“We tested whether the internal patterns we created using machine learning could predict the risk of atherosclerosis in the carotid artery, and the results showed that to some extent they can,” says the first author. Qiaosen ChenPhD student at the Department of Medicine, Karolinska Institutet in Solna.
The researchers now plan to investigate the genes and mechanisms underlying the different endogenous patterns of atherosclerosis and related heart and brain diseases. They are also interested in investigating how the results of the current study translate into predicting the progression of atherosclerosis in other vascular beds.
The research was funded primarily by the Heart-Lung Foundation, ALF, and the Professor Nanna Svartz Foundation (see study for more information). No conflicts of interest have been reported.
“A machine learning-based approach to identify endogenous patterns of carotid subclinical atherosclerosis”. Kyaw Sen Chen, Otto Bergmann, Louise Ziegler, Damiano Baldassare, Fabrizio Viglia, Elena Tremoli, Rona J. Strawbridge, Antonio Gallo, Matteo Biro, Andres J. Smit, Sudhir Kurll, Kai Savonen, Lars Lind, Per Erickson, Bruna Gigante in the framework of the research study. Cardiovascular researchonline 21 Jul 2023 doi: 10.1093/cvr/cvad106.
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