TotalSegmentator-AI for aortic segmentation in CT: Reliable performance in normal anatomy but limited utility in pathological aortic disease

Introduction: Accurate aortic segmentation on computed tomography angiography (CTA) is essential for diagnosing aortic disease, cardiovascular risk assessment, and surgical planning. Deep learning algorithms, such as TotalSegmentator-AI, offer fully automated multi-organ segmentation, yet their performance in pathological aortic conditions remains uncertain. This study performs a clinical stress-test of TotalSegmentator-AI, mapping its boundaries and structural failure modes across a spectrum of normal and pathological cases. Methods: In this monocentric, retrospective study, 60 CTA scans from 2014 to 2024 were categorized into six groups: young, elderly, aneurysm, dissection, venous phase, and non-contrast phase. TotalSegmentator-AI was applied without manual correction. Two radiologists independently rated six aortic segments per scan using a five-point qualitative scale. Quantitative segmentation errors were correlated with qualitative scores using Spearman’s correlation, and inter-reader agreement was assessed with weighted Cohen’s kappa. Results: All scans were successfully processed, yielding 360 aortic segments. Median segmentation quality was 4 IQR 4–5, with 77% rated good or excellent. Performance was consistent across segments (p = 0.16) but varied by category (p < 0.001): best in young patients (5 IQR 4–5) and adequate in non-contrast and venous-phase scans (4 IQR 4–5), poorest in dissections (3 IQR 3–4) and aneurysms (4 IQR 3–4). A strong negative correlation was observed between qualitative scores and quantitative errors (ρ =–1, p = 0.017). Inter-reader agreement was substantial (κ = 0.72). Conclusion: TotalSegmentator-AI achieves accurate aortic segmentation in normal anatomy but is inadequate for unsupervised clinical use in complex pathologies like aneurysms and dissections. Comprehensive human-in-the loop quality control or dedicated pathology-inclusive models are mandatory before AI-based segmentation can be safely integrated into vascular clinical workflows.