An interpretable machine learning approach to prognosis of melioidosis pneumonia via computed tomography quantification and clinical data.

ABSTRACT: This study aimed to develop a dataset comprising computed tomography (CT) images and clinical data for melioidosis pneumonia and to utilize machine learning for assisting in prognosis prediction of the disease. We retrospectively analyzed multicenter data from five hospitals to establish a dataset for diagnosing melioidosis pneumonia, including CT images and clinical data. An AI-based CT segmentation system was employed to extract lung tissue and lesions. Quantitative analysis methods were used to derive CT lesion characteristics specific to melioidosis pneumonia. Pearson and Spearman correlation tests were performed to examine the relationships between CT lesion characteristics and clinical parameters. Finally, a machine learning method was applied by combining CT lesion characteristics w

SUBMITTER: Wu Y 

PROVIDER: S-EPMC12660668 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature