ArticleEuropean radiology2022
How artificial intelligence improves radiological interpretation in suspected pulmonary embolism.
Article in European radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 2 of them syntheses that pooled it.
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Who cites it
60 citing papers in PubMed, 2 syntheses or guidelines pooled it, 99 citations in OpenAlex.
- 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
- Facilitators and Barriers to Implementing AI in Routine Medical Imaging: Systematic Review and Qualitative Analysis.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence for pulmonary embolism detection: Is it comparable to radiology residents?European journal of radiology open · 2026Article
- Improved computer-aided detection performance for pulmonary embolism on CTPA with higher noise reduction reconstruction.Emergency radiology · 2026Article
- External validation of a top-ranked model from the RSNA pulmonary embolism detection challenge: assessment of generalizability.Scientific reports · 2026Article
- A Stepwise Approach to Computed Tomography Imaging of Pulmonary Hypertension.The Indian journal of radiology & imaging · 2026Review
- Interventional Management of Intermediate-High-Risk Pulmonary Embolism: Current Evidence, Patient Selection and Personalised Treatment Strategies.Journal of clinical medicine · 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
- Automated right ventricle-to-left ventricle diameter ratio predicts ICU stay for acute pulmonary embolism on CTPA examinations in the emergency department.Cardiovascular diagnosis and therapy · 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
- Computed tomography pulmonary angiography in around-the-clock clinical care with individualised scan protocols: a 5-year observational study on incidence and causes of repeat scanning.European radiology · 2026Observational
- An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification.Scientific reports · 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
- Development of a deep learning model for intussusception using point-of-care ultrasound.Frontiers in radiology · 2026Article
- Artificial intelligence in medicine: Current applications in cardiology, oncology, and radiology.World journal of methodology · 2025Review
- Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.International journal of emergency medicine · 2025Review
- Pulmonary Embolism Detection in Unenhanced CT Exams: A Radiomic-Based Classifier as a Promising Screening-Diagnostic Tool.Journal of personalized medicine · 2025Article
- Review
- Improvement of artificial intelligence-based computed tomography pulmonary angiography in identifying acute pulmonary embolism.Quantitative imaging in medicine and surgery · 2025Article
Corrections and comments
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo evaluate and compare the diagnostic performances of a commercialized artificial intelligence (AI) algorithm for diagnosing pulmonary embolism (PE) on CT pulmonary angiogram (CTPA) with those of emergency radiologists in routine clinical practice.
methodsThis was an IRB-approved retrospective multicentric study including patients with suspected PE from September to December 2019 (i.e., during a preliminary evaluation period of an approved AI algorithm). CTPA quality and conclusions by emergency radiologists were retrieved from radiological reports. The gold standard was a retrospective review of CTPA, radiological and clinical reports, AI outputs, and patient outcomes. Diagnostic performance metrics for AI and radiologists were assessed in the entire cohort and depending on CTPA quality.
resultsOverall, 1202 patients were included (median age: 66.2 years). PE prevalence was 15.8% (190/1202). The AI algorithm detected 219 suspicious PEs, of which 176 were true PEs, including 19 true PEs missed by radiologists. In the cohort, the highest sensitivity and negative predictive values (NPVs) were obtained with AI (92.6% versus 90% and 98.6% versus 98.1%, respectively), while the highest specificity and positive predictive value (PPV) were found with radiologists (99.1% versus 95.8% and 95% versus 80.4%, respectively). Accuracy, specificity, and PPV were significantly higher for radiologists except in subcohorts with poor-to-average injection quality. Radiologists positively evaluated the AI algorithm to improve their diagnostic comfort (55/79 [69.6%]).
conclusionInstead of replacing radiologists, AI for PE detection appears to be a safety net in emergency radiology practice due to high sensitivity and NPV, thereby increasing the self-confidence of radiologists. KEY POINTS: • Both the AI algorithm and emergency radiologists showed excellent performance in diagnosing PE on CTPA (sensitivity and specificity ≥ 90%; accuracy ≥ 95%). • The AI algorithm for PE detection can help increase the sensitivity and NPV of emergency radiologists in clinical practice, especially in cases of poor-to-moderate injection quality. • Emergency radiologists recommended the use of AI for PE detection in satisfaction surveys to increase their confidence and comfort in their final diagnosis.
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