Early Detection of Feminine Disease Using IR Spectroscopy and Graphene Electrodes

Abstract

Artificial intelligence, a technology that empowers computers and machines with human-level intelligence and problem-solving potential, is a game-changer in health care. Its significant ability to improve treatment and epidemiology, diagnosing conditions more rapidly and precisely, is a beacon of hope. Uterine cancer, a leading cause of death among women, underscores the importance of early detection for effective treatment. This paper describes complete non-cutaneous, non-invasive methods that integrate AI with Infrared (IR) spectroscopy and the cutaneous graphene-based electrode. This system is used to test for various female diseases, such as PCOD, fibroids, uterine prolapse, and cancer, using biomarkers, including CA125, a biomarker standardized for uterine cancer, focusing on physical check-ups, medical histology, and biomarker identification. By applying advanced machine learning algorithms such as SVM to analyse IR spectral data, AI significantly enhances the accuracy and speed of classifying biological samples, such as distinguishing between non-cancerous and cancerous tissues, providing a ray of hope for the future of healthcare.