Tuberculosis Treatment

Tuberculosis (TB) treatment research focuses on improving diagnosis and treatment outcomes, particularly in resource-limited settings. Current efforts leverage machine learning, employing deep convolutional neural networks (CNNs), recurrent neural networks (RNNs), and ensemble methods to analyze chest X-rays, cough audio, and other patient data for accurate and rapid TB detection and prediction of treatment adherence. These advancements aim to enhance early diagnosis, personalize treatment strategies, and ultimately reduce TB mortality and morbidity globally, particularly in underserved populations.