Journal
MICROORGANISMS
Volume 11, Issue 8, Pages -Publisher
MDPI
DOI: 10.3390/microorganisms11081872
Keywords
NGS; MDR-TB; SNPs; rpoB; Mycobacterium tuberculosis; ML; IA
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Tuberculosis remains a significant global health problem, and diagnosing drug-resistant tuberculosis has become more challenging due to an increasing number of multidrug-resistant cases. The recommended diagnostic methods are unable to detect all drug resistance-associated genetic mutations. Artificial intelligence models, particularly Artificial Neural Networks, are commonly employed for predicting drug resistance profiles in tuberculosis.
Tuberculosis (TB) remains one of the most significant global health problems, posing a significant challenge to public health systems worldwide. However, diagnosing drug-resistant tuberculosis (DR-TB) has become increasingly challenging due to the rising number of multidrug-resistant (MDR-TB) cases, despite the development of new TB diagnostic tools. Even the World Health Organization-recommended methods such as Xpert MTB/XDR or Truenat are unable to detect all the Mycobacterium tuberculosis genome mutations associated with drug resistance. While Whole Genome Sequencing offers a more precise DR profile, the lack of user-friendly bioinformatics analysis applications hinders its widespread use. This review focuses on exploring various artificial intelligence models for predicting DR-TB profiles, analyzing relevant English-language articles using the PRISMA methodology through the Covidence platform. Our findings indicate that an Artificial Neural Network is the most commonly employed method, with non-statistical dimensionality reduction techniques preferred over traditional statistical approaches such as Principal Component Analysis or t-distributed Stochastic Neighbor Embedding.
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