Objectives To 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). Methods A 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. Results Compared 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). Conclusion Automated AI-driven CTPA analysis provides a dependable approach for evaluating patients with CTEPH, CTED, and normal controls, demonstrating excellent consistency and efficiency. Key Points <Emphasis Type="BoldItalic">Question</Emphasis> Guidelines do not advocate for applying treatment protocols for CTEPH to patients with CTED; early detection of the condition is crucial. <Emphasis Type="BoldItalic">Findings</Emphasis> Automated CTPA analysis was feasible in 100% of patients with good agreement and would have added information for early detection and identification. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> 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. Graphical Abstract

Diagnostic value of fully automated CT pulmonary angiography in patients with chronic thromboembolic pulmonary hypertension and chronic thromboembolic disease

期刊论文
发表日期：

2025年5月20日

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Yue Lin,
Miao Li,
Sheng Xie

摘要

Objectives

To 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).

Methods

A 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.

Results

Compared 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).

Conclusion

Automated 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.

Graphical Abstract