ArticleEuropean radiology2020
Evaluation of acute pulmonary embolism and clot burden on CTPA with deep learning.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 2 of them syntheses that pooled it.
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Who cites it
51 citing papers in PubMed, 2 syntheses or guidelines pooled it, 109 citations in OpenAlex.
- Deep learning applications in visual data for benign and malignant hematologic conditions: a systematic review and visual glossary.Haematologica · 2023Pooled it
- Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis.Scientific reports · 2021Pooled it
- Time efficiency and reliability of established computed tomographic obstruction scores in patients with acute pulmonary embolism.PloS one · 2021Trial
- Attention-Guided 3D Residual Learning for Pulmonary Embolism Segmentation on CT Pulmonary Angiography.Journal of imaging informatics in medicine · 2026Article
- Artificial intelligence in computed tomography imaging for pulmonary embolism: a narrative review from computed tomography pulmonary angiography to non-contrast computed tomography.Quantitative imaging in medicine and surgery · 2026Review
- Commercial artificial intelligence-assisted performance and interpretation time of first-on-call radiology residents using computed tomography pulmonary angiography to detect pulmonary embolism: a multireader, multicenter study.Quantitative imaging in medicine and surgery · 2026Article
- 3D region-growing nnU-Net improves pulmonary embolism detection on CTPA: a dual-cohort validation study.European radiology experimental · 2026Article
- Effects of Clot Burden and Distribution on Pulmonary Embolism Response Team Patients.CHEST pulmonary · 2026Article
- Quantitative assessment of risk stratification of acute pulmonary embolism based on three-dimensional computed tomography bronchography and angiography.Journal of thoracic disease · 2026Article
- Deep learning models for pulmonary embolism segmentation on dual-energy CT: performance analysis and image quality correlation.BMC medical imaging · 2026Article
- Developing a novel deep learning-based model for automatic right ventricular parameters assessment on ctpa in pulmonary embolism.Emergency radiology · 2025Article
- Review
- Comprehensive review of pulmonary embolism imaging: past, present and future innovations in computed tomography (CT) and other diagnostic techniques.Japanese journal of radiology · 2025Review
- Advancements in Acute Pulmonary Embolism Diagnosis and Treatment: A Narrative Review of Emerging Imaging Techniques and Intravascular Interventions.Journal of cardiovascular development and disease · 2025Review
- Deep learning-based algorithm for automatic detection of incidental pulmonary embolism on contrast-enhanced CT: a multicenter multivendor study.Radiology advances · 2025Article
- Developing a deep learning-based imaging diagnostic framework, PVDNet, for differentiating pulmonary artery sarcoma and pulmonary thromboembolism: a multi-center observational study.The Lancet regional health. Western Pacific · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Application of Artificial Intelligence in Thoracic Radiology: A Narrative Review.Tuberculosis and respiratory diseases · 2025Article
- Quantitative assessment and risk stratification of random acute pulmonary embolism cases using a deep learning model based on computed tomography pulmonary angiography images.Quantitative imaging in medicine and surgery · 2025Article
- Prediction of short-term adverse clinical outcomes of acute pulmonary embolism using conventional machine learning and deep Learning based on CTPA images.Journal of thrombosis and thrombolysis · 2025Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
Abstract
objectivesTo take advantage of the deep learning algorithms to detect and calculate clot burden of acute pulmonary embolism (APE) on computed tomographic pulmonary angiography (CTPA). MATERIALS AND
methodsThe training set in this retrospective study consisted of 590 patients (460 with APE and 130 without APE) who underwent CTPA. A fully deep learning convolutional neural network (DL-CNN), called U-Net, was trained for the segmentation of clot. Additionally, an in-house validation set consisted of 288 patients (186 with APE and 102 without APE). In this study, we set different probability thresholds to test the performance of U-Net for the clot detection and selected sensitivity, specificity, and area under the curve (AUC) as the metrics of performance evaluation. Furthermore, we investigated the relationship between the clot burden assessed by the Qanadli score, Mastora score, and other imaging parameters on CTPA and the clot burden calculated by the DL-CNN model.
resultsThere was no statistically significant difference in AUCs with the different probability thresholds. When the probability threshold for segmentation was 0.1, the sensitivity and specificity of U-Net in detecting clot respectively were 94.6% and 76.5% while the AUC was 0.926 (95% CI 0.884-0.968). Moreover, this study displayed that the clot burden measured with U-Net was significantly correlated with the Qanadli score (r = 0.819, p < 0.001), Mastora score (r = 0.874, p < 0.001), and right ventricular functional parameters on CTPA.
conclusionsDL-CNN achieved a high AUC for the detection of pulmonary emboli and can be applied to quantitatively calculate the clot burden of APE patients, which may contribute to reducing the workloads of clinicians. KEY POINTS: • Deep learning can detect APE with a good performance and efficiently calculate the clot burden to reduce the physicians' workload. • Clot burden measured with deep learning highly correlates with Qanadli and Mastora scores of CTPA. • Clot burden measured with deep learning correlates with parameters of right ventricular function on CTPA.
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