Studies on Artificial Intelligence (AI) Techniques for Diabetes Diagnosis Using Facial Features

Mohammad Bagher  Owlia; Hamidreza  Soltani

doi:10.18502/jimc.v8i2.17691

Mohammad Bagher Owlia Department of Rheumatology, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran
Hamidreza Soltani Department of Rheumatology, School of Medicine, Shahid Sadoughi University of Medical Sciences, Yazd, Iran

DOI: https://doi.org/10.18502/jimc.v8i2.17691

Keywords: Artificial intelligence, Diabetes mellitus, Facial recognition, Oxidative Stress

Abstract

Diabetes Mellitus (DM) stands as one of the most widespread non-infectious diseases globally. Although diagnosis of diabetes is possible with the fasting plasma glucose test after 12-hour fast, once diabetes is diagnosed, it cannot be reversed. Therefore, it is crucial to identify early indicators for predicting diabetes.

Presently, DM can be discerned through various methods involving the analysis of human facial features. One method for facial recognition in diabetes relies on experimental evidence, with its accuracy contingent on the skill and expertise of the physician.

Another approach involves diagnosis based on facial morphological features. These morphological changes may be attributed to oxidative stress, damage of blood vessels and collagen, edema and craniofacial abnormalities stemming from hyperglycemia. While cephalometric analysis remains the gold standard for diagnosing skeletal craniofacial morphology, it is a costly and technique-sensitive procedure.

Facial recognition based on Artificial Intelligence (AI) has proven to be a valuable tool in the diagnosis and screening of diabetes. Its combination of simplicity, accuracy, and cost-effectiveness makes it a promising addition to the healthcare landscape, ultimately contributing to advancements in pre-clinical diagnosis and leading to enhanced patient outcomes.

Given the rapid global increase in diabetes, the importance of early detection of diabetes and the limited information about the role of facial recognition in this regard, this study assesses diabetes through facial features using AI approaches.