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

Abstract

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 A retrospective analysis was conducted on CTPA scans from multiple centers. The dataset was divided into a primary internal cohort (n = 1,368) for model derivation and testing, alongside an independent external cohort (n = 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.