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DC poleHodnotaJazyk
dc.contributor.authorMoztarzadeh, Omid
dc.contributor.authorJamshidi, Mohammad
dc.contributor.authorSargolzaei, Saleh
dc.contributor.authorJamshidi, Alireza
dc.contributor.authorBaghalipour, Nasimeh
dc.contributor.authorMoghani, Malekzadeh Mona
dc.contributor.authorHauer, Lukáš
dc.date.accessioned2023-07-24T10:00:16Z-
dc.date.available2023-07-24T10:00:16Z-
dc.date.issued2023
dc.identifier.citationMOZTARZADEH, 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: 2306-5354cs
dc.identifier.issn2306-5354
dc.identifier.uri2-s2.0-85156115122
dc.identifier.urihttp://hdl.handle.net/11025/53027
dc.format23 s.cs
dc.format.mimetypeapplication/pdf
dc.language.isoenen
dc.publisherMDPIen
dc.relation.ispartofseriesBioengineering-Baselen
dc.rights© The Author(s)en
dc.titleMetaverse and Healthcare: Machine Learning-Enabled Digital Twins of Canceren
dc.typečlánekcs
dc.typearticleen
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.description.abstract-translatedMedical 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.en
dc.subject.translatedbreast canceren
dc.subject.translateddigital twinsen
dc.subject.translatedcanceren
dc.subject.translatedmachine learningen
dc.subject.translatedartificial intelligenceen
dc.subject.translatedmetaverseen
dc.subject.translatedhealthcareen
dc.identifier.doi10.3390/bioengineering10040455
dc.type.statusPeer-revieweden
dc.identifier.document-number977541200001
dc.identifier.obd43939546
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Články / Articles (KEV)
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