Integrating Artificial Intelligence for Enhanced Tuberculosis Diagnosis and Management: A Comprehensive Analysis

An infectious disease called tuberculosis (TB) is primarily lung-related and is caused by a particular kind of bacterium known as Mycobacterium tuberculosis. When sick people cough, sneeze, or spit, the infection spreads via the air. It is possible to prevent and treat tuberculosis, and it is believed that the TB bacteria has infected about 25% of the world’s population. In the end, 5–10% of TB-infected individuals will have symptoms and develop TB illness. It cannot be spread by those who are infected but not (yet) unwell with the illness. Antibiotics are typically used to treat tuberculosis (TB), which can be lethal if left untreated. The Bacille Calmette-Guérin (BCG) vaccine is administered to infants and young children in some nations to prevent tuberculosis. The vaccine outside of the lungs prevents TB but not within the lungs. Machine learning is gradually being used in the field of disease diagnosis. One method uses machine learning algorithms to analyze medical images, such as MRI, CT, and X-rays. These algorithms can automatically identify image patterns and features indicative of certain diseases, such as tumors or abnormalities. This can aid medical professionals and radiologists diagnose patients more precisely and quickly. Machine learning is also being applied to the diagnosis of diseases by analyzing patient data, including demographics, lab findings, and electronic medical records. Machine learning algorithms can analyze this data to find trends and risk factors linked to certain diseases. In addition to establishing prediction models that can assist in identifying individuals at a greater risk of contracting specific diseases, this can assist medical professionals in making better-educated decisions regarding diagnosis and treatment. The application of machine learning to illness diagnosis has promise for raising diagnostic precision and efficacy while assisting medical professionals in reaching better treatment options. The procedures for utilizing AI/ML to solve the disease diagnosis problem are covered in this chapter.