Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach

Our study highlights the potential of novel image analysis techniques in extracting quantitative information beyond calcification in the identification of CAD. Though further work is required, the transition of these novel techniques into clinical practice may eventually facilitate better stroke risk stratification.

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Publication Details

Journal
European Journal of Radiology Open
Published
2024-08-31
DOI
https://doi.org/10.1016/j.ejro.2024.100594
Citations
14
Primary Topic
Cerebrovascular and Carotid Artery Diseases
Type
article
Field-Weighted Citation Impact
5.87

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article

Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach

Nicholas R. Evans, C. Wall, Mark Yu Zheng Wong, Michael Roberts et al.
14 citations
European Journal of Radiology Open
Cerebrovascular and Carotid Artery Diseases
5.87
article

Using machine learning to predict carotid artery symptoms from CT angiography: A radiomics and deep learning approach

Nicholas R. Evans, C. Wall, Mark Yu Zheng Wong, Michael Roberts, Evis Sala, Holly Pavey, Carola‐Bibiane Schönlieb, Patrick A. Coughlin, Fulvio Zaccagna, Elizabeth Le, Rouchelle Sriranjan, Jonathan Richard Weir-McCall, Yuan Huang, Elizabeth A. Warburton, Jason M. Tarkin, James H.F. Rudd, Andrej Ćorović, Leonardo Rundo, Mohammed M. Chowdhury
article en
14 citations

Abstract

Our study highlights the potential of novel image analysis techniques in extracting quantitative information beyond calcification in the identification of CAD. Though further work is required, the transition of these novel techniques into clinical practice may eventually facilitate better stroke risk stratification.

European Journal of Radiology OpenVol. 13
Università Cattolica del Sacro Cuore (IT), Engineering and Physical Sciences Research Council (GB), University of Leeds (GB), University of Salerno (IT), University of Cambridge (GB), Royal Papworth Hospital (GB), Cambridge University Hospitals NHS Foundation Trust (GB), Agostino Gemelli University Polyclinic (IT), University of Oxford (GB), Cancer Research UK Cambridge Center (GB), Cambridge School (PT), Istituti di Ricovero e Cura a Carattere Scientifico (IT)
Wellcome Trust, National Institute for Health and Care Research, British Heart Foundation, Department of Health and Social Care, Medical Research Council, Engineering and Physical Sciences Research Council, NIHR Cambridge Biomedical Research Centre
Good health and well-being
Openalex Percentile: Top 3%
Cerebrovascular and Carotid Artery Diseases
5.87
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