Abstract

Diabetes leads to protein glycation and causes dysfunction of collagen-containing tissues. The accompanying structural and functional changes of collagen significantly contribute to the development of various pathological malformations affecting the skin, blood vessels, and nerves, causing a number of complications, increasing disability risks and threat to life. The prevalence of diabetic complications is a significant public health problem with a considerable economic cost. In fact, no methods currently exist of noninvasive assessment of glycation and associated metabolic processes in biotissues or prediction of possible skin complications, e.g., ulcers, for clinical diagnosis and use by endocrinologists. Here, utilizing emerging photonics-based technology, innovative solutions in machine learning, and definitive physiological characteristics, we describe a diagnostic approach capable of evaluating the skin complications of diabetes mellitus at an early stage. The technique of polarization-based hyperspectral imaging developed in-house, accomplished by implementing an artificial neural network, provides new horizons in the study and diagnosis of age-related diseases.