Metabolomics and Artificial Intelligence (AI) assisted metabolomics in the diagnosis of non-tuberculous mycobacterial infections: progress and challenges.

BACKGROUND: Non tuberculous mycobacterial (NTM) infections are caused in individuals who are immunocompromised or with certain lung conditions. The NTM disease in the lungs, also referred to as NTM-pulmonary disease (NTM-PD), can lead to nodular bronchiectasis or cavitary disease is serious as it can cause respiratory failure or complications like fibrosis or scarring. Thus, NTM infection diagnosis at an earlier stage is important for efficient disease management.

AIM OF REVIEW: This review summarizes the role played by metabolomics and artificial intelligence (AI) assisted metabolomics in enhancing the efficiency of NTM diagnosis.

KEY SCIENTIFIC CONCEPTS OF REVIEW: NTM infections deserve importance as the non-tuberculous mycobacteria are ubiquitous and can cause infections in immunocompromised and those with abnormal lung conditions. Thus, this review elaborates about the current diagnostic methods for NTM infections and their demerits. The potential of metabolomics in providing solutions to the lacunae in current diagnostic methods is also addressed along with the challenges associated with this diagnostic platform. Also, the pivotal role played by the AI based technologies in bolstering the current metabolomic methodologies is also discussed and finally the way forward for harnessing this technology to enhance NTM diagnosis is also explained.