ArticleBMC medical imaging2022
Automated detection of pulmonary embolism from CT-angiograms using deep learning.
Article in BMC medical imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accuracy of AI-Based Algorithms in Pulmonary Embolism Detection on Computed Tomographic Pulmonary Angiography: An Updated Systematic Review and Meta-analysis.Journal of imaging informatics in medicine · 2026Pooled 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
- 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
- Efficacy of an Automated Pulmonary Embolism (PE) Detection Algorithm on Routine Contrast-Enhanced Chest CT Imaging for Non-PE Studies.Journal of imaging informatics in medicine · 2026Article
- AI-Based Pulmonary Embolism Detection: The Added Value of a False-Positive Reduction Module over a Region Proposal Network.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-based segmentation of acute pulmonary embolism in cardiac CT images.International journal of computer assisted radiology and surgery · 2026Article
- Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures.Journal of imaging informatics in medicine · 2026Article
- 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
- Benchmarking Two Leading Large Language Models for Pulmonary Embolism Identification on CT Pulmonary Angiography.Cureus · 2025Article
- 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
- Improved pulmonary embolism detection in CT pulmonary angiogram scans with hybrid vision transformers and deep learning techniques.Scientific reports · 2025Article
- Deep learning-based algorithm for automatic detection of incidental pulmonary embolism on contrast-enhanced CT: a multicenter multivendor study.Radiology advances · 2025Article
- Correlating Patient Symptoms and CT Morphology in AI-Detected Incidental Pulmonary Embolisms.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections.European radiology experimental · 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
- Detection and segmentation of pulmonary embolism in 3D CT pulmonary angiography using a threshold adjustment segmentation network.Scientific reports · 2025Article
- Artificial Intelligence for Detecting Pulmonary EmbolismsCurrent medical imaging · 2025Article
- The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection.Frontiers in medicine · 2025Review
- Advancements in Machine Learning and Artificial Intelligence in the Radiological Detection of Pulmonary Embolism.Cureus · 2025Review
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe aim of this study was to develop and evaluate a deep neural network model in the automated detection of pulmonary embolism (PE) from computed tomography pulmonary angiograms (CTPAs) using only weakly labelled training data.
methodsWe developed a deep neural network model consisting of two parts: a convolutional neural network architecture called InceptionResNet V2 and a long-short term memory network to process whole CTPA stacks as sequences of slices. Two versions of the model were created using either chest X-rays (Model A) or natural images (Model B) as pre-training data. We retrospectively collected 600 CTPAs to use in training and validation and 200 CTPAs to use in testing. CTPAs were annotated only with binary labels on both stack- and slice-based levels. Performance of the models was evaluated with ROC and precision-recall curves, specificity, sensitivity, accuracy, as well as positive and negative predictive values.
resultsBoth models performed well on both stack- and slice-based levels. On the stack-based level, Model A reached specificity and sensitivity of 93.5% and 86.6%, respectively, outperforming Model B slightly (specificity 90.7% and sensitivity 83.5%). However, the difference between their ROC AUC scores was not statistically significant (0.94 vs 0.91, p = 0.07).
conclusionsWe show that a deep learning model trained with a relatively small, weakly annotated dataset can achieve excellent performance results in detecting PE from CTPAs.
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