Supervised laser-speckle image sampling of skin tissue to detect very early stage of diabetes by its effects on skin subcellular properties

This paper investigates the effectiveness of a multi-disciplinary system based on laser speckle image sampling, image texture analysis and Artificial intelligence applied to the early detection of diabetes disease. With the latest developments in laser-optics and laser speckle imaging technologies, it may be possible to optimise laser parameters such as its wavelength, energy level and image texture measures to achieve this goal. The new approach is potentially more effective than the classical skin glucose level observation because of its optimised combination of laser physics and AI techniques, and additionally it allows non-expert individuals to perform more frequent skin tissue tests for an early detection of diabetes.