Visual Analytics for Early Detection of Retinal Diseases

TL;DR

This paper presents novel visual analytics methods for early detection of retinal diseases using OCT imaging, enabling interactive exploration, comparison with normative data, and automated quantification, thereby improving diagnosis and research efficiency.

Contribution

The paper introduces new visual analytics approaches specifically designed for early retinal disease detection, developed in collaboration with ophthalmology experts and industry, enhancing analysis speed and accuracy.

Findings

Reduced analysis time and effort for OCT data

Enabled new biomedical discoveries in retinal disease research

Improved early detection capabilities for retinal abnormalities

Abstract

Advances in optical coherence tomography (OCT) have enabled noninvasive imaging of substructures of the human retina with high spatial resolution. OCT examinations are now a standard procedure in clinics and an integral part of ophthalmic research. The interpretation of the OCT helps ophthalmologists understand the impact of various retinal and systemic diseases on the structure of the retina in a way not previously possible. In the early stages of retinal diseases, however, the identification and analysis of small and localized substructural changes in the retina remains a challenge. We present an overview of novel visual analytics approaches for the interactive exploration of early retinal changes in single and multiple patients, the comparison of the changes with normative data, and automated quantification and measurement of diagnosis-relevant information. We developed these approaches in close collaboration with ophthalmology researchers and industry experts from a leading OCT device manufacturer. As a result, they not only significantly reduced the time and effort required for OCT data analysis, especially in the context of cross-sectional studies, but have also led to several new discoveries published in biomedical journals.