ArticleEuropean radiology2025
Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease.
Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Small vessel disease in chronic thromboembolic pulmonary hypertension: a comprehensive narrative review of multimodality imaging evaluation.Cardiovascular diagnosis and therapy · 2026Review
- Deep learning-based identification of chronic pulmonary embolism on CTPA: a regional lung analysis using multiplanar MIP images.European radiology experimental · 2026Article
- Current Perspectives on Balloon Pulmonary Angioplasty for Chronic Thromboembolic Pulmonary Hypertension.Journal of clinical medicine · 2025Review
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Authors and funding
3 authors.
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Abstract
objectivesTo evaluate the value of employing artificial intelligence (AI)-assisted CT pulmonary angiography (CTPA) for patients with chronic thromboembolic pulmonary hypertension (CTEPH) and chronic thromboembolic disease (CTED).
methodsA single-center, retrospective analysis of 350 sequential patients with right heart catheterization (RHC)-confirmed CTEPH, CTED, and normal controls was conducted. Parameters such as the main pulmonary artery diameter (MPAd), the ratio of MPA to ascending aorta diameter (MPAd/AAd), the ratio of right to left ventricle diameter (RVd/LVd), and the ratio of RV to LV volume (RVv/LVv) were evaluated using automated AI software and compared with manual analysis. The reliability was assessed through an intraclass correlation coefficient (ICC) analysis. The diagnostic accuracy was determined using receiver-operating characteristic (ROC) curves.
resultsCompared to CTED and control groups, CTEPH patients were significantly more likely to have elevated automatic CTPA metrics (all p < 0.001, respectively). Automated MPAd, MPAd/Aad, and RVv/LVv had a strong correlation with mPAP (r = 0.952, 0.904, and 0.815, respectively, all p < 0.001). The automated and manual CTPA analyses showed strong concordance. For the CTEPH and CTED categories, the optimal area under the curve (AU-ROC) reached 0.939 (CI: 0.908-0.969). In the CTEPH and control groups, the best AU-ROC was 0.970 (CI: 0.953-0.988). In the CTED and control groups, the best AU-ROC was 0.782 (CI: 0.724-0.840).
conclusionAutomated AI-driven CTPA analysis provides a dependable approach for evaluating patients with CTEPH, CTED, and normal controls, demonstrating excellent consistency and efficiency. KEY POINTS: Question Guidelines do not advocate for applying treatment protocols for CTEPH to patients with CTED; early detection of the condition is crucial. Findings Automated CTPA analysis was feasible in 100% of patients with good agreement and would have added information for early detection and identification. Clinical relevance Automated AI-driven CTPA analysis provides a reliable approach demonstrating excellent consistency and efficiency. Additionally, these noninvasive imaging findings may aid in treatment stratification and determining optimal intervention directed by RHC.
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