Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening.

ABSTRACT: Age-related macular degeneration (AMD) is the leading cause of central vision impairment among the elderly. Effective and accurate AMD screening tools are urgently needed. Indocyanine green angiography (ICGA) is a well-established technique for detecting chorioretinal diseases, but its invasive nature and potential risks impede its routine clinical application. Here, we innovatively developed a deep-learning model capable of generating realistic ICGA images from color fundus photography (CF) using generative adversarial networks (GANs) and evaluated its performance in AMD classification. The model was developed with 99,002 CF-ICGA pairs from a tertiary center. The quality of the generated ICGA images underwent objective evaluation using mean absolute error (MAE), peak signal-to-noise ratio

SUBMITTER: Chen R 

PROVIDER: S-EPMC10861476 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature