SynthesisScientific reports2021
Deep learning for pulmonary embolism detection on computed tomography pulmonary angiogram: a systematic review and meta-analysis.
Synthesis in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 64 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
64 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
- Application of an artificial intelligence-based tool in [European journal of nuclear medicine and molecular imaging · 2023Trial
- Article
- 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
- External validation of a top-ranked model from the RSNA pulmonary embolism detection challenge: assessment of generalizability.Scientific reports · 2026Article
- Ultra-Low-Dose CTPA Using Sparse Sampling CT Combined with the U-Net for Deep Learning-Based Artifact Reduction: An Exploratory Study.Journal of imaging informatics in medicine · 2026Article
- When Cancer Clots: An Extensive Radiologic Analysis of Cancer-Associated Thromboembolism.Cancers · 2026Review
- 3D region-growing nnU-Net improves pulmonary embolism detection on CTPA: a dual-cohort validation study.European radiology experimental · 2026Article
- Deep learning models for pulmonary embolism segmentation on dual-energy CT: performance analysis and image quality correlation.BMC medical imaging · 2026Article
- AI-Based Pulmonary Embolism Detection: The Added Value of a False-Positive Reduction Module over a Region Proposal Network.Diagnostics (Basel, Switzerland) · 2026Article
- Enhancing diagnostic safety with low iodine, low radiation CTPA classification using deep learning.Scientific reports · 2026Article
- Bridging practice and evidence: insights from the Scandinavian Society of Anaesthesiology and Intensive Care Medicine and Saudi Critical Care Society Guidelines on trauma-related VTE.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Review
- Artificial Intelligence-Driven Integration of ECG and Molecular Biomarkers in Pulmonary Embolism.International journal of molecular sciences · 2026Review
- A deep learning model for the interpretable identification of pulmonary thromboembolism from computed tomography pulmonary angiography.Frontiers in cardiovascular medicine · 2026Article
- Lesion feature enhancement and boundary-aware fusion for pulmonary embolism recognition on CT images.Frontiers in medicine · 2026Article
- External validation of a commercial AI system for pulmonary embolism detection on chest CTPA: a multicenter study.Frontiers in molecular biosciences · 2026Article
- Appropriateness of CT Pulmonary Angiography Requests at Croydon University Hospital: An Audit.Cureus · 2025Article
- Impact of an Artificial Intelligence-Guided Pulmonary Embolism Response Team (AIPERT) on Patient Transfers, Diagnosis, and Management: A Healthcare System Experience.Clinics and practice · 2025Article
- Improvement of artificial intelligence-based computed tomography pulmonary angiography in identifying acute pulmonary embolism.Quantitative imaging in medicine and surgery · 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
4 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computed tomographic pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis. However, this diagnosis is susceptible to misdiagnosis. In this study, we aimed to perform a systematic review of current literature applying deep learning for the diagnosis of PE on CTPA. MEDLINE/PUBMED were searched for studies that reported on the accuracy of deep learning algorithms for PE on CTPA. The risk of bias was evaluated using the QUADAS-2 tool. Pooled sensitivity and specificity were calculated. Summary receiver operating characteristic curves were plotted. Seven studies met our inclusion criteria. A total of 36,847 CTPA studies were analyzed. All studies were retrospective. Five studies provided enough data to calculate summary estimates. The pooled sensitivity and specificity for PE detection were 0.88 (95% CI 0.803-0.927) and 0.86 (95% CI 0.756-0.924), respectively. Most studies had a high risk of bias. Our study suggests that deep learning models can detect PE on CTPA with satisfactory sensitivity and an acceptable number of false positive cases. Yet, these are only preliminary retrospective works, indicating the need for future research to determine the clinical impact of automated PE detection on patient care. Deep learning models are gradually being implemented in hospital systems, and it is important to understand the strengths and limitations of these algorithms.
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