Machine Learning in Tuberculosis: Advancements in Diagnostics, Drug Resistance Prediction, and Prognosis
DOI:
https://doi.org/10.64229/0x7z9373Keywords:
Tuberculosis, Machine learning, Artificial intelligence, Biomarkers, Drug resistance, Whole-genome sequencing, Radiomics, PrognosisAbstract
Tuberculosis (TB) continues to challenge global health and has become much worse due to the complexities associated with diagnosis, the need to perform time-consuming culture-based drug susceptibility testing, and the emergence of drug-resistant strains. The integration of machine learning (ML) and Artificial Intelligence (AI) has transformed how we computationally approach TB control, providing approaches to use ML and AI as tools to assist with the entire scope of the disease, rapidly and accurately, without the need for invasive methods. This review highlights the recent developments in the application of ML models and highlights their potential use as diagnostic biomarkers (e.g., using host-gene-expression, metabolomics, and spectrographic data), predicting drug resistance from genomic sequencing (Whole-Genome Sequencing), and predicting patient prognosis and treatment outcome. Although there are many different algorithms that can produce predictive results, further work in developing capabilities for model interpretation and performing external validation using diverse cohorts from around the world is needed to enable the use of these novel tools to be incorporated into everyday clinical use; thereby supporting continued efforts to achieve global eradication of TB.
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