Machine learning and radiomics for predicting therapeutic efficacy in newly diagnosed sputum-negative pulmonary tuberculosis: a retrospective study

Machine learning models based on radiomic features extracted from various CT signs demonstrate potential for predicting the therapeutic efficacy in newly diagnosed pulmonary tuberculosis patients after intensive therapy, providing effective guidance for subsequent treatment.