Age Related Macular Degeneration(AMD) has been one of the most leading causes of permanent vision impairment in ophthalmology. Though treatments, such as anti VEGF drugs or photodynamic therapies, were developed to slow down the degenerative process of AMD, there is still no specific cure to reverse vision loss caused by AMD. Thus, for AMD, detecting existence of risk factors of AMD or AMD itself within the patient retina in early stages is a crucial task to reduce the possibility of vision impairment. Apart from traditional approaches, deep learning based methods, especially attention mechanism based CNNs and GradCAM based XAI analysis on OCT scans, exhibited successful performance in distinguishing AMD retina from normal retinas, making it possible to use AI driven models to aid medical diagnosis and analysis by ophthalmologists regarding AMD. However, though having significant success, previous works mostly focused on prediction performance itself, not pathologies or underlying causal mechanisms of AMD, which can prohibit intervention analysis on specific factors or even lead to less reliable decisions. Thus, this paper introduces a novel causal AMD analysis model: GCVAMD, which incorporates a modified CausalVAE approach that can extract latent causal factors from only raw OCT images. By considering causality in AMD detection, GCVAMD enables causal inference such as treatment simulation or intervention analysis regarding major risk factors: drusen and neovascularization, while returning informative latent causal features that can enhance downstream tasks. Results show that through GCVAMD, drusen status and neovascularization status can be identified with AMD causal mechanisms in GCVAMD latent spaces, which can in turn be used for various tasks from AMD detection(classification) to intervention analysis.

翻译：年龄相关性黄斑变性（AMD）一直是眼科学中导致永久性视力损伤的最主要病因之一。尽管已开发出抗VEGF药物或光动力疗法等治疗方法来延缓AMD的退行性进程，但目前仍无特异性疗法可逆转AMD引起的视力丧失。因此，对于AMD而言，在早期阶段检测患者视网膜中是否存在AMD风险因素或AMD本身，是降低视力损伤可能性的关键任务。除传统方法外，基于深度学习的方法，特别是基于注意力机制的CNN以及对OCT扫描进行基于GradCAM的XAI分析，在区分AMD视网膜与正常视网膜方面表现出成功性能，使得利用AI驱动模型辅助眼科医生进行AMD的医学诊断与分析成为可能。然而，尽管取得显著成功，先前的研究大多侧重于预测性能本身，而非AMD的病理学或潜在因果机制，这可能阻碍对特定因素的干预分析，甚至导致决策可靠性降低。因此，本文提出一种新颖的因果性AMD分析模型：GCVAMD，该模型采用一种改进的CausalVAE方法，能够仅从原始OCT图像中提取潜在因果因素。通过在AMD检测中引入因果考量，GCVAMD能够实现关于主要风险因素（玻璃膜疣和新生血管）的因果推断，如治疗模拟或干预分析，同时返回可增强下游任务的信息性潜在因果特征。结果表明，通过GCVAMD，可在其潜在空间中结合AMD因果机制识别玻璃膜疣状态与新生血管状态，进而应用于从AMD检测（分类）到干预分析等多种任务。