Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions.
Cast your vote
You can rate an item by clicking the amount of stars they wish to award to this item.
When enough users have cast their vote on this item, the average rating will also be shown.
Your vote was cast
Thank you for your feedback
Thank you for your feedback
Edmunds, Kyle J
MetadataShow full item record
CitationRicciardi C, Edmunds KJ, Recenti M, et al. Assessing cardiovascular risks from a mid-thigh CT image: a tree-based machine learning approach using radiodensitometric distributions. Sci Rep. 2020;10(1):2863. Published 2020 Feb 18. doi:10.1038/s41598-020-59873-9
AbstractThe nonlinear trimodal regression analysis (NTRA) method based on radiodensitometric CT distributions was recently developed and assessed for the quantification of lower extremity function and nutritional parameters in aging subjects. However, the use of the NTRA method for building predictive models of cardiovascular health was not explored; in this regard, the present study reports the use of NTRA parameters for classifying elderly subjects with coronary heart disease (CHD), cardiovascular disease (CVD), and chronic heart failure (CHF) using multivariate logistic regression and three tree-based machine learning (ML) algorithms. Results from each model were assembled as a typology of four classification metrics: total classification score, classification by tissue type, tissue-based feature importance, and classification by age. The predictive utility of this method was modelled using CHF incidence data. ML models employing the random forests algorithm yielded the highest classification performance for all analyses, and overall classification scores for all three conditions were excellent: CHD (AUCROC: 0.936); CVD (AUCROC: 0.914); CHF (AUCROC: 0.994). Longitudinal assessment for modelling the prediction of CHF incidence was likewise robust (AUCROC: 0.993). The present work introduces a substantial step forward in the construction of non-invasive, standardizable tools for associating adipose, loose connective, and lean tissue changes with cardiovascular health outcomes in elderly individuals.
DescriptionTo access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Download
- Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images.
- Authors: Recenti M, Ricciardi C, Edmunds K, Gislason MK, Gargiulo P
- Issue date: 2020 Apr 7
- Healthy Aging Within an Image: Using Muscle Radiodensitometry and Lifestyle Factors to Predict Diabetes and Hypertension.
- Authors: Recenti M, Ricciardi C, Edmunds K, Gislason MK, Sigurdsson S, Carraro U, Gargiulo P
- Issue date: 2020 Dec 11
- Advanced quantitative methods in correlating sarcopenic muscle degeneration with lower extremity function biometrics and comorbidities.
- Authors: Edmunds K, Gíslason M, Sigurðsson S, Guðnason V, Harris T, Carraro U, Gargiulo P
- Issue date: 2018
- Dynamic Features Impact on the Quality of Chronic Heart Failure Predictive Modelling.
- Authors: Balabaeva K, Kovalchuk S, Metsker O
- Issue date: 2019
- Leveraging Machine Learning Techniques to Forecast Patient Prognosis After Percutaneous Coronary Intervention.
- Authors: Zack CJ, Senecal C, Kinar Y, Metzger Y, Bar-Sinai Y, Widmer RJ, Lennon R, Singh M, Bell MR, Lerman A, Gulati R
- Issue date: 2019 Jul 22