Validation of machine learning angiography-derived physiological pattern of coronary artery disease

The machine learning models for physiological patterns of CAD classification outperformed the binary PPGi method, providing robust and generalizable classification across different study populations.

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Publication Details

Journal
European Heart Journal - Digital Health
Published
2025-04-07
DOI
https://doi.org/10.1093/ehjdh/ztaf031
Citations
5
Primary Topic
Cardiac Imaging and Diagnostics
Type
article
Field-Weighted Citation Impact
6.80

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article

Validation of machine learning angiography-derived physiological pattern of coronary artery disease

Mattia Lunardi, William C. Wijns, Andrew J. Simpkin, Flavio Luciano Ribichini et al.
5 citations
European Heart Journal - Digital Health
Cardiac Imaging and Diagnostics
6.80
article

Validation of machine learning angiography-derived physiological pattern of coronary artery disease

Mattia Lunardi, William C. Wijns, Andrew J. Simpkin, Flavio Luciano Ribichini, Angela McInerney, Concetta Mammone, Norma Bargary, Antonio Maria Leone, Roberto Scarsini, Gabriele Pesarini, Simone Fezzi, Max Wagener, Carlo Trani, Francesco Burzotta, Daixin Ding, Shengxian Tu, David J. Connolly, Gábor G. Tóth, Yueyun Zhu
article en
5 citations

Abstract

The machine learning models for physiological patterns of CAD classification outperformed the binary PPGi method, providing robust and generalizable classification across different study populations.

European Heart Journal - Digital HealthVol. 6(4)
Università Cattolica del Sacro Cuore (IT), University of Verona (IT), Shanghai Jiao Tong University (CN), Ollscoil na Gaillimhe – University of Galway (IE), Medical University of Graz (AT), University of Limerick (IE), Istituti di Ricovero e Cura a Carattere Scientifico (IT)
Science Foundation Ireland, National Natural Science Foundation of China, Bracco Imaging, MicroPort, Abbott Vascular
Openalex Percentile: Top 3%
Cardiac Imaging and Diagnostics
6.80
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