Key result
VB-Net deep learning model detects pulmonary embolism on CTPA with ~95% sensitivity.
Why the study?
Semi-quantitative clot burden scoring on CTPA aids risk stratification and prognosis in acute PE, but calculation difficulty limits its clinical application.
Does a VB-Net deep learning model accurately detect pulmonary embolism and automatically calculate clot burden scores from CTPA images compared to manual assessment?
Observational (n=2,424)
No
Does a VB-Net deep learning model accurately detect pulmonary embolism and automatically calculate clot burden scores from CTPA images compared to manual assessment?
A deep learning model can accurately detect acute pulmonary embolism and automatically calculate clot burden scores from CTPA images, showing strong correlation with manual expert assessment.
Answers are grounded in this paper and cite their sources.
Supports automated PE detection on CTPA; leaves open prospective validation before clinical adoption.
Background: Computed tomography pulmonary angiography (CTPA) is the gold standard for the diagnosis of pulmonary embolism (PE). The semi-quantitative clot burden scoring based on imaging is related to the risk stratification and prognosis of acute PE, but it cannot be widely applied in the clinic due to the difficulty of calculation. This study developed a high-quality VB-Net deep learning (DL) model combined with Transformer, which can detect PE from images and automatically calculate the clot burden score (CBS). The aim of this study was to help patients via earlier diagnosis, risk stratification, and determination of treatment plans, thereby improving prognosis, as well as alleviate the burden on radiologists. To our knowledge, no related studies have been reported. Methods: A retrospective inclusion of 2,424 CTPA examination cases (44% male) were conducted to train and test the VB-Net DL model for the detection of PE and to evaluate the clot burden volume and scoring. Area under the curve (AUC), and sensitivity and specificity on the case or clot level were used to evaluate the model's performance. Random CTPA data from Zhongshan Hospital Affiliated to Fudan University (30 cases with acute PE, 40 cases without PE) were applied to test the relationship between the clot burden automatically calculated by the model and the Qanadli score determined manually, as well as other imaging parameters. Results: The performance of the VB-Net DL model on the testing set had an AUC of 0.972 based on the case level. The sensitivity at the operational point of the model threshold selected was 94.6% [95% confidence interval (CI): 0.8650-0.9828], and the specificity was 89.4% (95% CI: 0.8407-0.9308). In the random CTPA examinations from this research center, the model's sensitivity based on the case was 76.67% (95% CI: 0.5880-0.8848), the specificity was 95.00% (95% CI: 0.8261-0.9950), the positive predictive value (PPV) was 92.00%, and the accuracy was 87.14%. On the clot-based level, the sensitivity was 84.43%, the PPV was 87.29%, and the false positive rate was 0.19 per case. The clot burden volume and score automatically measured by the model were significantly correlated with the manually determined Qanadli score (r=0.866, P<0.001 and r=0.899, P<0.001, respectively). The severity grading of the CBS groups was consistent with the degree of right ventricular dilation. Conclusions: The VB-Net DL model based on CTPA could conveniently and efficiently detect and quantitatively evaluate PE.
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Yang et al. (2025) conducted an observational in Acute pulmonary embolism (n=2,424). VB-Net deep learning model vs. Manual diagnosis and Qanadli scoring was evaluated on Sensitivity for detecting pulmonary embolism on the testing set (95% CI 0.8650-0.9828). The VB-Net deep learning model detected pulmonary embolism on CTPA images with an AUC of 0.972, achieving 94.6% sensitivity and 89.4% specificity on the testing set.
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