A Physiological Thermal Regulation Model with Application to the Diagnosis of Diabetic Peripheral Neuropathy

Diabetes afflicts an estimated over 400 million people worldwide. People with diabetes are at the risk of a wide range of devastating complications including diabetic peripheral neuropathy, which is commonly referred to as the "diabetic foot" and most often affects the lower extremities (i.e., leg and foot) and can lead to amputations. In this paper, we present a computer aided diagnostic system for diabetic foot. At the core of our system is an improved thermoregulation model that characterizes the thermal recovery process of the extremities of the body (e.g., foot) after a cold stress. The model consists of a series of differential equations which is developed based on physiological characterizations and yet also exhibits analytical solutions. The model has been shown to be accurate and robust. Based on the new thermal regulation model, we have developed a 2D Bayesian classifier. We have applied the classifier to a cohort of 49 subjects (35 with no diabetic peripheral neuropathy and 14 with diabetic peripheral neuropathy). The classifier can accurately diagnose 93% of the subjects with diabetic peripheral neuropathy with a false positive rate of only 6%. This significantly outperforms current clinical diagnostic methods which may miss 61% of the patients with diabetic peripheral neuropathy.