AI in the clinical management of GA: A novel therapeutic universe requires novel tools
Gregor S. Reiter, Julia Mai, Sophie Riedl, Klaudia Birner, Sophie Frank, Hrvoje Bogunović, Ursula Schmidt‐Erfurth
Abstract
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. • Current assessment of GA in clinical practice is neglected and unsatisfactory. • Artificial intelligence provides reliable localization and quantification of subclinical GA biomarkers. • Automated algorithms allow identification of different types and dynamics of GA progression. • Performance of advanced AI tools is superior to human experts in terms of accuracy and speed. • AI in GA analysis brings precision medicine to real-world patient monitoring and screening.