ArticleDiagnostics (Basel, Switzerland)2022
Assessing the Accuracy of an Artificial Intelligence-Based Segmentation Algorithm for the Thoracic Aorta in Computed Tomography Applications.
Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- TotalSegmentator-AI for aortic segmentation in CT: Reliable performance in normal anatomy but limited utility in pathological aortic disease.European journal of radiology open · 2026Article
- Automated Aortic Quantification Based on Artificial Intelligence: Validation Using Contrast-Enhanced and Non-Contrast CT Scans from the Same Session.Bioengineering (Basel, Switzerland) · 2026Article
- Unsupervised cluster analysis identifies risk profiles driving heterogeneity and survival patterns in aortic aneurysm patients.Scientific reports · 2026Article
- Emerging technologies in thoracic surgery: artificial intelligence, robotic-assisted surgery, and telesurgery.Frontiers in surgery · 2026Review
- Automatic Diagnosis, Classification, and Segmentation of Abdominal Aortic Aneurysm and Dissection from Computed Tomography Images.Diagnostics (Basel, Switzerland) · 2025Article
- Aorta unveiled: the crucial role of imaging in diagnosing and managing aortic disease-a review.European heart journal. Imaging methods and practice · 2025Review
- Artificial intelligence in aortic CT angiography: current applications and future perspectives.Frontiers in cardiovascular medicine · 2025Review
- Artificial intelligence-powered solutions for automated aortic diameter measurement in computed tomography: a narrative review.Annals of translational medicine · 2024Review
- Thoracic Endovascular Aortic Repair and Endovascular Aneurysm Repair Approaches for Managing Aortic Pathologies: A Retrospective Cohort Study.Journal of clinical medicine · 2024Article
- Assessing the role of an artificial intelligence assessment tool for thoracic aorta diameter on routine chest CT.The British journal of radiology · 2023Article
- Sizing the Frozen Elephant Trunk Based on Aortic Pathology and the Importance of Pre-Operative Imaging.Journal of clinical medicine · 2023Article
- Machine Learning-Based Segmentation of the Thoracic Aorta with Congenital Valve Disease Using MRI.Bioengineering (Basel, Switzerland) · 2023Article
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9 authors.
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Abstract
The aim was to evaluate the accuracy of a prototypical artificial intelligence-based algorithm for automated segmentation and diameter measurement of the thoracic aorta (TA) using CT. One hundred twenty-two patients who underwent dual-source CT were retrospectively included. Ninety-three of these patients had been administered intravenous iodinated contrast. Images were evaluated using the prototypical algorithm, which segments the TA and determines the corresponding diameters at predefined anatomical locations based on the American Heart Association guidelines. The reference standard was established by two radiologists individually in a blinded, randomized fashion. Equivalency was tested and inter-reader agreement was assessed using intra-class correlation (ICC). In total, 99.2% of the parameters measured by the prototype were assessable. In nine patients, the prototype failed to determine one diameter along the vessel. Measurements along the TA did not differ between the algorithm and readers (p > 0.05), establishing equivalence. Inter-reader agreement between the algorithm and readers (ICC ≥ 0.961; 95% CI: 0.940−0.974), and between the readers was excellent (ICC ≥ 0.879; 95% CI: 0.818−0.92). The evaluated prototypical AI-based algorithm accurately measured TA diameters at each region of interest independent of the use of either contrast utilization or pathology. This indicates that the prototypical algorithm has substantial potential as a valuable tool in the rapid clinical evaluation of aortic pathology.
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