loading . . . Detection and Management of Geographic Atrophy Secondary to Age-Related Macular Degeneration Using Noninvasive Retinal Images and Artificial Intelligence: Systematic Review Background: Geographic atrophy (GA), the endpoint of dry age-related macular degeneration (AMD), is irreversible. The recent approval by the FDA of complement C3 inhibitor, marks a significant breakthrough, highlighting the critical importance of early detection and management of GA. Consequently, there is an urgent and unmet need for efficient, accurate, and accessible methods to identify and monitor GA. Artificial intelligence (AI), particularly deep learning (DL), applied to non-invasive retinal imaging, offers a promising solution for automating and enhancing GA management. Objective: This systematic review aimed to assess the performance of AI using non-invasive imaging modalities and compare it with clinical expert assessment as a ground truth. Methods: Two consecutive searched were conducted on PubMed, Embase, Web of Science, Scopus, Cochrane Library, and CINAHL. The last search was performed in 5 October, 2025. Studies utilizing AI for GA secondary to dry AMD via non-invasive retinal images were included. Two authors worked in pairs to extract the study characteristics independently. A third author adjudicated disagreements. QUADAS-AI and PROBAST were applied to evaluate the risk of bias and application. Results: Of the 803 records initially identified, 176 were found through an updated search. Subsequently, 200 articles were assessed in full text, of which 41 were included in the final analysis: 10 for GA detection, 20 for GA assessment and progression, and 11 for GA lesion prediction. The reviewed studies collectively involved at least 24,592 participants (detection: 7,132; assessment and progression: 14,064; prediction: 6,706) , with a wide age range of 50 to 94 years. The studies conducted span a diverse array of countries, including the United States, the United Kingdom, China, Austria, Australia, France, Israel, Italy, Switzerland, and Germany, as well as a multicenter study encompassing seven European nations. The studies utilized a variety of imaging modalities to assess GA, including CFP, FAF, NIR, SD-OCT, SS-OCT, and 3D-OCT. DL algorithms (e.g., U-Net, ResNet50, EfficientNetB4, Xception, Inception v3, PSC-UNet) consistently showed remarkable performance in GA detection and management tasks, with several studies achieving performance comparable to clinical experts. Conclusions: AI, particularly DL-based algorithms, holds considerable promise for the detection and management of GA secondary to dry AMD with performance comparable to ophthalmologists. This review innovatively consolidates evidence across the GA management—from initial detection to progression prediction—using diverse non-invasive imaging. It has strong potential to augment clinical decision-making. However, to realize this potential in real-world settings, future research is needed to robustly enhance reporting specifications, ensure data diversity across populations and devices, and implement rigorous external validation in prospective, multicenter studies. Clinical Trial: This systematic review was registered with PROSPERO (CRD420251000963). http://dlvr.it/TPPM6B
