ArticleScientific reports2022
A multitask deep learning approach for pulmonary embolism detection and identification.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 58 citations in OpenAlex.
- A systematic review of artificial intelligence tools for chronic pulmonary embolism on CT pulmonary angiography.Frontiers in radiology · 2024Pooled it
- 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
- Harnessing hybrid stacking ensemble learning for accurate pulmonary embolism diagnosis using tabular clinical data.Scientific reports · 2026Article
- 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
- Deep learning-based identification of chronic pulmonary embolism on CTPA: a regional lung analysis using multiplanar MIP images.European radiology experimental · 2026Article
- 3D region-growing nnU-Net improves pulmonary embolism detection on CTPA: a dual-cohort validation study.European radiology experimental · 2026Article
- Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures.Journal of imaging informatics in medicine · 2026Article
- Deep learning-based segmentation of acute pulmonary embolism in cardiac CT images.International journal of computer assisted radiology and surgery · 2026Article
- Lesion feature enhancement and boundary-aware fusion for pulmonary embolism recognition on CT images.Frontiers in medicine · 2026Article
- Tooth-to-white spot lesion YOLO: a novel model for white spot lesion detection.BMC oral health · 2025Article
- AI-Enhanced Deep Learning Framework for Pulmonary Embolism Detection in CT Angiography.Bioengineering (Basel, Switzerland) · 2025Article
- A novel multimodal computer-aided diagnostic model for pulmonary embolism based on hybrid transformer-CNN and tabular transformer.Physical and engineering sciences in medicine · 2025Article
- Comparing efficiency of an attention-based deep learning network with contemporary radiological workflow for pulmonary embolism detection on CTPA: A retrospective study.European journal of radiology open · 2025Article
- Prediction of pulmonary embolism by an explainable machine learning approach in the real world.Scientific reports · 2025Article
- Advancements in Machine Learning and Artificial Intelligence in the Radiological Detection of Pulmonary Embolism.Cureus · 2025Review
- Machine Learning-Based Objective Evaluation Model of CTPA Image Quality: A Multi-Center Study.International journal of general medicine · 2025Article
- The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection.Frontiers in medicine · 2025Review
- Improved detection of small pulmonary embolism on unenhanced computed tomography using an artificial intelligence-based algorithm - a single centre retrospective study.The international journal of cardiovascular imaging · 2024Article
- Enhanced classification performance using deep learning based segmentation for pulmonary embolism detection in CT angiography.Heliyon · 2024Article
- Improving the radiological diagnosis of hepatic artery thrombosis after liver transplantation: Current approaches and future challenges.World journal of transplantation · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Pulmonary embolism (PE) is a blood clot traveling to the lungs and is associated with substantial morbidity and mortality. Therefore, rapid diagnoses and treatments are essential. Chest computed tomographic pulmonary angiogram (CTPA) is the gold standard for PE diagnoses. Deep learning can enhance the radiologists'workflow by identifying PE using CTPA, which helps to prioritize important cases and hasten the diagnoses for at-risk patients. In this study, we propose a two-phase multitask learning method that can recognize the presence of PE and its properties such as the position, whether acute or chronic, and the corresponding right-to-left ventricle diameter (RV/LV) ratio, thereby reducing false-negative diagnoses. Trained on the RSNA-STR Pulmonary Embolism CT Dataset, our model demonstrates promising PE detection performances on the hold-out test set with the window-level AUROC achieving 0.93 and the sensitivity being 0.86 with a specificity of 0.85, which is competitive with the radiologists'sensitivities ranging from 0.67 to 0.87 with specificities of 0.89-0.99. In addition, our model provides interpretability through attention weight heatmaps and gradient-weighted class activation mapping (Grad-CAM). Our proposed deep learning model could predict PE existence and other properties of existing cases, which could be applied to practical assistance for PE diagnosis.
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Registered trials
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