Machine Learning Assisted Analysis of Fingernail Impression Techniques for the Detection of Metabolic Diseases

In India and around the world, the rates at which metabolic disorders like diabetes and cancer are becoming more prevalent are concerning. For the purpose of improving economical burdens and better managing metabolic disorders like diabetes and cancer, early detection of these diseases is essential. In recent, artificial intelligence and machine learning tools have provided impetus to the rapid and accurate early diagnosis of diseases like diabetes and cancer. Also, noninvasive and discarded tissues are preferred over invasive and surgical approaches for the diagnosis of diseases. The present invention describes the Nail Impression Technique (NIT) providing a painless, and needle-free method for diagnosing various illnesses. This invention requires a nail impression of the patient, which is collected by an in-house nail impression-capturing tool and analyzed by a computer-assisted imaging tool to generate unique graphs and histograms for healthy and clinical subjects. For the accurate prediction of diseases, a machine learning tool is employed. This invention is an environmentally compatible technique, generating only biodegradable waste, such as paper, and eliminating biological waste generated by traditional diagnostic methods.