Detection of Nonexudative Macular Neovascularization on Structural OCT Images Using Vision Transformers

Predict and Protect: Evaluating the Double-Layer Sign in Age-Related Macular Degeneration

INTRODUCTION

Advanced age-related macular degeneration (AMD) is a major cause of vision loss. Therefore, there is interest in precursor lesions that may predict or prevent the onset of advanced AMD. One such lesion is a shallow separation of the retinal pigment epithelium (RPE) and Bruch's membrane (BM), which is described by various terms, including double-layer sign (DLS).

METHODS

In this article, we aim to examine and clarify the different terms referring to shallow separation of the RPE and BM. We also review current evidence on the outcomes associated with DLS: firstly, whether DLS is predictive of exudative neovascular AMD; and secondly, whether DLS has potential protective properties against geographic atrophy.

RESULTS

The range of terms used to describe a shallow separation of the RPE and BM reflects that DLS can present with different characteristics. While vascularised DLS appears to protect against atrophy but can progress to exudation, non-vascularised DLS is associated with an increased risk of atrophy. Optical coherence tomography (OCT) angiography (OCTA) is the principal method for identifying and differentiating various forms of DLS. If OCTA is unavailable or not practically possible, simplified classification of DLS as thick or thin, using OCT, enables the likelihood of vascularisation to be approximated. Research is ongoing to automate DLS detection by applying deep-learning algorithms to OCT scans.

CONCLUSIONS

The term DLS remains applicable for describing shallow separation of the RPE and BM. Detection and classification of this feature provides valuable information regarding the risk of progression to advanced AMD. However, the appearance of DLS and its value in predicting AMD progression can vary between patients. With further research, individualised risks can be confirmed to inform appropriate treatment.

Discriminative, generative artificial intelligence, and foundation models in retina imaging

Recent advances of artificial intelligence (AI) in retinal imaging found its application in two major categories: discriminative and generative AI. For discriminative tasks, conventional convolutional neural networks (CNNs) are still major AI techniques. Vision transformers (ViT), inspired by the transformer architecture in natural language processing, has emerged as useful techniques for discriminating retinal images. ViT can attain excellent results when pretrained at sufficient scale and transferred to specific tasks with fewer images, compared to conventional CNN. Many studies found better performance of ViT, compared to CNN, for common tasks such as diabetic retinopathy screening on color fundus photographs (CFP) and segmentation of retinal fluid on optical coherence tomography (OCT) images. Generative Adversarial Network (GAN) is the main AI technique in generative AI in retinal imaging. Novel images generated by GAN can be applied for training AI models in imbalanced or inadequate datasets. Foundation models are also recent advances in retinal imaging. They are pretrained with huge datasets, such as millions of CFP and OCT images and fine-tuned for downstream tasks with much smaller datasets. A foundation model, RETFound, which was self-supervised and found to discriminate many eye and systemic diseases better than supervised models. Large language models are foundation models that may be applied for text-related tasks, like reports of retinal angiography. Whereas AI technology moves forward fast, real-world use of AI models moves slowly, making the gap between development and deployment even wider. Strong evidence showing AI models can prevent visual loss may be required to close this gap.

Rediscovering Age-Related Macular Degeneration with Swept-Source OCT Imaging: The 2022 Charles L. Schepens, MD, Lecture

PURPOSE

Swept-source OCT angiography (SS-OCTA) scans of eyes with age-related macular degeneration (AMD) were used to replace color, autofluorescence, infrared reflectance, and dye-based fundus angiographic imaging for the diagnosis and staging of AMD. Through the use of different algorithms with the SS-OCTA scans, both structural and angiographic information can be viewed and assessed using both cross sectional and en face imaging strategies.

DESIGN

Presented at the 2022 Charles L. Schepens, MD, Lecture at the American Academy of Ophthalmology Retina Subspecialty Day, Chicago, Illinois, on September 30, 2022.

PARTICIPANTS

Patients with AMD.

METHODS

Review of published literature and ongoing clinical research using SS-OCTA imaging in AMD.

MAIN OUTCOME MEASURES

Swept-source OCT angiography imaging of AMD at different stages of disease progression.

RESULTS

Volumetric SS-OCTA dense raster scans were used to diagnose and stage both exudative and nonexudative AMD. In eyes with nonexudative AMD, a single SS-OCTA scan was used to detect and measure structural features in the macula such as the area and volume of both typical soft drusen and calcified drusen, the presence and location of hyperreflective foci, the presence of reticular pseudodrusen, also known as subretinal drusenoid deposits, the thickness of the outer retinal layer, the presence and thickness of basal laminar deposits, the presence and area of persistent choroidal hypertransmission defects, and the presence of treatment-naïve nonexudative macular neovascularization. In eyes with exudative AMD, the same SS-OCTA scan pattern was used to detect and measure the presence of macular fluid, the presence and type of macular neovascularization, and the response of exudation to treatment with vascular endothelial growth factor inhibitors. In addition, the same scan pattern was used to quantitate choriocapillaris (CC) perfusion, CC thickness, choroidal thickness, and the vascularity of the choroid.

CONCLUSIONS

Compared with using several different instruments to perform multimodal imaging, a single SS-OCTA scan provides a convenient, comfortable, and comprehensive approach for obtaining qualitative and quantitative anatomic and angiographic information to monitor the onset, progression, and response to therapies in both nonexudative and exudative AMD.

FINANCIAL DISCLOSURE(S)

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

A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD)

The rapid advancement of computational infrastructure has led to unprecedented growth in machine learning, deep learning, and computer vision, fundamentally transforming the analysis of retinal images. By utilizing a wide array of visual cues extracted from retinal fundus images, sophisticated artificial intelligence models have been developed to diagnose various retinal disorders. This paper concentrates on the detection of Age-Related Macular Degeneration (AMD), a significant retinal condition, by offering an exhaustive examination of recent machine learning and deep learning methodologies. Additionally, it discusses potential obstacles and constraints associated with implementing this technology in the field of ophthalmology. Through a systematic review, this research aims to assess the efficacy of machine learning and deep learning techniques in discerning AMD from different modalities as they have shown promise in the field of AMD and retinal disorders diagnosis. Organized around prevalent datasets and imaging techniques, the paper initially outlines assessment criteria, image preprocessing methodologies, and learning frameworks before conducting a thorough investigation of diverse approaches for AMD detection. Drawing insights from the analysis of more than 30 selected studies, the conclusion underscores current research trajectories, major challenges, and future prospects in AMD diagnosis, providing a valuable resource for both scholars and practitioners in the domain.

Multi-Dataset Comparison of Vision Transformers and Convolutional Neural Networks for Detecting Glaucomatous Optic Neuropathy from Fundus Photographs

Glaucomatous optic neuropathy (GON) can be diagnosed and monitored using fundus photography, a widely available and low-cost approach already adopted for automated screening of ophthalmic diseases such as diabetic retinopathy. Despite this, the lack of validated early screening approaches remains a major obstacle in the prevention of glaucoma-related blindness. Deep learning models have gained significant interest as potential solutions, as these models offer objective and high-throughput methods for processing image-based medical data. While convolutional neural networks (CNN) have been widely utilized for these purposes, more recent advances in the application of Transformer architectures have led to new models, including Vision Transformer (ViT,) that have shown promise in many domains of image analysis. However, previous comparisons of these two architectures have not sufficiently compared models side-by-side with more than a single dataset, making it unclear which model is more generalizable or performs better in different clinical contexts. Our purpose is to investigate comparable ViT and CNN models tasked with GON detection from fundus photos and highlight their respective strengths and weaknesses. We train CNN and ViT models on six unrelated, publicly available databases and compare their performance using well-established statistics including AUC, sensitivity, and specificity. Our results indicate that ViT models often show superior performance when compared with a similarly trained CNN model, particularly when non-glaucomatous images are over-represented in a given dataset. We discuss the clinical implications of these findings and suggest that ViT can further the development of accurate and scalable GON detection for this leading cause of irreversible blindness worldwide.

Unleashing the power of optical attenuation coefficients to facilitate segmentation strategies in OCT imaging of age-related macular degeneration: perspective

The use of optical attenuation coefficients (OAC) in optical coherence tomography (OCT) imaging of the retina has improved the segmentation of anatomic layers compared with traditional intensity-based algorithms. Optical attenuation correction has improved our ability to measure the choroidal thickness and choroidal vascularity index using dense volume scans. Algorithms that combine conventional intensity-based segmentation with depth-resolved OAC OCT imaging have been used to detect elevations of the retinal pigment epithelium (RPE) due to drusen and basal laminar deposits, the location of hyperpigmentation within the retina and along the RPE, the identification of macular atrophy, the thickness of the outer retinal (photoreceptor) layer, and the presence of calcified drusen. OAC OCT algorithms can identify the risk-factors that predict disease progression in age-related macular degeneration.

A vision transformer architecture for the automated segmentation of retinal lesions in spectral domain optical coherence tomography images

Neovascular age-related macular degeneration (nAMD) is one of the major causes of irreversible blindness and is characterized by accumulations of different lesions inside the retina. AMD biomarkers enable experts to grade the AMD and could be used for therapy prognosis and individualized treatment decisions. In particular, intra-retinal fluid (IRF), sub-retinal fluid (SRF), and pigment epithelium detachment (PED) are prominent biomarkers for grading neovascular AMD. Spectral-domain optical coherence tomography (SD-OCT) revolutionized nAMD early diagnosis by providing cross-sectional images of the retina. Automatic segmentation and quantification of IRF, SRF, and PED in SD-OCT images can be extremely useful for clinical decision-making. Despite the excellent performance of convolutional neural network (CNN)-based methods, the task still presents some challenges due to relevant variations in the location, size, shape, and texture of the lesions. This work adopts a transformer-based method to automatically segment retinal lesion from SD-OCT images and qualitatively and quantitatively evaluate its performance against CNN-based methods. The method combines the efficient long-range feature extraction and aggregation capabilities of Vision Transformers with data-efficient training of CNNs. The proposed method was tested on a private dataset containing 3842 2-dimensional SD-OCT retina images, manually labeled by experts of the Franziskus Eye-Center, Muenster. While one of the competitors presents a better performance in terms of Dice score, the proposed method is significantly less computationally expensive. Thus, future research will focus on the proposed network's architecture to increase its segmentation performance while maintaining its computational efficiency.