A decision tree for predicting the causative pathogens of community-acquired pneumonia from thin-section computed tomography

To determine whether decision trees are useful for predicting organisms that cause community-acquired pneumonia (CAP). We developed a decision tree for predicting the organisms that cause CAP based on previously reported characteristic computed tomography findings. Sixteen readers (two student doctors, six residents, and eight radiologists) separately diagnosed 68 randomly selected cases of CAP using chest computed tomography. The first, second, and third most likely causative organisms were estimated for each case, and the percentages of correct answers were evaluated for consistency with the isolated organisms. The same 68 cases were then read again using the decision tree, with the first three most likely organisms again being estimated, and the percentage of agreement was evaluated as the percentage of correct responses after using the decision tree. For student doctors, residents, and radiologists, the percentage of correct responses increased significantly (p