Tuberculosis is the world’s leading killer in the case of infectious diseases, even when medical research offers testimony that it is a preventable and curable disease. To cure TB patients completely to provide relief, an accurate diagnosis should be made at the right time. According to the WHO, in the year 2021, only one in three people infected with the bacterium were diagnosed and hence had access to TB treatment. While this is disturbing, we have good news in TB eradication when it comes to advancing
technology. This blog discusses the role that AI plays in the diagnosis of tuberculosis and how it can pave the way to a TB-free future.

TB-AI: Enhancing Diagnosis Accuracy
A specific AI system called TB-AI is proven to be an invaluable tool for helping medical personnel find TB bacilli. Its purpose is to supplement human expertise rather than replace it, thereby improving diagnostic precision. Here, we explore the crucial part TB-AI plays in the diagnosis procedure to make sure that no prospective cases are missed. An initial screening rather than a final diagnosis is made when TB-AI determines that a sample is positive. The highest degree of accuracy in TB diagnosis is ensured by this combined method between AI and pathologists. Let’s examine the advantages of this collaboration for both patients and healthcare professionals.

AI Methods: Deep Learning and Radiomics
Deep learning and radiomics, two fundamental – AI approaches, are at the forefront of TB diagnosis. Radiomics pulls quantitative information from medical images, whereas deep learning specializes at processing and interpreting them. This section delves into how these AI approaches are used to detect B-related anomalies, giving clinicians vital insights.
Challenges in TB-AI Implementation
While employing AI to detect tuberculosis holds great promise, there are still certain difficulties to overcome. The acquisition of reliable data is a significant challenge. In contrast to computer vision, medical data, particularly for illnesses such as tuberculosis, can be restricted and of variable quality. It takes a lot of effort to create a high-quality dataset for diagnosing tuberculosis. Because the type of information required depends on the unique diagnostic criteria, data compilation is a continual and challenging task.
Takeaway Message
With its unmatched accuracy, speed, and automation, artificial intelligence is ushering in a new era in tuberculosis detection. By utilizing AI, we are better equipped to diagnose tuberculosis, separate it from other lung diseases, and even detect drug resistance. While there are still issues, it is impossible to

ignore the potential advantages of earlier detection and better treatment. As AI develops, it has the potential to be a useful tool in our global efforts to eradicate tuberculosis and enhance pulmonary healthcare. To sum up, the utilization of Artificial Intelligence in the diagnosis of tuberculosis represents a significant stride towards more efficient and accurate healthcare. AI’s ability to swiftly analyses vast datasets and detect subtle patterns can revolutionize TB diagnosis, enabling early intervention and improved patient outcomes. Moreover, it offers a ray of hope in resource-constrained regions where the burden of TB is
particularly high. As AI technologies continue to advance, their integration into healthcare systems holds the promise of not only reducing the impact of tuberculosis but also serving as a model for the innovative use of technology in combating other global health challenges.

References:
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https://jtd.amegroups.org/article/view/19696/html#:~:text=Results%3A%20Examined%20against%20the%20double,and%20help%20make%20clinical%20decisions.
 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9366014/
 https://www.mdpi.com/2079-9292/11/17/2634
 https://www.linkedin.com/pulse/role-artificial-intelligence-biochemical-diagnostic-based-srivastava