ArticleDiagnostics (Basel, Switzerland)2023
Deep Learning-Based Algorithm for Automatic Detection of Pulmonary Embolism in Chest CT Angiograms.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it, 40 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
- 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
- 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
- Emergency Department Prediction of In-Hospital Mortality in Suspected Pulmonary Embolism: An Explainable Machine Learning Approach.Journal of clinical medicine · 2026Article
- Advancing Pulmonary Embolism Detection with Integrated Deep Learning Architectures.Journal of imaging informatics in medicine · 2026Article
- Comprehensive review of pulmonary embolism imaging: past, present and future innovations in computed tomography (CT) and other diagnostic techniques.Japanese journal of radiology · 2025Review
- 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
- 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
- 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
- The promise and limitations of artificial intelligence in CTPA-based pulmonary embolism detection.Frontiers in medicine · 2025Review
- Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization.Emergency radiology · 2024Review
- Research progress in interventional therapy for acute intermediate-high-risk and high-risk pulmonary embolism.Journal of thoracic disease · 2024Review
- Article
- Enhancing Radiological Diagnosis: A Comprehensive Review of Image Quality Assessment and Optimization Strategies.Cureus · 2024Review
- An Enhanced Mask R-CNN Approach for Pulmonary Embolism Detection and Segmentation.Diagnostics (Basel, Switzerland) · 2024Article
- Improving the radiological diagnosis of hepatic artery thrombosis after liver transplantation: Current approaches and future challenges.World journal of transplantation · 2024Article
- Cardiovascular Computed Tomography in the Diagnosis of Cardiovascular Disease: Beyond Lumen Assessment.Journal of cardiovascular development and disease · 2024Review
- New Diagnostic Tools for Pulmonary Embolism Detection.Methodist DeBakey cardiovascular journal · 2024Review
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeSince the prompt recognition of acute pulmonary embolism (PE) and the immediate initiation of treatment can significantly reduce the risk of death, we developed a deep learning (DL)-based application aimed to automatically detect PEs on chest computed tomography angiograms (CTAs) and alert radiologists for an urgent interpretation. Convolutional neural networks (CNNs) were used to design the application. The associated algorithm used a hybrid 3D/2D UNet topology. The training phase was performed on datasets adequately distributed in terms of vendors, patient age, slice thickness, and kVp. The objective of this study was to validate the performance of the algorithm in detecting suspected PEs on CTAs.
methodsThe validation dataset included 387 anonymized real-world chest CTAs from multiple clinical sites (228 U.S. cities). The data were acquired on 41 different scanner models from five different scanner makers. The ground truth (presence or absence of PE on CTA images) was established by three independent U.S. board-certified radiologists.
resultsThe algorithm correctly identified 170 of 186 exams positive for PE (sensitivity 91.4% [95% CI: 86.4-95.0%]) and 184 of 201 exams negative for PE (specificity 91.5% [95% CI: 86.8-95.0%]), leading to an accuracy of 91.5%. False negative cases were either chronic PEs or PEs at the limit of subsegmental arteries and close to partial volume effect artifacts. Most of the false positive findings were due to contrast agent-related fluid artifacts, pulmonary veins, and lymph nodes.
conclusionsThe DL-based algorithm has a high degree of diagnostic accuracy with balanced sensitivity and specificity for the detection of PE on CTAs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.