Breakthrough of Clinical Candida Cultures Identification Using the Analysis of Volatile Organic Compounds and Artificial Intelligence Methods

Infections triggered by fungi of the genus Candida are widely known, although the high incidence and mortality factors are still unclear. The classic methods of identifying Candida species are subject to errors, requiring new techniques with faster and more accurate performance. We present a study f...

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Bibliographic Details
Published in:IEEE sensors journal 2022-07, Vol.22 (13), p.12493-12503
Main Authors: Castro, Maria C. A., Almeida, Leandro M., Ferreira, Renan Williams M., Benevides, Clayton A., Zanchettin, Cleber, Menezes, Frederico D., Inacio, Cicero P., de Lima-Neto, Reginlado G., Filho, Jose Gilson A. T., Neves, Rejane P.
Format: Article
Language:English
Subjects:
Artificial intelligence
Automated machine learning
electronic nose
Electronic noses
Fungi
fungi species identification
Identification methods
intelligent gas sensor
Intelligent sensors
Machine learning
microorganism identification
Protocols
Sensor arrays
Sensors
VOCs
Volatile organic compounds
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Summary:Infections triggered by fungi of the genus Candida are widely known, although the high incidence and mortality factors are still unclear. The classic methods of identifying Candida species are subject to errors, requiring new techniques with faster and more accurate performance. We present a study for identifying fungi species by analyzing volatile organic compounds of cultures acquired and interpreted using Electronic Nose and Artificial Intelligence methods. The proposed approach contributes to establishing an agile and appropriate treatment, reducing the complications of the disease and the number of deaths. We perform experiments with three species of Candida obtaining accuracy above 90% in the fungi identification. Therefore, future works are encouraged to deal with more types of fungi to help create a new identification methodology faster and more reliable using artificial intelligence methods.
ISSN:1530-437X
1558-1748
DOI:10.1109/JSEN.2022.3178346