Monitoring the progress of subconjunctival hemorrhage through spectral reconstruction from RGB images

Abstract

Subconjunctival hemorrhage (SCH) is a prevalent ocular condition characterized by the accumulation of blood beneath the conjunctiva, resulting in a visible red patch on the eye's surface. The appearance of SCH and the limited understanding of its progression can cause significant anxiety for patients. To address this issue and enhance ocular health management, we develop a deep learning-enabled monitoring approach that quantitatively tracks the SCH healing process through spectral reconstruction. Our approach comprises two key technical components. Firstly, automatic white balance algorithms are employed to estimate the light source's color temperature and adjust image colors, minimizing the impact of varying lighting conditions. The SCH segmentation achieves an accuracy of 96.2 %, effectively avoiding interference from skin and eyelashes. Secondly, our monitoring approach evaluates SCH color changes, which are crucial for determining the stage of recovery. By learning a complex mapping function, the approach generates 31 hyperspectral bands (400-700 nm) by recovering the lost spectral information from a given RGB image. This process allows for a more detailed spectroscopic assessment of the affected area. The rich spectral signatures obtained from these hyperspectral images enable the classification of SCH into three distinct stages, reflecting the blood reabsorption process. This study is the first to apply deep learning-based spectral reconstruction to SCH determination, enabling evaluation of the recovery process through spectroscopic and quantitative analysis. This approach has the potential to improve daily patient care and promote better eye health control by offering more comprehensive monitoring of SCH progression.