Deep Density Estimation for Cone Counting and Diagnosis of Genetic Eye Diseases From Adaptive Optics Scanning Light Ophthalmoscope Images

Abstract

Purpose: Adaptive optics scanning light ophthalmoscope (AOSLO) imaging offers a microscopic view of the living retina, holding promise for diagnosing and researching eye diseases like retinitis pigmentosa and Stargardt’s disease. The technology’s clinical impact of AOSLO hinges on early detection through automated analysis tools. Methods: We introduce Cone Density Estimation (CoDE) and CoDE for Diagnosis (CoDED). CoDE is a deep density estimation model for cone counting that estimates a density function whose integral is equal to the number of cones. CoDED is an integration of CoDE with deep image classifiers for diagnosis. We use two AOSLO image datasets to train and evaluate the performance of cone density estimation and classification models for retinitis pigmentosa and Stargardt’s disease. Results: Bland-Altman plots show that CoDE outperforms state-of-the-art models for cone density estimation. CoDED reported an F1 score of 0.770 ± 0.04 for disease classification, outperforming traditional convolutional networks. Conclusions: CoDE shows promise in classifying the retinitis pigmentosa and Stargardt’s disease cases from a single AOSLO image. Our preliminary results suggest the potential role of analyzing patterns in the retinal cellular mosaic to aid in the diagnosis of genetic eye diseases.