PurposeRetinopathy of prematurity (ROP) is a retinal vascular disease affecting premature infants that can culminate in blindness within days if not monitored and treated. A disease stage for scrutiny and administration of treatment within ROP is “plus disease” characterized by increased tortuosity and dilation of posterior retinal blood vessels. The monitoring of ROP occurs via routine imaging, typically using expensive instruments ($50 to $140 K) that are unavailable in low-resource settings at the point of care.ApproachAs part of the smartphone-ROP program to enable referrals to expert physicians, fundus images are acquired using smartphone cameras and inexpensive lenses. We developed methods for artificial intelligence determination of plus disease, consisting of a preprocessing pipeline to enhance vessels and harmonize images followed by deep learning classification. A deep learning binary classifier (plus disease versus no plus disease) was developed using GoogLeNet.ResultsVessel contrast was enhanced by 90% after preprocessing as assessed by the contrast improvement index. In an image quality evaluation, preprocessed and original images were evaluated by pediatric ophthalmologists from the US and South America with years of experience diagnosing ROP and plus disease. All participating ophthalmologists agreed or strongly agreed that vessel visibility was improved with preprocessing. Using images from various smartphones, harmonized via preprocessing (e.g., vessel enhancement and size normalization) and augmented in physically reasonable ways (e.g., image rotation), we achieved an area under the ROC curve of 0.9754 for plus disease on a limited dataset.ConclusionsPromising results indicate the potential for developing algorithms and software to facilitate the usage of cell phone images for staging of plus disease.
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.
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