Cardiovascular disease/stroke risk stratification in deep learning framework: a review

Abstract

The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review

“…Researchers developed five generations of cardiovascular risk stratification methods over time. The first generation used manual calculations, assessing risk based on blood tests, family history, and carotid ultrasound [ 214 ]. The second generation employed calculators like framingham risk score (FRS) and atherosclerotic cardiovascular disease (ASCVD) but had variability [ 26 ].…”

“…The fourth generation used machine learning, collecting data from MRI, US, and CT with automated segmentation and classifiers like SVM and random forest (RF). In the fifth generation, deep learning was employed for detailed multiclass risk assessment, representing a comprehensive evolution from manual calculations to advanced DL-based approaches with the potential for monitoring treatment responses [ 214 ].…”

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

“… 18 , 19 Precision modifies the treatments to meet patients' needs and preferences. 16 , 18 , 20 , 21 , 22 , 23 , 24 , 25 Pharmacogenomics focuses on understanding how an individual's genetic makeup affects their drug response, leading to optimised drug selection and dosages. 4 , 26 Patient empowerment provides patients with the knowledge, skills, and resources necessary to make informed decisions about their health.…”

A Novel Approach for the Detection and Severity Grading of Chronic Obstructive Pulmonary Disease Based on Transformed Volumetric Capnography

“…The advancement of artificial intelligence, especially through deep learning techniques, has revolutionized the field of medical diagnostics. With the advancement of artificial intelligence, particularly deep learning, substantial potential has been demonstrated in disease diagnosis, such as cardiovascular diseases [ 16 , 17 , 18 , 19 ] and cancer diagnosis [ 20 , 21 , 22 ]. Deep learning, particularly Convolutional Neural Networks (CNN), stands out for its proficiency in image-based analysis, adept at deciphering complex patterns and extracting meaningful insights from high-dimensional data.…”