Vessel enhancement in smartphone fundus images to aid retinopathy of prematurity and plus disease diagnosis and classification

Abstract

Retinopathy of prematurity (ROP) is a retinal vascular disease that affects premature infants and can result in blindness within days if not monitored and treated. A disease stage for increased scrutiny and treatment within ROP is “plus disease,” characterized by increased tortuosity and dilation of posterior retinal blood vessels. Monitoring of ROP occurs with routine imaging, typically using expensive instruments ranging from $50-140K. In low-resource areas of the world, smartphone cameras and inexpensive Volk 28D lenses are being used to image the fundus, albeit with lower fields of view and image quality than the expensive systems. We developed a preprocessing pipeline to enhance vessel visualization and harmonize images for automated analysis using deep learning algorithms. After preprocessing, vessel contrast was enhanced by 90% as assessed by the contrast improvement index. In an image quality evaluation, 441 images were evaluated by pediatric ophthalmologists from the US and South America, all with years of experience diagnosing ROP and plus disease. 100% of participating ophthalmologists either agreed or strongly agreed that vessel visibility was improved in the processed images. A preliminary deep learning binary classifier (plus vs. no plus disease) was developed using GoogLeNet. Using smartphone images harmonized via preprocessing (e.g., vessel enhancement and size normalization) and augmented in physically reasonable ways (e.g., image rotation), we achieved an exceptional accuracy of 0.96 for plus disease on a limited dataset. These promising results suggest the potential to create algorithms and software to improve usage of cell phone images for ROP staging.