Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer
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 th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Bioengineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2306-5354/10/4/455 |
| _version_ | 1827745869367083008 |
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| author | Omid Moztarzadeh Mohammad (Behdad) Jamshidi Saleh Sargolzaei Alireza Jamshidi Nasimeh Baghalipour Mona Malekzadeh Moghani Lukas Hauer |
| author_facet | Omid Moztarzadeh Mohammad (Behdad) Jamshidi Saleh Sargolzaei Alireza Jamshidi Nasimeh Baghalipour Mona Malekzadeh Moghani Lukas Hauer |
| author_sort | Omid Moztarzadeh |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T05:14:32Z |
| format | Article |
| id | doaj.art-f701644b73844338931a745c9d872782 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T05:14:32Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-f701644b73844338931a745c9d8727822023-11-17T18:22:20ZengMDPI AGBioengineering2306-53542023-04-0110445510.3390/bioengineering10040455Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of CancerOmid Moztarzadeh0Mohammad (Behdad) Jamshidi1Saleh Sargolzaei2Alireza Jamshidi3Nasimeh Baghalipour4Mona Malekzadeh Moghani5Lukas Hauer6Department of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech RepublicFaculty of Electrical Engineering, University of West Bohemia, 30100 Pilsen, Czech RepublicDepartment of Computer Engineering, Mashhad Branch, Islamic Azad University, Mashhad 9187147578, IranDentistry School, Babol University of Medical Sciences, Babol 4717647745, IranDepartment of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech RepublicDepartment of Radiation Oncology, Medical School, Shahid Beheshti, University of Medical Sciences, Teheran 1985717443, IranDepartment of Stomatology, University Hospital Pilsen, Faculty of Medicine in Pilsen, Charles University, 32300 Pilsen, Czech RepublicMedical 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.https://www.mdpi.com/2306-5354/10/4/455breast cancerdigital twinscancermachine learningartificial intelligencemetaverse |
| spellingShingle | Omid Moztarzadeh Mohammad (Behdad) Jamshidi Saleh Sargolzaei Alireza Jamshidi Nasimeh Baghalipour Mona Malekzadeh Moghani Lukas Hauer Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer Bioengineering breast cancer digital twins cancer machine learning artificial intelligence metaverse |
| title | Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer |
| title_full | Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer |
| title_fullStr | Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer |
| title_full_unstemmed | Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer |
| title_short | Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer |
| title_sort | metaverse and healthcare machine learning enabled digital twins of cancer |
| topic | breast cancer digital twins cancer machine learning artificial intelligence metaverse |
| url | https://www.mdpi.com/2306-5354/10/4/455 |
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