LAUSR

Rapid advances in retinal imaging technology combined with deep learning approaches for image analysis have provided new avenues of investigation in ophthalmic disease. First, deep learning provides a de novo approach to image analysis, identifying previously unrecognized imaging features that correlate with functional changes. In age-related macular degeneration (AMD), deep learning approaches identified subtle retinal features, hyporeflective outer retinal bands in the central macula, that are associated with delayed rod-mediation dark adaptation, a functional biomarker of early AMD. Second, deep learning allows prediction of clinical outcomes such as visual field progression in glaucoma. Lastly, deep learning models can also be used to segment anatomic features from ophthalmic imaging, enabling accurate and fully automated periorbital measurements with many potential clinical applications in oculoplastics. Deep learning applications in ophthalmic imaging have potential to improve our understanding of disease and their clinical outcomes.