EJVES vascular forum · 2025
Imaging Characterisation of Peripheral Artery Disease: A Scoping Review on Current Classifications and New Insights Brought by Artificial Intelligence.
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Lareyre Fabien, Guzzi Lisa, Nasr Bahaa, Alouane Ahmed, Goffart Sébastien, Chierici Andréa, Delingette Hervé, Raffort Juliette
Аннотация
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.
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.