A deep learning model for the interpretable identification of pulmonary thromboembolism from computed tomography pulmonary angiography

Objective: The rapid identification of pulmonary thromboembolism (PTE) on computed tomography pulmonary angiography (CTPA) is vital but labor-intensive, often leading to diagnostic delays. We aimed to construct and evaluate a YOLOv11 object detection algorithm capable of automatically highlighting intraluminal filling defects to expedite emergency radiological workflows. Methods: = 98) to assess generalizability. The diagnostic efficacy of the YOLOv11 architecture was quantified using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Additionally, gradient-weighted class activation mapping (Grad-CAM) was applied to map the spatial distribution of the model's focus, ensuring clinical transparency. Results: During internal testing, the proposed framework yielded an AUC of 0.777 [95% confidence interval (CI): 0.765-0.788], corresponding to a sensitivity of 74.53% and a specificity of 64.26%. When applied to the external cohort, the algorithm's discriminative ability remained consistent with an AUC of 0.778 (95% CI: 0.749-0.806). Notably, the external sensitivity reached 86.75% (specificity: 54.46%). Visual assessments via Grad-CAM saliency maps confirmed that the model accurately localized embolic occlusions within the complex pulmonary arterial tree. Conclusion: Utilizing the YOLOv11 architecture for automated CTPA analysis yields a highly sensitive and visually interpretable screening mechanism. This artificial intelligence-assisted approach holds substantial promise for reducing missed diagnoses and accelerating patient triage in acute clinical settings.