An Emerging Artificial Intelligence Tool for the Advancement of Modern Health Care in Tuberculosis

Tuberculosis is the deadly infectious disease from ancient days, but that is still persisting as an unresolved condition in healthcare field across the universe even in the twenty-first century. The detection and treatment of contagious and active TB patients is critical to establishing tuberculosis control and breaking the transmission chain of Mycobacterium tuberculosis [1]. Robert Koch identified Mycobacterium tuberculosis (MTB) in 1882, about 150 years ago. Tuberculosis (TB) is still a dangerous human infection that poses a threat to the entire world. In many places, it is so pervasive that it poses a risk to healthcare workers on the field. TB has historically been one of the top ten causes of death worldwide due to its high incidence including both immune-competent and immune-compromised patients. In 2019, India (27%, 2.8 million cases yearly, 150,000 MDR-TB cases yearly), China (14%), and the Russian Federation (8%), accounted for the biggest proportion of the worldwide burden. According to estimates, around ten million individuals will contract TB globally in 2020. In comparison to levels in 2015, the aims for 2030 include a 90% decrease in TB deaths and an 80% decrease in new cases annually. By 2050, the targets aim for new cases to be at or below 1 per million people [2]. Hence, it is necessary to establish accurate, precise, rapid, and novel diagnostic as well as therapeutic tools for the optimal prevention and eradication of tuberculosis.