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

TL;DR

This study explores a laser speckle imaging method combined with AI, specifically K-nearest neighbors, to detect early diabetes signs through skin tissue analysis, offering a non-invasive and accessible diagnostic tool.

Contribution

It introduces a novel AI-guided laser speckle imaging approach optimized for early diabetes detection by analyzing skin subcellular properties.

Findings

Effective early detection of diabetes through skin analysis

Enhanced laser parameter optimization using AI techniques

Potential for non-expert use in frequent testing

Abstract

This paper investigates the effectiveness of an expert system based on K-nearest neighbors algorithm for laser speckle image sampling applied to the early detection of diabetes. With the latest developments in artificial intelligent guided laser speckle imaging technologies, it may be possible to optimise laser parameters, such as wavelength, energy level and image texture measures in association with a suitable AI technique to interact effectively with the subcellular properties of a skin tissue to detect early signs of diabetes. 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.