Non-Invasive Diagnosis and Monitoring of Skin Anomalies

Abstract

Skin cancer, Acne, Eczema, among others, are some of the most commonly occurring skin anomalies that impose debilitating strain on the health of affected individuals and impede their social and economic welfare. The world health organization estimates between 2 to 3 million skin cancer cases occurring globally every year, highlighting its commonality. However, traditional skin disease diagnosis techniques typically followed by medical professionals, including physicians and dermatologists, primarily rely on qualitative and invasive measures, such as visual screening and biopsies. Therefore, developing an adjunctive tool that assists medical professionals in providing diagnosis that is more accurate in a low-cost, non-invasive manner is highly advantageous. Such a novel tool relieves the time-consuming and uncomfortable procedures of traditional diagnosis methods. Additionally, proper monitoring and characterization of the condition of a skin anomaly increases the chances of successful treatment. Keeping in mind that early diagnosis reduces any potential health complications.

Extensive research measures have been taken in the pursuit of non-invasive, low-cost, and reliable diagnosis and monitoring methods. In the radio-frequency domain, clear differentiation between healthy and malignant tissues has been shown. Throughout this thesis, a novel and complete system for the non-invasive diagnosis and monitoring of skin anomalies using radio-frequency technology is designed and validated. The portable and handheld system comprises a highly sensitive electromagnetic sensor, custom wave analyzer circuitry, and the corresponding firmware and statistical classification algorithms. The system is then validated by performing clinical trials on patients with pre-diagnosed skin cancer and on healthy controls. Our findings illustrate a clear, distinct, and consistent differentiation between healthy and cancerous skin lesions, thereby conceiving a powerful tool that has the ability to augment traditional diagnosis methods to improve clinical diagnosis accuracy and enhance the patients’ overall quality of life.