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

Abstract

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.

Keywords: Age-related macular degeneration; Anti-VEGF; Artificial intelligence; Choroidal neovascularization; Deep learning; Drusen; Geographic atrophy; Optical coherence tomography.

Fig. 1

Convolutional neural network with an encoder-decoder architecture to identify intraretinal fluid (green) and subretinal fluid (blue). The retinal tissue is marked in red. Reproduced with permission from Schlegl et al., 2022 [37]. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article)

Fig. 2

Deep learning utilizing a short-path approach for segmenting retinal layers. Orange boxes represent training steps and gray boxes represent evaluation steps. Reproduced with permission from Mishra et al., 2020 [38]

Fig. 3

Geographic atrophy (GA) lesion from two patients at baseline (BSL) (upper row) and at month 12 (lower row) from OAKS clinical trial dataset automated segmented with the AI-based GA monitor. Retinal pigment epithelium (RPE) loss (blue) and photoreceptor integrity loss (green) are shown as en face visualizations (left) and example B-scans (right). RPE loss extends into regions of preexisting PR loss

Fig. 4

Example of 2 lesions with geographic atrophy and different PR/RPE loss ratios. PR loss is marked in green and RPE loss in red. (a) shows a lesion with a small PR/RPE loss ratio and (b) a lesion with a large PR/RPE loss ratio at baseline. Letters (c) and (d) show the respective lesions at month 12 with significantly faster growth in the lesion with the higher ratio (d). Reproduced with permission from Schmidt-Erfurth et al., 2023 [90]