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Predicting tuberculosis drug resistance with machine learning-assisted Raman spectroscopy. ArXiv Ogunlade, B., Tadesse, L. F., Li, H., Vu, N., Banaei, N., Barczak, A. K., Saleh, A. A., Prakash, M., Dionne, J. A. 2023

Abstract

Tuberculosis (TB) is the world's deadliest infectious disease, with 1.5 million annual deaths and half a million annual infections. Rapid TB diagnosis and antibiotic susceptibility testing (AST) are critical to improve patient treatment and to reduce the rise of new drug resistance. Here, we develop a rapid, label-free approach to identify Mycobacterium tuberculosis (Mtb) strains and antibiotic-resistant mutants. We collect over 20,000 single-cell Raman spectra from isogenic mycobacterial strains each resistant to one of the four mainstay anti-TB drugs (isoniazid, rifampicin, moxifloxacin and amikacin) and train a machine-learning model on these spectra. On dried TB samples, we achieve > 98% classification accuracy of the antibiotic resistance profile, without the need for antibiotic co-incubation; in dried patient sputum, we achieve average classification accuracies of ~ 79%. We also develop a low-cost, portable Raman microscope suitable for field-deployment of this method in TB-endemic regions.

View details for DOI 10.3390/molecules24244516

View details for PubMedID 37332564

View details for PubMedCentralID PMC10274949