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- W4386187781 abstract "ObjectivesTo evaluate the performance of artificial intelligence (AI) software for automatic thoracic aortic diameter assessment in a heterogeneous cohort with low-dose, non-contrast chest computed tomography (CT).Materials and methodsParticipants of the XXX (XXX) study who underwent low-dose, non-contrast chest CT (August 2017 - May 2022) were included using random samples of 80 participants <50y, ≥80y, and with thoracic aortic diameter ≥ 40 mm. AI-based aortic diameters at eight guideline compliant positions were compared with manual measurements. In 90 examinations (30 per group) diameters were reassessed for intra- and inter-reader variability, which was compared to discrepancy of the AI system using Bland-Altman analysis, paired samples t-testing and linear mixed models.ResultsWe analyzed 240 participants (63 ± 16 years; 50% men). AI evaluation failed in 11 cases due to incorrect segmentation (4.6%), leaving 229 cases for analysis. No difference was found in aortic diameter between manual and automatic measurements (32.7 ± 6.4 mm vs 32.7 ± 6.0 mm, p=0.70). Bland-Altman analysis yielded no systematic bias and a repeatability coefficient of 4.0 mm for AI. Mean discrepancy of AI (1.3 ± 1.6 mm) was comparable to inter-reader variability (1.4 ± 1.4 mm); only at the proximal aortic arch showed AI higher discrepancy (2.0 ± 1.8 mm vs 0.9 ± 0.9 mm, p<0.001). No difference between AI discrepancy and inter-reader variability was found for any subgroup (all: p>0.05).ConclusionThe AI software can accurately measure thoracic aortic diameters, with discrepancy to a human reader similar to inter-reader variability in a range from normal to dilated aortas." @default.
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- W4386187781 date "2023-10-01" @default.
- W4386187781 modified "2023-10-14" @default.
- W4386187781 title "Validation of an AI-based algorithm for measurement of the thoracic aortic diameter in low-dose chest CT" @default.
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- W4386187781 doi "https://doi.org/10.1016/j.ejrad.2023.111067" @default.
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