Cardiovascular magnetic resonance imaging (CMRI) for the evaluation of patients with cardiovascular diseaseusing(CVD) using machine learning: An overview

Backgrounds: Despite significant advances in diagnosis and treatment, CVD remains the most common cause of morbidity/mortality worldwide, accounting for approximately one third of annual deaths.Cardiovascular imaging has a pivotal role in diagnostic decision-making and treatment follow-up for CVD. Among the diagnostic methods, CMRI is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs.CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass. In addition to quantitative measurements, for years, clinicians have been relying on manual approaches for CMR image analysis which is time consuming and prone to subjective errors. It is a major clinical challenge to automatically derive quantitative and clinically information from CMR images.Because of the large number of cardiac images that are routinely acquired with a wide range of modalities researchers have proposed artificial intelligence (AI) techniques for the automatic diagnosis of CVDs using CMRI data.Methods :Various databases like Google Scholar, PubMed, Scopus, and Web of Science were searched over a period of ۸ years from ۲۰۱۵ to ۲۰۲۳.All the studies indicating the The potential role of imaging for heart disease, including Cardiovascular magnetic resonance and artificial intelligence techniques such as a machine learning were included in this review. Exclusion criteria were studies unavailable and irrelevant studies of the subject.Results :Correlations between machine learning and manual segmentation-derived flow approached unity (r = ۰.۹۹, p < ۰.۰۰۱).Among patients without advanced mitral regurgitation, machine learning correlated well (r = ۰.۶۳, p < ۰.۰۰۱) .Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ ۱۲.۶ ± ۲۰.۹ mL, p = ۰.۰۰۵), further supporting validity of this method. the results demonstrated that ANNs, DTs, SVMs, Naïve Bayes, and KNN are the most widely used algorithms for CAD detection.Due to inherent differences among datasets, inconsistent performances have been reported for different datasets using similar ML algorithms.The reported results indicate that KNN, SVM, and ANN have achieved the highest accuracies for most of the CAD datasets.Conclusion :Findings support use of machine learning for analysis of large scale CMR datasets.However, Despite the progress that has been made in recent years, there remain key shortcomings in ML-based detection of CAD that must be addressed in upcoming years.AI researchers introduced DL methods to tackle the challenges of ML method.

Keywords:

CMRI , CVD , Machine learning