A Vision Language Correlation Framework for Screening Disabled Retina

Retinopathy is a group of retinal disabilities that causes severe visual impairments or complete blindness. Due to the capability of optical coherence tomography to reveal early retinal abnormalities, many researchers have utilized it to develop autonomous retinal screening systems. However, to the best of our knowledge, most of these systems rely only on mathematical features, which might not be helpful to clinicians since they do not encompass the clinical manifestations of screening the underlying diseases. Such clinical manifestations are critically important to be considered within the autonomous screening systems to match the grading of ophthalmologists within the clinical settings. To overcome these limitations, we present a novel framework that exploits the fusion of vision language correlation between the retinal imagery and the set of clinical prompts to recognize the different types of retinal disabilities. The proposed framework is rigorously tested on six public datasets, where, across each dataset, the proposed framework outperformed state-of-the-art methods in various metrics. Moreover, the clinical significance of the proposed framework is also tested under strict blind testing experiments, where the proposed system achieved a statistically significant correlation coefficient of 0.9185 and 0.9529 with the two expert clinicians. These blind test experiments highlight the potential of the proposed framework to be deployed in the real world for accurate screening of retinal diseases.