Medical digital twins, which represent medical assets, play a crucial role in connecting the physical world to the metaverse, enabling patients to access virtual medical services and experience immersive interactions with the real world. One serious disease that can be diagnosed and treated using this technology is cancer. However, the digitalization of such diseases for use in the metaverse is a highly complex process. To address this, this study aims to use machine learning (ML) techniques to create real-time and reliable digital twins of cancer for diagnostic and therapeutic purposes. The study focuses on four classical ML techniques that are simple and fast for medical specialists without extensive Artificial Intelligence (AI) knowledge, and meet the requirements of the Internet of Medical Things (IoMT) in terms of latency and cost. The case study focuses on breast cancer (BC), the second most prevalent form of cancer worldwide. The study also presents a comprehensive conceptual framework to illustrate the process of creating digital twins of cancer, and demonstrates the feasibility and reliability of these digital twins in monitoring, diagnosing, and predicting medical parameters.

Dentistry School Babol University of Medical Sciences Babol 4717647745 Iran

Department of Anatomy Faculty of Medicine in Pilsen Charles University 32300 Pilsen Czech Republic

Department of Computer Engineering Mashhad Branch Islamic Azad University Mashhad 9187147578 Iran

Department of Radiation Oncology Medical School Shahid Beheshti University of Medical Sciences Teheran 1985717443 Iran

Department of Stomatology University Hospital Pilsen Faculty of Medicine in Pilsen Charles University 32300 Pilsen Czech Republic

Faculty of Electrical Engineering University of West Bohemia 30100 Pilsen Czech Republic

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Predicting Chronic Hyperplastic Candidiasis Retro-Angular Mucosa Using Machine Learning