AI-Driven Modeling of Mycobacterium tuberculosis Dynamics to Predict Disease Progression: Experimental and Deterministic Approaches

The development of multidrug-resistant Mycobacterium tuberculosis is a serious threat to tuberculosis control programs across the world. Artificial Intelligence (AI) has been considered for its possible contribution to comprehending and combating infectious diseases such as M. tuberculosis. The present work discusses the use of AI by designing experimental and deterministic models for simulating the dynamics of M. tuberculosis infection. The experimental model makes use of molecular biology techniques like gradient PCR, plasmid isolation, and transformation of Escherichia coli cells, including different biological and environmental factors, to forecast disease progression, patient outcomes, and drug resistance patterns. The deterministic model, on the other hand, makes use of computational algorithms to model the spread and dynamics of the pathogen within populations under controlled settings.Machine learning models like SVM, k-NN, and ANN are used to improve predictive accuracy and provide insights into the evolution of M. tuberculosis resistance. The findings illustrate the capability of AI in enhancing therapeutic strategies, diagnostic accuracy, and long-term epidemiological trend prediction, thus contributing towards better tuberculosis management and control strategies.