Cancer Digital Twins in Metaverse

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23220%2F22%3A43966896" target="_blank" >RIV/49777513:23220/22:43966896 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/9983328" target="_blank" >https://ieeexplore.ieee.org/document/9983328</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ME54704.2022.9983328" target="_blank" >10.1109/ME54704.2022.9983328</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Cancer Digital Twins in Metaverse

Popis výsledku v původním jazyce

The Metaverse is an emerging technology to make virtual environments for users to benefit from a huge number of virtual services, while users experience immersive interactions with the real world. Digital twins, which are representatives of assets in this virtual world, play an important role to connect this environment to the actual world. Therefore, translating problematic assets, objects, and disease like cancers to this cyber world provide patients with this opportunity to benefit from its advantages. This study aims to conceptualize an approach to how machine learning (ML) can realize real-time and robust digital twins of cancers to be used in the Metaverse for diagnosis and treatment. While there are a large number of ML methods, which have advantages based on the various types of healthcare data, four classic ML techniques, including ML linear regression (ML LR), decision tree regression (DTR), Random Forest Regression (RFR), and Gradient Boosting Algorithm (GBA), have been employed to implement the main part of this approach in this research. Moreover, a comprehensive conceptual framework of the ML digital twinning method has been presented to illustrate the process of digital twining cancers with different medical data.

Název v anglickém jazyce

Cancer Digital Twins in Metaverse

Popis výsledku anglicky

The Metaverse is an emerging technology to make virtual environments for users to benefit from a huge number of virtual services, while users experience immersive interactions with the real world. Digital twins, which are representatives of assets in this virtual world, play an important role to connect this environment to the actual world. Therefore, translating problematic assets, objects, and disease like cancers to this cyber world provide patients with this opportunity to benefit from its advantages. This study aims to conceptualize an approach to how machine learning (ML) can realize real-time and robust digital twins of cancers to be used in the Metaverse for diagnosis and treatment. While there are a large number of ML methods, which have advantages based on the various types of healthcare data, four classic ML techniques, including ML linear regression (ML LR), decision tree regression (DTR), Random Forest Regression (RFR), and Gradient Boosting Algorithm (GBA), have been employed to implement the main part of this approach in this research. Moreover, a comprehensive conceptual framework of the ML digital twinning method has been presented to illustrate the process of digital twining cancers with different medical data.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20201 - Electrical and electronic engineering

Projekt

<a href="/cs/project/EF18_053%2F0016927" target="_blank" >EF18_053/0016927: Mobility Západočeské univerzity v Plzni</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Proceedings of the 2022 20th International Conference on Mechatronics - Mechatronika, ME 2022

ISBN

978-1-66541-040-3

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

404-409

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Pilsen, Czech Republic

Datum konání akce

7. 12. 2022

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000947331700058