Unified model with random penalty entropy loss for robust nasogastric tube placement analysis in X-ray

Background and objective: An accurate nasogastric (NG) tube placement assessment is essential to prevent serious complications. However, manual chest X-ray verification is prone to human error and variability. We propose a unified deep learning model that jointly performs segmentation and classification to improve the generalization and reliability of automated NG tube placement assessment. Methods: We developed a unified architecture based on nnUNet, which was optimized simultaneously for segmentation and classification. To enhance robustness and reduce overconfidence, we introduce Random Penalty Entropy Loss, which dynamically scales entropy penalties during training. The model was evaluated on internal datasets (5674 chest X-rays from three South Korean hospitals) and an external dataset from MIMIC-CXR. Results: On the internal test set, the proposed model outperformed the Wang 2-Stage method (F1: 93.94% vs. 87.39%), particularly in ambiguous cases. Baseline models using Focal Loss or Label Smoothing performed well internally but showed substantial performance drops and miscalibration externally. In contrast, our model with Random Penalty Entropy Loss achieved the highest external classification accuracy (F1: 66.34%, AUROC: 84.82%) and superior calibration (MCE: 0.429, ECE: 0.274). Conclusion: The proposed unified model surpasses existing two-stage approaches in classification and calibration. Incorporating Random Penalty Entropy Loss improves robustness and generalization across diverse clinical settings. These results highlight the model's potential to reduce diagnostic errors and enhance patient safety in NG tube placement assessment.