ReviewFrontiers in cardiovascular medicine2025
Artificial intelligence in aortic CT angiography: current applications and future perspectives.
Review in Frontiers in cardiovascular medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Centroid Regression for Preoperative Risk Assessment of Acute Type A Aortic Dissection Based on Multivariate Clinical Data.Journal of clinical medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is revolutionizing cardiovascular imaging, with aortic computed tomography angiography (CTA) emerging as a prominent area of application. CTA imaging is essential for the diagnosis, risk stratification, and treatment planning of aortic diseases. However, conventional CTA techniques face limitations such as radiation exposure, contrast agent risks, and reliance on manual interpretation. The integration of AI into aortic CTA offers innovative solutions across multiple domains. AI can enhance image quality, automate anatomical segmentation, improve diagnostic accuracy for aortic emergencies, and provide quantitative tools for prognostic evaluation following interventions like endovascular aortic repair. Furthermore, this review provides the analysis of emerging techniques, including advanced image synthesis methods, Vision Transformer architectures, multi-task learning, weakly supervised learning, and the paradigm shift introduced by Foundation Models, emphasizing their potential for clinical application. This work comprehensively summarizes the current applications and nascent technological paradigms of AI in aortic CTA, along with existing challenges and future research directions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.