Metaverse and microorganism digital twins: A deep transfer learning approach

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11140/23:10468013

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Metaverse and microorganism digital twins: A deep transfer learning approach

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Metaverse and microorganism digital twins: A deep transfer learning approach

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30303 - Infectious Diseases

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název periodika

Applied Soft Computing Journal

ISSN

1568-4946

e-ISSN

1872-9681

Svazek periodika

147

Číslo periodika v rámci svazku

November

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

110798

Kód UT WoS článku

001076894700001

EID výsledku v databázi Scopus

2-s2.0-85171430599