Machine learning algorithms to predict treatment success for patients with pulmonary tuberculosis.

Despite advancements in detection and treatment, tuberculosis (TB), an infectious illness caused by the Mycobacterium TB bacteria, continues to pose a serious threat to world health. The TB diagnosis phase includes a patient's medical history, physical examination, chest X-rays, and laboratory procedures, such as molecular testing and sputum culture. In artificial intelligence (AI), machine learning (ML) is an advanced study of statistical algorithms that can learn from historical data and generalize the results to unseen data.

4-month moxifloxacin containing regimens in the treatment of patients with sputum-positive pulmonary tuberculosis in South India - a randomised clinical trial.

Background: Shortening tuberculosis (TB) treatment duration is a research priority. We tested the efficacy and safety of 3- and 4-month regimens containing moxifloxacin in a randomised clinical trial in pulmonary TB (PTB) patients in South India.

Methods: New, sputum-positive, adult, HIV-negative, non-diabetic PTB patients were randomised to 3- or 4-month moxifloxacin regimens [moxifloxacin (M), isoniazid (H), rifampicin (R), pyrazinamide (Z) and ethambutol (E)] or to a control regimen (2H R Z E /4R H ) [C].