Frontiers in cardiovascular medicine · 2026
A deep learning model for the interpretable identification of pulmonary thromboembolism from computed tomography pulmonary angiography.
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Li Yemei, Chen Sunyu, Chen Guangkun, Ren Baosheng, Hu Shibing, Zong Min, Jia Zhongzhi, Xue Tongqing, Feng Ganzhu, Li Dancen
Аннотация
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.
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.