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 with clinical parameters to perform prognosis analysis, predicting the progression of melioidosis pneumonia patients to severe or critical illness. Correlation analysis revealed that lung injury was associated with clinical markers from other organ systems, indicating an interrelationship between lung lesions and systemic health. Using a multilayer perceptron classifier, which combined CT lesion characteristics with clinical parameters, the model predicted the progression to severe or critical illness with an AUC of 0.9570 (95% CI: 0.9262-0.9816). This study demonstrated that the CT lesion characteristics of melioidosis pneumonia are correlated with indicators of multi-organ function. Combining CT lesion characteristics with clinical parameters improves the efficiency of prognosis prediction for melioidosis pneumonia. Lung injury CT lesion characteristics were identified as primary markers for predicting the disease prognosis.

Keywords: Computed tomography; Melioidosis pneumonia; Prognosis prediction; Quantitative measurement.