Human Tongue Thermography Could Be a Prognostic Tool for Prescreening the Type II Diabetes Mellitus

• Published in: Evidence-based complementary and alternative medicine Vol. 2020; no. 2020; pp. 1 - 16
• Main Authors: Janardanan, Kumar, Krishnan, Palani Thanaraj, Umapathy, Snekhalatha, Thirunavukkarasu, Usharani
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 2020; Hindawi; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Algorithms; Blood pressure; Body mass index; Body temperature; Cable television broadcasting industry; Cardiovascular disease; Data mining; Development and progression; Diabetes; Diabetes mellitus; Fasting; Herbal medicine; Histograms; Image processing; Infrared imaging; Learning algorithms; Machine learning; Medical prognosis; Medical screening; Methods; Neural networks; Population; Segmentation; Statistical correlation; Temperature measurement; Thermal imaging; Thermography; Tongue; Traditional Chinese medicine; Type 2 diabetes; Womens health; Canada; United States; Taiwan; India
• ISSN: 1741-427X; 1741-4288
• DOI: 10.1155/2020/3186208

Diabetes mellitus is one of the life threatening diseases over the globe, and an early prediction of diabetes is of utmost importance in this current scenario. International Diabetes Federation (IDF) reported nearly half of the world’s population was undiagnosed and unaware of being developed into diabetes. In 2017, around 84 million individuals were living with diabetes, and it might increase to 156 million by the end of 2045 stated by IDF. Generally, the diagnosis of diabetes relies on the biochemical method that may cause uneasiness and probability of infections to the subjects. To overcome such difficulties, a noninvasive method is much needed around the globe for primary screening. A change in body temperature is an indication of various diseases. Infrared thermal imaging is relatively a novel technique for skin temperature measurement and turned out to be well known in the medical field due to being noninvasive, risk-free, and repeatable. According to traditional Chinese medicine, the human tongue is a sensitive mirror that reflects the body’s pathophysiological condition. So, we have (i) analysed and classified diabetes based on thermal variations at human tongue, (ii) segmented the hot spot regions from tongue thermogram by RGB (red, green, blue) based color histogram image segmentation method and extracted the features using gray level co-occurrence matrix algorithm, (iii) classified normal and diabetes using various machine learning algorithms, and (iv) developed computer aided diagnostic system to classify diabetes mellitus. The baseline measurements and tongue thermograms were obtained from 140 subjects. The measured tongue surface temperature of the diabetic group was found to be greater than normal. The statistical correlation between the HbA1c and the thermal distribution in the tongue region was found to be r2 = 0.5688. The Convolutional Neural Network has outperformed the other classifiers with 94.28% accuracy rate. Thus, tongue thermograms could be used as a preliminary screening approach for diabetes prognosis.