AI-POWERED ENTERPRISE ANALYSIS
Carotid Plaque Vulnerability: DSA-based RWS Validated by MRI
This study evaluates plaque stability in atherosclerotic carotid plaques, which are key stroke contributors, by using radial wall strain (RWS) from digital subtraction angiography (DSA). It explores the Link between RWS, symptomatic stenosis, and endovascular treatment outcomes. In a single-center prospective study, 82 patients undergoing endovascular carotid atherosclerotic stenosis (CAS) treatment were assessed. Plaque vulnerability was analyzed using high-resolution magnetic resonance angiography and RWS from DSA images, examining the consistency and correlation of these methods, determining the optimal RWS threshold, and its relation to ischemic symptoms. A statistically significant correlation (p < 0.001) and concordance (Kappa = 0.447, p < 0.001) were observed between RWSmax and various aspects of plaque stability, as evaluated using high-resolution nuclear magnetic resonance. In severe CAS, RWSmax was higher in damaged plaques (17.7% vs. 12.7%, p < 0.001), with a non-significant trend in moderate CAS (15.7% vs. 10.8%, p = 0.068). Symptomatic CAS patients had higher RWSmax in vulnerable plaques (18% vs. 10%, p < 0.001), with a similar non-significant difference in asymptomatic patients (16.9% vs. 13.1%; p = 0.051). The optimal RWSmax cutoff for identifying vulnerability was 14.9% (AUC = 0.838; p < 0.001; 85.5% sensitivity, 74.1% specificity). RWSmax demonstrated excellent diagnostic accuracy across subgroups. More symptomatic patients had vulnerable plaques than asymptomatic ones [85.71% vs. 39.39%, p < 0.001], with higher median RWSmax in symptomatic patients [17.40% vs. 14.80%, p = 0.008]. Significantly more symptomatic than asymptomatic patients had RWSmax values ≥ 14.9% (77.55% vs. 48.48%, p = 0.006). The DSA-based RWS is a valuable index for the evaluation of CAS plaque vulnerability.
Executive Impact: Data-Driven Insights
Explore the key performance indicators and strategic advantages identified through our analysis.
Optimal threshold identified for radial wall strain to predict plaque vulnerability.
Area Under Curve for RWSmax in identifying vulnerable plaques.
Percentage of symptomatic patients with vulnerable plaques.
Sensitivity of RWSmax at optimal cutoff for vulnerability detection.
Deep Analysis & Enterprise Applications
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This study demonstrates that AI-generated radial wall strain (RWS) from digital subtraction angiography (DSA) is a valuable, non-invasive index for assessing carotid atherosclerotic plaque vulnerability. It shows strong correlation with high-resolution MRI findings and offers a robust predictive tool for ischemic events, potentially transforming clinical decision-making in stroke prevention and management.
The odds ratio for plaque instability when RWSmax values are ≥ 14.9% compared to values < 14.9%.
Enterprise Process Flow
| Feature | DSA-based RWS | HR-VWI (MRI) |
|---|---|---|
| Assessment Focus | Biomechanical status, vessel wall deformation | Plaque composition (fibrous cap, hemorrhage, necrotic core) |
| Stroke Risk Factor | Indicates plaque instability/rupture risk | Identifies vulnerable plaque characteristics |
| Interventional Guidance | Direct imaging foundation for intervention selection | Facilitates early diagnosis & treatment guidance |
| Limitations | Does not directly show plaque composition | Potential for some uncertainty in plaque characteristics vs OCT/IVUS |
| Advantages | Evaluates hemodynamics & collateral circulation, larger vessel applicability | Precise differentiation of plaque components, high accuracy rates |
Clinical Utility of RWSmax in Symptomatic Patients
In a cohort of symptomatic CAS patients, RWSmax values were significantly higher in vulnerable plaques (18% vs. 10%, p < 0.001) compared to stable plaques. This highlights the practical application of DSA-based RWS in identifying high-risk patients, enabling clinicians to make more informed decisions regarding endovascular treatment strategies and improving patient outcomes by targeting unstable plaques more effectively.
Calculate Your Potential ROI
See how AI-powered RWS analysis can lead to significant cost savings by reducing the incidence of recurrent ischemic strokes, optimizing treatment selection, and improving long-term patient outcomes, thereby reducing healthcare burden.
Implementation Roadmap
A phased approach to integrate predictive AI into your operations.
Phase 1: Data Integration & Model Training
Integrate existing DSA and HR-VWI datasets, preprocessing data for AI model training. Develop and train initial RWS-based plaque vulnerability prediction models.
Phase 2: Validation & Refinement
Conduct internal and external validation of the AI models. Refine algorithms based on performance metrics and clinical feedback to optimize accuracy and reliability.
Phase 3: Clinical Pilot & Workflow Integration
Pilot the RWS-based assessment tool in a clinical setting with a small patient cohort. Integrate the tool into existing PACS/RIS workflows for seamless adoption by clinicians.
Phase 4: Scalable Deployment & Continuous Monitoring
Deploy the validated solution across multiple sites. Establish continuous monitoring for performance, data drift, and ongoing model improvements. Provide training and support for medical staff.
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