Introduction
Geographic atrophy (GA), an advanced stage of age-related macular degeneration, is a significant global health concern, being a leading cause of legal blindness. The condition affects approximately 5 million individuals, and projections suggest that this number could rise to 10 million cases by 2040.1 2 The challenge in managing GA lies in its poorly understood aetiology and pathogenesis. GA is characterised by the progressive death of retinal pigment epithelium (RPE) and photoreceptor cells, as well as choriocapillaris loss. These alterations result in clearly delineated regions observable on retinal imaging and, if the central foveal area is implicated, they can result in significant vision loss.3 4
The recent approval of pegcetacoplan injection (SYFOVRE, Apellis Pharmaceuticals), an antagonist of C3 complement, has opened new avenues for GA therapy. By targeting the complement system, this treatment slows disease progression.5 6 This therapeutic breakthrough underscores the critical need for early diagnosis and regular monitoring of patients with GA to maximise the benefits of such treatments. To facilitate accurate and timely detection of GA, the use of advanced imaging techniques, such as optical coherence tomography (OCT), fundus autofluorescence (FAF) and colour fundus photography (CFP), is crucial. These imaging modalities offer detailed information on retinal structure and function, providing in-depth insights that enable healthcare professionals to identify GA and monitor its progression more effectively. In CFP, GA is depicted as a sharply defined, typically circular area displaying either partial or complete depigmentation of the RPE, often revealing the large choroidal blood vessels underneath. FAF amplifies the accuracy in pinpointing GA lesions and their boundaries due to the notable contrast between atrophic and non-atrophic regions, aiding in a more precise outlining and segmentation of GA lesions. OCT, as a three-dimensional imaging technique, offers advantages over two-dimensional methods (CFP and FAF), facilitating a thorough examination of atrophy and a quantitative evaluation of the involvement of specific retinal layers. It plays a pivotal role in identifying complete RPE and outer retinal atrophy, characterised by a hypertransmissive zone exceeding 250 µm, RPE disruption over 250 µm, photoreceptor degeneration, and absence of signs of RPE tears.7–11
The rapid progress in artificial intelligence (AI) technology has sparked growing interest in its application for prompt diagnosis and management of ophthalmic diseases, including GA screening and monitoring. AI algorithms have the capacity to analyse vast quantities of data from various imaging modalities, enabling the detection of subtle retinal changes that may be overlooked by human observers.12 By learning patterns and features of GA from extensive image databases, AI models can apply this knowledge to new images for automated GA diagnosis.13 However, while deep learning models employing FAF and OCT have shown remarkable performance in identifying GA when compared with CFP, their practicality and availability in various healthcare settings remain limited.14–16 In contrast, CFP stands out as a more widespread, accessible and cost-effective technique for GA screening and monitoring. CFP is a simpler method that captures images of the retina using a fundus camera, which is generally more affordable and portable than FAF and OCT equipment. This makes CFP a more viable option for healthcare facilities with limited resources or those located in remote areas. Moreover, the operation of CFP typically requires less specialised training than FAF and OCT, making it more accessible to a broader range of healthcare professionals. As a result, CFP can be more easily integrated into GA screening programmes, ensuring that a larger population has access to early detection and monitoring services.17 18
The development of explainable AI (XAI) algorithms is essential for ensuring the reliability and safety of medical decision-making processes, as they allow interpretation and explanation of AI decisions. By offering a transparent understanding of the decision-making process, XAI can foster trust between patients and healthcare providers, particularly in fields like ophthalmology, where early detection and treatment of eye diseases such as GA are paramount. XAI algorithms can analyse retinal images to identify disease-specific features and patterns, while providing a clear explanation of the diagnostic process, which not only improves diagnostic accuracy but also streamlines decision-making, enabling faster and more efficient treatment.19–21
Given the importance of such advancements, our study aimed to develop an XAI model that can accurately and reliably identify GA from colour fundus images. This model is designed to provide a cost-effective, accessible and explainable tool for early GA detection.