Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00669806%3A_____%2F23%3A10458676" target="_blank" >RIV/00669806:_____/23:10458676 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11140/23:10458676 RIV/49777513:23220/23:43968546
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=8vG6lxK0J3" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=8vG6lxK0J3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/bioengineering10040455" target="_blank" >10.3390/bioengineering10040455</a>
Alternative languages
Result language
angličtina
Original language name
Metaverse and Healthcare: Machine Learning-Enabled Digital Twins of Cancer
Original language 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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30204 - Oncology
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Bioengineering
ISSN
2306-5354
e-ISSN
2306-5354
Volume of the periodical
10
Issue of the periodical within the volume
4
Country of publishing house
CH - SWITZERLAND
Number of pages
23
Pages from-to
455
UT code for WoS article
000977541200001
EID of the result in the Scopus database
2-s2.0-85156115122