Název:	Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer
Autoři:	Moztarzadeh, Omid Jamshidi, Mohammad Sargolzaei, Saleh Jamshidi, Alireza Baghalipour, Nasimeh Moghani, Malekzadeh Mona Hauer, Lukáš
Citace zdrojového dokumentu:	MOZTARZADEH, O. JAMSHIDI, M. SARGOLZAEI, S. JAMSHIDI, A. BAGHALIPOUR, N. MOGHANI, MM. HAUER, L. Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer. Bioengineering-Basel, 2023, roč. 10, č. 4, s. nestránkováno. ISSN: 2306-5354
Datum vydání:	2023
Nakladatel:	MDPI
Typ dokumentu:	článek article
URI:	2-s2.0-85156115122 http://hdl.handle.net/11025/53027
ISSN:	2306-5354
Klíčová slova v dalším jazyce:	breast cancer;digital twins;cancer;machine learning;artificial intelligence;metaverse;healthcare
Abstrakt v dalším jazyce:	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.
Práva:	© The Author(s)
Vyskytuje se v kolekcích:	Články / Articles (RICE) Články / Articles (KEV) OBD

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