[A bidirectional feature mapping classification model based on multi-constrained latent representation learning for differential diagnosis of pneumonia].

OBJECTIVES

To develop a bidirectional feature-mapping classification model for differential diagnosis of pneumonia.

METHODS

We collected chest X-ray (CXR) images from 1457 patients with pneumonia and 1456 healthy individuals. Radiomic features extracted from the segmentation masks using PyRadiomics were mapped into a latent shared space to construct the classification model using the bidirectional feature mapping classification model based on multi-constrained latent representation learning. The performance of the constructed model for differential diagnosis of pneumonia was evaluated using 5-fold cross-validation and compared with other feature-based classification models. Decision curve analysis was used for evaluating clinical utility of the model, and ablation experiments were performed to assess the contribution of each constraint module. The importance of the radiomics features was interpreted using the SHAP method, and a two-dimensional visualization experiment of the low-dimensional latent features obtained through the proposed mapping method was conducted to verify the feasibility and effectiveness of the model.

RESULTS

The 5-fold cross-validation results showed that the proposed classification model had a positive predictive value of 0.796, a negative predictive value of 0.830, a specificity of 0.784, a sensitivity of 0.830, an accuracy of 0.811, and an area under the ROC curve of 0.893 for differential diagnosis of pneumonia. Decision curve analysis demonstrated a high net clinical benefit of the model within acceptable threshold probabilities. Ablation studies confirmed the essential role of the multi-constraint module, and the SHAP analysis revealed that the model focused primarily on clinically meaningful and medically interpretable features. The feature mapping method exhibited excellent performance in visual experiments to confirm the effectiveness of the proposed model.

CONCLUSIONS

The proposed bidirectional feature mapping classification model demonstrates strong discriminative capability and high potential for differential diagnosis of pneumonia and shows obvious advantages over other classification models in pneumonia classification tasks.