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UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based
Luca Saba1, Mahesh Maindarkar2,3, Amer M Johri4
1Department of Radiology, Azienda Ospedaliero Universitaria, 40138 Cagliari, Italy.
Insights
Integrating genomic-based biomarkers (GBBM) and radiomic-based biomarkers (RBBM) with AI improves cardiovascular disease (CVD) and stroke risk prediction. This approach enhances patient stratification beyond conventional risk factors for personalized prevention.
Area of Science:
- Biomedical Engineering
- Cardiology
- Artificial Intelligence in Medicine
Background:
- Cardiovascular disease (CVD) and stroke diagnosis is challenging due to late-onset symptoms.
- Current clinical risk scores inadequately predict cardiac events, leaving many at-risk patients unclassified.
- Conventional risk factors alone are insufficient for comprehensive CVD risk stratification.
Purpose of the Study:
- To review the integration of genomic-based biomarkers (GBBM) and radiomic-based biomarkers (RBBM) for improved CVD and stroke risk assessment.
- To propose a novel artificial intelligence (AI)-based preventive, precision, and personalized (P^3) CVD/Stroke risk model.
- To evaluate the correlation between RBBM and GBBM for precise CVD and stroke severity detection.
Main Methods:
- Systematic literature review using PRISMA guidelines, selecting 246 studies on CVD/Stroke risk.
- Analysis of deep learning (DL) models for risk stratification using integrated RBBM and GBBM.
- Overview of platelet function, complete blood count (CBC), and diagnostic methods relevant to CVD/Stroke.
Main Results:
- RBBM and GBBM biomarkers show strong correlation, enabling precise detection of CVD and stroke severity.
- Deep learning models effectively stratify CVD/Stroke risk when utilizing combined RBBM and GBBM.
- The proposed AI-driven P^3 model offers a promising framework for advanced CVD/Stroke risk assessment.
Conclusions:
- Integration of RBBM and GBBM offers a powerful paradigm for CVD/Stroke risk assessment.
- AI, particularly DL, streamlines the integration of these biomarkers for enhanced risk prediction.
- The proposed P^3 model represents a significant advancement in personalized CVD/Stroke risk management.
Abstract:
Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. model signifies a promising advancement in CVD/Stroke risk assessment.
