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. 2022 Jun 23:2022:2632770.
doi: 10.1155/2022/2632770. eCollection 2022.

Machine Learning Approach for Cardiovascular Risk and Coronary Artery Calcification Score

C R Aditya  1 , Naveen Chakravarthy Sattaru  2 , Kumaraguruparan Gopal  3 , R Rahul  4 , G Chandra Shekara  4 , Omaima Nasif  5 , Sulaiman Ali Alharbi  6 , S S Raghavan  7 , S Arockia Jayadhas  8
Affiliations

Machine Learning Approach for Cardiovascular Risk and Coronary Artery Calcification Score

C R Aditya et al. Biomed Res Int. .

Retraction in

Abstract

Coronary artery calcification (CAC) could assist in the discovery of new risk elements for coronary artery disorder. CAC evaluation, on the other hand, is difficult due to the wide range of CAC in the populations. As a reason, evaluating and analysing data among research have become complicated. In the Research of Inherited Risk Factors for Coronary Atherosclerosis, we used CAC information to test the effects of different analytical methodologies on the correlation with recognized cardiovascular risk elements in asymptomatic patients. Cardiac computed tomography (CT) is also seeing an increase in examinations, and machine learning (ML) could assist with the growing amount of extracted data. Furthermore, there are other sectors in cardiac CT where machine learning could be crucial, including coronary calcium scoring, perfusion, and CT angiography. The establishment of risk evaluation algorithms based on information from CAC utilizing machine learning could assist in the categorization of patients undergoing cardiovascular into distinct risk groups and effectively adapt their treatments to their unique situations. Our findings imply that for forecasting CVD occurrences in asymptomatic people, age-sex segmentation by CAC percentile rank is as effective as absolute CAC scoring. Longitudinal population-based investigations are currently underway and would offer further definitive findings. While machine learning is a strong technology with a lot of possibilities, its implementations in the domain of cardiac CAC are generally in the early stages of development and are not currently commonly accessible in medical practise because of the requirement for substantial verification. Enhanced machine learning will, however, have a significant effect on cardiovascular and coronary artery calcification in the upcoming years.

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Conflict of interest statement

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Figures

Figure 1
Figure 1
A single-layer neural network (NN) for machine learning against a deep neural network with four hidden layers.
Figure 2
Figure 2
Proposed flow chart for the machine learning analysis procedure for CAC.
Figure 3
Figure 3
Atherosclerotic vascular disorder prevalence by coronary artery calcium scoring classification. ASVD is an acronym for atherosclerotic vascular disorder.
Figure 4
Figure 4
The frequency distributions of coronary artery calcification (as measured by Agatston scoring) in RA patients and healthier regulate persons. The numbers across the bars represent the number of participants in every scoring subgroup as a percentage of the overall number of individuals.
Figure 5
Figure 5
The age-related incidence of coronary artery calcification in individuals with rheumatoid arthritis (RA) and healthy controls. For those under the age of 60, there was an important association among age and disorder conditions (P < 0.06 for interactions).
See this image and copyright information in PMC

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