Key result
AI applications in aortic and peripheral artery disease show high retrospective accuracy (AUROC 0.76-0.94; Dice 0.89-0.94), but lack prospective outcome trials to alter guideline treatment thresholds.
Why the study?
Aortic disease and lower-extremity PAD generate substantial morbidity, mortality, and longitudinal imaging burden, and although AI is increasingly studied for vascular risk prediction, image analysis, procedural support, and surveillance, the maturity of these applications is heterogeneous.
Do artificial intelligence applications improve risk prediction, imaging analysis, procedural support, longitudinal surveillance, and clinical outcomes compared to standard guideline-directed care in patients with aortic disease and peripheral artery disease?
Do artificial intelligence applications improve risk prediction, imaging analysis, procedural support, longitudinal surveillance, and clinical outcomes compared to standard guideline-directed care in patients with aortic disease and peripheral artery disease?
While AI shows promise in automated vascular image segmentation and opportunistic AAA case finding, most vascular risk prediction, radiomics, and digital-twin applications remain investigational and should not alter current guideline thresholds for repair, revascularization, or surveillance.
Answers are grounded in this paper and cite their sources.
Retrospective AI accuracy in aortic/PAD imaging should not alter guidelines; leaves open need for prospective outcome trials.
Aortic disease and lower-extremity peripheral artery disease (PAD) generate substantial morbidity, mortality, and longitudinal imaging burden. Artificial intelligence (AI) is increasingly studied for vascular risk prediction, image analysis, procedural support, and surveillance, but the maturity of these applications is heterogeneous. This review evaluates recent AI applications in aortic disease and PAD, emphasizing the distinction between algorithmic performance, external validation, demonstrated workflow impact, and proven patient benefit. Automated image segmentation and opportunistic abdominal aortic aneurysm (AAA) detection have the most mature evidence base, including retrospective external validation and, for AI-assisted AAA case finding, nonrandomized evidence of improved downstream evaluation and follow-up. Conversely, while machine-learning models for vascular risk prediction and disease progression often report high retrospective accuracy, their clinical readiness remains strictly investigational. Across most aortic and PAD applications, there is a persistent lack of independent external validation, decision-curve analysis, and prospective testing against standard care. Image-fusion guidance has clinical evidence for reducing procedural resource use, but this evidence should not be conflated with autonomous AI or robotic navigation. Cardiovascular digital twins remain largely mechanistic, computational, or early-development frameworks. AI can augment vascular imaging and risk assessment, but high retrospective accuracy is not equivalent to clinical utility. Current guideline thresholds for repair, revascularization, and surveillance should not be altered on the basis of investigational radiomics or prediction models. Translation requires multicenter external validation, prospective workflow evaluation, calibration and net-benefit assessment, transparent reporting, and trials demonstrating improved patient outcomes.
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Robert et al. (2026) conducted a review in Aortic disease and lower-extremity peripheral artery disease (PAD). Artificial intelligence (AI), machine learning, deep learning, radiomics, image fusion, and digital twins vs. Standard clinical care and contemporary guideline-directed thresholds was evaluated on Clinical maturity, external validation, workflow impact, and prospective patient outcomes of AI applications in aortic disease and PAD. AI applications in aortic and peripheral artery disease show high retrospective accuracy (AUROC 0.76-0.94; Dice 0.89-0.94), but lack prospective outcome trials to alter guideline treatment thresholds.
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