Abstract
PURPOSE: Lens adapted smartphones are being used regularly instead of ophthalmoscopes. The most common causes of preventable blindness in the world, which are glaucoma and diabetic retinopathy, can develop asymptomatic changes to the optic nerve head (ONH) especially in the developing world where there is a dire shortage of ophthalmologists but ubiquitous mobile phones. We developed a proof-of-concept ONH biometric (application [APP]) to use as a routine biometric on a mobile phone. The unique blood vessel pattern is verified if it maps on to a previously enrolled image. METHODS: The iKey APP platform comprises three deep neural networks (DNNs) developed from anonymous ONH images: the graticule blood vessel (GBV) and the blood vessel specific feature (BVSF) DNNs were trained on unique blood vessel vectors. A non-feature specific (NFS) baseline ResNet50 DNN was trained for comparison. RESULTS: Verification reached an accuracy of 97.06% with BVSF, 87.24% with GBV and 79.8% using NFS. CONCLUSIONS: A new ONH biometric was developed with a hybrid platform of ONH algorithms for use as a verification biometric on a smartphone. Failure to verify will alert the user to possible changes to the image, so that silent changes may be observed before sight threatening disease progresses. The APP retains a history of all ONH images. Future longitudinal analysis will explore the impact of ONH changes to the iKey biometric platform. TRANSLATIONAL RELEVANCE: Phones with iKey will host ONH images for biometric protection of both health and financial data. The ONH may be used for automatic screening by new disease detection DNNs.