von der Emde Leon, Künzel Sandrine H, Pfau Maximilian, Morelle Olivier, Liermann Yannick, Chang Petrus, Pfau Kristina, Thiele Sarah, Holz Frank G
Augenklinik, Universitätsklinikum Bonn, Venusberg-Campus 1, 53127, Bonn, Deutschland.
Institut für Molekulare und Klinische Ophthalmologie Basel, Basel, Schweiz.
Ophthalmologie. 2024 Aug;121(8):609-615. doi: 10.1007/s00347-024-02078-6. Epub 2024 Jul 31.
Advances in imaging and artificial intelligence (AI) have revolutionized the detection, quantification and monitoring for the clinical assessment of intermediate age-related macular degeneration (iAMD). The iAMD incorporates a broad spectrum of manifestations, which range from individual small drusen, hyperpigmentation, hypopigmentation up to early stages of geographical atrophy. Current high-resolution imaging technologies enable an accurate detection and description of anatomical features, such as drusen volumes, hyperreflexive foci and photoreceptor degeneration, which are risk factors that are decisive for prediction of the course of the disease; however, the manual annotation of these features in complex optical coherence tomography (OCT) scans is impractical for the routine clinical practice and research. In this context AI provides a solution by fully automatic segmentation and therefore delivers exact, reproducible and quantitative analyses of AMD-related biomarkers. Furthermore, the application of AI in iAMD facilitates the risk assessment and the development of structural endpoints for new forms of treatment. For example, the quantitative analysis of drusen volume and hyperreflective foci with AI algorithms has shown a correlation with the progression of the disease. These technological advances therefore improve not only the diagnostic precision but also support future targeted treatment strategies and contribute to the prioritized target of personalized medicine in the diagnostics and treatment of AMD.
成像技术和人工智能（AI）的进步彻底改变了对中度年龄相关性黄斑变性（iAMD）进行临床评估的检测、量化和监测方式。iAMD涵盖了广泛的表现形式，从单个小玻璃膜疣、色素沉着过度、色素沉着不足到地图样萎缩的早期阶段。当前的高分辨率成像技术能够准确检测和描述解剖特征，如玻璃膜疣体积、高反射灶和光感受器变性，这些都是决定疾病进程的危险因素；然而，在复杂的光学相干断层扫描（OCT）图像中手动标注这些特征对于常规临床实践和研究来说是不切实际的。在这种情况下，人工智能通过全自动分割提供了一种解决方案，从而能够对与AMD相关的生物标志物进行精确、可重复的定量分析。此外，人工智能在iAMD中的应用有助于风险评估以及开发新治疗形式的结构终点。例如，使用人工智能算法对玻璃膜疣体积和高反射灶进行定量分析已显示出与疾病进展相关。因此，这些技术进步不仅提高了诊断精度，还支持未来的靶向治疗策略，并有助于实现AMD诊断和治疗中个性化医疗的优先目标。
