ArticleThe international journal of cardiovascular imaging2019
A novel software tool for semi-automatic quantification of thoracic aorta dilatation on baseline and follow-up computed tomography angiography.
Article in The international journal of cardiovascular imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Thoracic Aortic Aneurysm Growth Rates and Predicting Factors: A Systematic Review and Meta-Analysis.Journal of the American Heart Association · 2025Pooled it
- Use of artificial intelligence-assisted measurement of aortic diameter in clinical decision making about abdominal aortic aneurysm repair.Abdominal radiology (New York) · 2026Article
- Artificial intelligence-powered solutions for automated aortic diameter measurement in computed tomography: a narrative review.Annals of translational medicine · 2024Review
- Artificial Intelligence Provides Accurate Quantification of Thoracic Aortic Enlargement and Dissection in Chest CT.Diagnostics (Basel, Switzerland) · 2024Article
- Three-dimensional aortic geometry mapping via registration of non-gated contrast-enhanced or gated and respiratory-navigated MR angiographies.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Observational
- Assessing the Accuracy of an Artificial Intelligence-Based Segmentation Algorithm for the Thoracic Aorta in Computed Tomography Applications.Diagnostics (Basel, Switzerland) · 2022Article
- Validation of a robust method for quantification of three-dimensional growth of the thoracic aorta using deformable image registration.Medical physics · 2022Article
- Registration-based semi-automatic assessment of aortic diameter growth rate from contrast-enhanced computed tomography outperforms manual quantification.European radiology · 2022Article
- Evaluating the Performance of a Convolutional Neural Network Algorithm for Measuring Thoracic Aortic Diameters in a Heterogeneous Population.Radiology. Artificial intelligence · 2022Article
- Automated segmentation and quantification of the healthy and diseased aorta in CT angiographies using a dedicated deep learning approach.European radiology · 2022Article
- Local aortic aneurysm wall expansion measured with automated image analysis.JVS-vascular science · 2022Article
- Comparison of a novel Compressed SENSE accelerated 3D modified relaxation-enhanced angiography without contrast and triggering with CE-MRA in imaging of the thoracic aorta.The international journal of cardiovascular imaging · 2021Article
- Cardiovascular imaging 2019 in the International Journal of Cardiovascular Imaging.The international journal of cardiovascular imaging · 2020Article
- Towards the automation of early-stage human embryo development detection.Biomedical engineering online · 2019Article
- Cardiovascular imaging 2018 in the International Journal of Cardiovascular Imaging.The international journal of cardiovascular imaging · 2019Article
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Authors and funding
8 authors at 2 institutions in 2 countries.
Funding
Abstract
A dedicated software package that could semi-automatically assess differences in aortic maximal cross-sectional diameters from consecutive CT scans would most likely reduce the post-processing time and effort by the physicians. The aim of this study was to present and assess the quality of a new tool for the semi-automatic quantification of thoracic aorta dilation dimensions. Twenty-nine patients with two CTA scans of the thoracic aorta for which the official clinical report indicated an increase in aortic diameters were included in the study. Aortic maximal cross-sectional diameters of baseline and follow-up studies generated semi-automatically by the software were compared with corresponding manual measurements. The semi-automatic measurements were performed at seven landmarks defined on the baseline scan by two operators. Bias, Bland-Altman plots and intraclass correlation coefficients were calculated between the two methods and, for the semi-automatic software, also between two observers. The average time difference between the two scans of a single patient was 1188 ± 622 days. For the semi-automatic software, in 2 out of 29 patients, manual interaction was necessary; in the remaining 27 patients (93.1%), semi-automatic results were generated, demonstrating excellent intraclass correlation coefficients (all values ≥ 0.91) and small differences, especially for the proximal aortic arch (baseline: 0.19 ± 1.30 mm; follow-up: 0.44 ± 2.21 mm), the mid descending aorta (0.37 ± 1.64 mm; 0.37 ± 2.06 mm), and the diaphragm (0.30 ± 1.14 mm; 0.37 ± 1.80 mm). The inter-observer variability was low with all errors in diameters ≤ 1 mm, and intraclass correlation coefficients all ≥ 0.95. The semi-automatic tool decreased the processing time by 40% (13 vs. 22 min). In this work, a semi-automatic software package that allows the assessment of thoracic aorta diameters from baseline and follow-up CTs (and their differences), was presented, and demonstrated high accuracy and low inter-observer variability.
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