Visual acuity assessment from optical coherence tomography images using the foundation model RETFound

Visual acuity is an important ophthalmologic measure. Its standard assessment method relies on vision tests, while there is no clinical method to derive visual acuity from medical eye images. This can be explained by the lack of defined structure-function correlations for all biomarkers visible in these images. Prior works showed that deep learning methods allow the prediction of visual impairment from medical images without biomarker identification. Beyond that, we show that fine-tuning an ophthalmic foundation model with a comparatively small data set from clinical routine enables us to derive visual acuity from only a single image. We adapt the foundation model RETFound such that it outputs one of three visual impairment levels from optical coherence tomography images taken of patients with one of two macular diseases. In this way, we achieve a satisfactory visual acuity prediction based on a single image and requiring only a small set of fine-tuning data.