Abstract Retinal diseases are among the leading causes of visual impairment worldwide, where timely diagnosis and management are critical to prevent irreversible vision loss and blindness, especially in regions with limited access to ophthalmologists. While artificial intelligence (AI) has shown remarkable potential for screening ocular diseases, many existing models are heavily dependent on the datasets used for their development and often experience significant performance drops when tested on external datasets. These limitations in robustness and generalizability reduce their practical applicability in clinical settings. In this study, we proposed our newly developed deep learning architecture, FlexiVarViT is designed to enhance robustness and generalizability by addressing the unique characteristics of optical coherence tomography (OCT) imaging, such as variable data (e.g., number of slices, resolution), while processing B-scans at their native resolution (i.e., without resizing) to preserve fine anatomical details. Our method was evaluated on three datasets for the accurate detection and classification of multiple retinal pathologies using OCT images. These datasets represent diverse imaging devices (Spectralis and Optovue) and populations from France, Russia, and Iran. The results highlight the versatility, robustness, and generalizability of our model across various patient demographics and imaging systems, demonstrating significant improvements compared to state-of-the-art methods and emphasizing its potential for clinical application.
Zhang et al. (Thu,) studied this question.