ReviewEJVES vascular forum2025
Imaging Characterisation of Peripheral Artery Disease: A Scoping Review on Current Classifications and New Insights Brought by Artificial Intelligence.
Review in EJVES vascular forum, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Investigating Age-Dependent Oxygenation and Blood Perfusion in a Mouse Model of Peripheral Artery Disease (PAD) Using Multispectral Optoacoustic Tomography (MSOT), Laser Speckle Contrast Imaging (LSCI) and Histology.Diagnostics (Basel, Switzerland) · 2026Article
- Optoacoustic muscle imaging.Journal of neuromuscular diseases · 2026Review
- Artificial Intelligence (AI) in Imaging Characterisation: Universality and Availability Will Rule Them All.EJVES vascular forum · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objectives: Several scan and imaging classifications have been described for the management of patients with peripheral artery disease (PAD). In parallel, artificial intelligence (AI) has brought new insights in vascular imaging analysis. This scoping review aimed to summarise imaging classification for PAD and to discuss how AI could be used to enhance these systems. Methods: Medline was searched for relevant studies that addressed imaging classification and use of AI in PAD vascular imaging. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) protocol was followed. Results: Thirty four articles were included. This paper provides an overview and discusses the advantages and limits of current imaging classifications used to characterise atherosclerotic lesions as well as calcifications in patients with PAD. AI offers new opportunities to enhance automatic detection and classification of PAD lesions, with potentially new techniques that could be used to assess vascular calcification and identify radiomic patterns. Conclusion: AI has brought new opportunities to improve imaging software to facilitate robust and reproducible analysis of lower limb arterial lesions. In the future, such applications may contribute to improved clinical workflow and help decision making.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.