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Image analysis for retinal images has been an active subject of research over the last couple of decades. With the popularity of mydriatic and non-mydriatic digital imaging cameras, color fundus photographs have become essential part of standard retinal eye care. Advances in image analysis, pattern recognition, and machine learning have opened up a great opportunity to enhance the clinical care available to patients suffering for retinal diseases such as diabetic retinopathy, age related macular degeneration, hypertensive retinopathy, and glaucoma.

Eyenuk is developing exciting new technologies to detect various lesions related to retinal diseases such as diabetic retinopathy (DR), and age related macular degeneration (ARMD). Eyenuk scientists have deep understanding of image processing theory and have leveraged this experience to redesign the low-level modules from scratch. This includes highly novel modules (customized for retinal images) for feature detection and description. In addition, theoretically well-grounded iterative joint segmentation-recognition module is employed to further improve the accuracy. Watch this space as we work our way into developing and validating this technology into a versatile product for clinicians!