Mejora de la seguridad diagnóstica con clasificación CTPA de baja radiación y bajo yodo mediante aprendizaje profundo

Pulmonary embolism (PE) is a life-threatening condition for which computed tomography pulmonary angiography (CTPA) is the standard diagnostic modality. However, conventional CTPA protocols require relatively high iodine contrast and radiation doses, raising concerns about renal injury and radiation exposure. In this study, we propose a deep learning-based framework for PE diagnosis under low-iodine and low-radiation CTPA conditions. The proposed two-stage framework integrates image enhancement and classification by jointly leveraging original low-exposure images and their super-resolved counterparts. We further construct and publicly release a low-iodine, low-radiation CTPA dataset developed in collaboration with a clinical institution to support reproducible research in safe imaging. Experimental results demonstrate that the proposed method substantially improves diagnostic performance compared with single-branch baselines, achieving an area under the ROC curve (AUC) of 0.928 while maintaining balanced sensitivity and specificity. These findings suggest that the proposed framework enables accurate and safer PE diagnosis under reduced contrast and radiation exposure, offering a practical solution for improving diagnostic safety in clinical CTPA imaging.