Metaverse and microorganism digital twins: A deep transfer learning approach

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00669806%3A_____%2F23%3A10468013" target="_blank" >RIV/00669806:_____/23:10468013 - isvavai.cz</a>

Alternative codes found

RIV/00216208:11140/23:10468013

Result on the web

<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=UJSjTlOBzZ" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=UJSjTlOBzZ</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2023.110798" target="_blank" >10.1016/j.asoc.2023.110798</a>

Result language

angličtina

Original language name

Metaverse and microorganism digital twins: A deep transfer learning approach

Original language description

Preparing the infrastructure for analyzing, recognizing, and characterizing microorganisms in the Metaverse can transform the fields of biology, medicine, and drug discovery. Accordingly, the realization of digital twins of microorganisms, such as viruses, fungi, algae, bacteria, protozoa, archaea, and multicellular animal parasites, can streamline the applicability of the Metaverse and similar emerging technologies like Cyber-Physical Healthcare Systems (CPHS). This is why a new approach to the digital twinning of bacteria has been presented in this research. This method of digital twinning can revolutionize the research and study of bacteria because it allows us to separate useful and harmful bacterial species, increasing the efficiency of treatment noticeably. This innovative method can easily be used by clinics, medical centers, or even by private users physically and virtually and can be adapted to every centralized or decentralized Metaverse Platform. To determine the proper treatment, biologists have always tried to identify the correct bacterial species that prompted a bacterial infection. They use various indicators, such as the bacterial cell&apos;s shape and the size of the colony formed by the bacteria, to classify different types of bacteria with different biochemistries and shapes. However, it is challenging because of the extensive similarities between some species. For instance, such similarities exist between Staphylococcus aureus and Staphylococcus saprophyticus, which has caused numerous false diagnostic reports by operators. Wrong species reports bring about treatment failure and increase antibiotic resistance issues. Therefore, the digital twins of bacteria cover all of their identifiable characteristics and overcome many limitations to study them. In this approach, DTL techniques, including MobileNetV2, EfficientNetV2-S, and ResNet-50, have been employed to build digital twins of bacteria species. A hybrid dataset was used for training and evaluation. Among the models, EfficientNetV2-S exhibited the best performance, with a validation accuracy of 99.58% and a test accuracy of 99.33%. The results showed the ability of deep learning models to make bacteria digital twins based on image processing from different labs, thus assisting experts in speeding up the process and reducing diagnostic errors. In addition, in the mispredicted cases, the correct species was among the first three choices of the model. Therefore, not only can experts use DTL approaches to speed up the digital twin realization of microorganisms in the Metaverse, but they can also use these methods to reduce diagnostic errors.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

30303 - Infectious Diseases

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

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

Name of the periodical

Applied Soft Computing Journal

ISSN

1568-4946

e-ISSN

1872-9681

Volume of the periodical

147

Issue of the periodical within the volume

November

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

16

Pages from-to

110798

UT code for WoS article

001076894700001

EID of the result in the Scopus database

2-s2.0-85171430599