Use of artificial intelligence for recognition of biomarkers in intermediate age-related macular degeneration

• Published in: Die Ophthalmologie Vol. 121; no. 8; p. 609
• Main Authors: von der Emde, Leon, Künzel, Sandrine H, Pfau, Maximilian, Morelle, Olivier, Liermann, Yannick, Chang, Petrus, Pfau, Kristina, Thiele, Sarah, Holz, Frank G
• Format: Journal Article
• Language: German
• Published: Germany 01.08.2024
• Subjects: Algorithms; Artificial Intelligence; Biomarkers - analysis; Biomarkers - metabolism; Humans; Image Interpretation, Computer-Assisted - methods; Macular Degeneration - diagnosis; Macular Degeneration - diagnostic imaging; Macular Degeneration - pathology; Retinal Drusen - diagnosis; Retinal Drusen - diagnostic imaging; Retinal Drusen - metabolism; Sensitivity and Specificity; Tomography, Optical Coherence - methods; Subretinal drusenoid deposits; Drusen; Predicted sensitivity; Photoreceptor degeneration; Hyperreflective foci
• ISSN: 2731-7218; 2731-7218
• DOI: 10.1007/s00347-024-02078-6

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.