Clinically relevant deep learning for detection and quantification of geographic atrophy from optical coherence tomography: a model development and external validation study Identifying and quantifying the features of dry AMD automatically for clinical trials

We present a fully developed and validated deep-learning composite model for segmentation of geographic atrophy and its subtypes that achieves performance at a similar level to manual specialist assessment. Fully automated analysis of retinal OCT from routine clinical practice could provide a promising horizon for diagnosis and prognosis in both research and real-life patient care, following further clinical validation.