Machine learning detects symptomatic patients with carotid plaques based on 6-type calcium configuration classification on CT angiography.
Francesco PisuHui ChenBin JiangGuangming ZhuMarco Virgilio UsaiMartin AustermannYousef ShehadaElias JohanssonJasjit SuriGiuseppe LanzinoJohn BensonValentina NardiAmir LermanMax WintermarkLuca SabaPublished in: European radiology (2023)
• While the association between carotid plaques composition and cerebrovascular events is recognized, the role of calcium configuration remains unclear. • Machine learning of 6-type plaque grading can identify symptomatic patients. Calcified plaques on the right artery, advanced age, and hyperlipidemia were the most important predictors. • Fast acquisition of CTA enables rapid grading of plaques upon the patient's arrival at the hospital, which streamlines the diagnosis of symptoms using ML.
Keyphrases
- machine learning
- end stage renal disease
- artificial intelligence
- ejection fraction
- deep learning
- chronic kidney disease
- newly diagnosed
- big data
- healthcare
- prognostic factors
- peritoneal dialysis
- coronary artery disease
- case report
- type diabetes
- adipose tissue
- metabolic syndrome
- depressive symptoms
- insulin resistance
- skeletal muscle
- loop mediated isothermal amplification