Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays

BackgroundCorrectly diagnosing and accurately distinguishing mycoplasma pneumonia in children has consistently posed a challenge in clinical practice, as it can directly impact the prognosis of affected children. To address this issue, we analyzed chest X-rays (CXR) using various deep learning models to diagnose pediatric mycoplasma pneumonia.MethodsWe collected 578 cases of children with mycoplasma infection and 191 cases of children with virus infection, with available CXR sets. Three deep convolutional neural networks (ResNet50, DenseNet121, and EfficientNetv2-S) were used to distinguish mycoplasma pneumonia from viral pneumonia based on CXR. Accuracy, area under the curve (AUC), sensitivity, and specificity were used to evaluate the performance of the model. Visualization was also achieved through the use of Class Activation Mapping (CAM), providing more transparent and interpretable classification results.ResultsOf the three models evaluated, ResNet50 outperformed the others. Pretrained with the ZhangLabData dataset, the ResNet50 model achieved 80.00% accuracy in the validation set. The model also showed robustness in two test sets, with accuracy of 82.65 and 83.27%, and AUC values of 0.822 and 0.758. In the test results using ImageNet pre-training weights, the accuracy of the ResNet50 model in the validation set was 80.00%; the accuracy in the two test sets was 81.63 and 62.91%; and the corresponding AUC values were 0.851 and 0.776. The sensitivity values were 0.884 and 0.595, and the specificity values were 0.655 and 0.814.ConclusionsThis study demonstrates that deep convolutional networks utilizing transfer learning are effective in detecting mycoplasma pneumonia based on chest X-rays (CXR). This suggests that, in the near future, such computer-aided detection approaches can be employed for the early screening of pneumonia pathogens. This has significant clinical implications for the rapid diagnosis and appropriate medical intervention of pneumonia, potentially enhancing the prognosis for affected children.