Regulatory approval of the first two therapeutic substances for the management of geographic atrophy (GA) secondary to age-related macular degeneration (AMD) is a major breakthrough following failure of numerous previous trials. However, in the absence of therapeutic standards, diagnostic tools are a key challenge as functional parameters in GA are hard to provide. The majority of anatomical biomarkers are subclinical, necessitating advanced and sensitive image analyses. In contrast to fundus autofluorescence (FAF), optical coherence tomography (OCT) provides high-resolution visualization of neurosensory layers, including photoreceptors, and other features that are beyond the scope of human expert assessment. Artificial intelligence (AI)-based methodology strongly enhances identification and quantification of clinically relevant GA-related sub-phenotypes. Introduction of OCT-based biomarker analysis provides novel insight into the pathomechanisms of disease progression and therapeutic, moving beyond the limitations of conventional descriptive assessment. Accordingly, the Food and Drug Administration (FDA) has provided a paradigm-shift in recognizing ellipsoid zone (EZ) attenuation as a primary outcome measure in GA clinical trials. In this review, the transition from previous to future GA classification and management is described. With the advent of AI tools, diagnostic and therapeutic concepts have changed substantially in monitoring and screening of GA disease. Novel technology combined with pathophysiological knowledge and understanding of the therapeutic response to GA treatments, is currently opening the path for an automated, efficient and individualized patient care with great potential to improve access to timely treatment and reduce health disparities.

中文翻译：

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AI 在 GA 临床管理中的应用：一个新的治疗领域需要新的工具


前两种用于治疗继发于年龄相关性黄斑变性 （AMD） 的地图样萎缩 （GA） 的监管批准是继先前许多试验失败之后的重大突破。然而，在缺乏治疗标准的情况下，诊断工具是一个关键挑战，因为 GA 中的功能参数难以提供。大多数解剖生物标志物是亚临床的，需要先进和敏感的图像分析。与眼底自发荧光 （FAF） 相比，光学相干断层扫描 （OCT） 提供神经感觉层（包括光感受器）的高分辨率可视化，以及超出人类专家评估范围的其他特征。基于人工智能 （AI） 的方法极大地增强了临床相关的 GA 相关亚表型的识别和量化。基于 OCT 的生物标志物分析的引入为疾病进展和治疗的病理机制提供了新的见解，超越了传统描述性评估的局限性。因此，美国食品药品监督管理局 （FDA） 提供了一种范式转变，将椭球区 （EZ） 衰减视为 GA 临床试验中的主要结果指标。在本综述中，描述了从以前到未来的 GA 分类和管理的转变。随着 AI 工具的出现，GA 疾病监测和筛查的诊断和治疗概念发生了重大变化。新技术与病理生理学知识和对 GA 治疗反应的理解相结合，目前正在为自动化、高效和个性化的患者护理开辟道路，在改善及时治疗和减少健康差异方面具有巨大潜力。