and 1 death). Mean (SE) square root GA enlargement rate in study eyes was 0.31 (0.03) mm per year during the run-in phase and 0.28 (0.02) mm per year during the treatment phase. The primary outcome measure of mean (SE) difference in enlargement rates between the 2 phases was -0.03 (0.03) mm per year (P = .39). Similarly, secondary outcome measures of GA enlargement rate showed no differences between the 2 phases. The secondary outcome measures of mean difference in rate of change between 2 phases were 0.2 letter score per month (95% CI, -0.4 to 0.9; P = .44) for visual acuity and 0.7 μm per month (-0.4 to 1.8; P = .20) for subfoveal retinal thickness. Of the 129 treatment-emergent adverse events among 32 participants, 49 (38%) were related to minocycline (with no severe or ocular events), including elevated thyrotropin level (15 participants) and skin hyperpigmentation/discoloration (8 participants).

Conclusions and Relevance

In this phase 2 nonrandomized controlled trial, oral minocycline was not associated with a decrease in GA enlargement over 24 months, compared with the run-in phase. This observation was consistent across primary and secondary outcome measures. Oral minocycline at this dose is likely not associated with slower rate of enlargement of GA in AMD.

Artificial intelligence in retinal imaging: current status and future prospects

INTRODUCTION

The steadily growing and aging world population, in conjunction with continuously increasing prevalences of vision-threatening retinal diseases, is placing an increasing burden on the global healthcare system. The main challenges within retinology involve identifying the comparatively few patients requiring therapy within the large mass, the assurance of comprehensive screening for retinal disease and individualized therapy planning. In order to sustain high-quality ophthalmic care in the future, the incorporation of artificial intelligence (AI) technologies into our clinical practice represents a potential solution.

AREAS COVERED

This review sheds light onto already realized and promising future applications of AI techniques in retinal imaging. The main attention is directed at the application in diabetic retinopathy and age-related macular degeneration. The principles of use in disease screening, grading, therapeutic planning and prediction of future developments are explained based on the currently available literature.

EXPERT OPINION

The recent accomplishments of AI in retinal imaging indicate that its implementation into our daily practice is likely to fundamentally change the ophthalmic healthcare system and bring us one step closer to the goal of individualized treatment. However, it must be emphasized that the aim is to optimally support clinicians by gradually incorporating AI approaches, rather than replacing ophthalmologists.

A machine learning framework for the quantification of experimental uveitis in murine OCT

This paper presents methods for the detection and assessment of non-infectious uveitis, a leading cause of vision loss in working age adults. In the first part, we propose a classification model that can accurately predict the presence of uveitis and differentiate between different stages of the disease using optical coherence tomography (OCT) images. We utilize the Grad-CAM visualization technique to elucidate the decision-making process of the classifier and gain deeper insights into the results obtained. In the second part, we apply and compare three methods for the detection of detached particles in the retina that are indicative of uveitis. The first is a fully supervised detection method, the second is a marked point process (MPP) technique, and the third is a weakly supervised segmentation that produces per-pixel masks as output. The segmentation model is used as a backbone for a fully automated pipeline that can segment small particles of uveitis in two-dimensional (2-D) slices of the retina, reconstruct the volume, and produce centroids as points distribution in space. The number of particles in retinas is used to grade the disease, and point process analysis on centroids in three-dimensional (3-D) shows clustering patterns in the distribution of the particles on the retina.