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Enterprise AI Analysis: Carotid atherosclerotic plaque vulnerability assessment from angiography-derived radial wall strain validated by MRI

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.

0 RWSmax Cutoff for Vulnerability

Optimal threshold identified for radial wall strain to predict plaque vulnerability.

0 Diagnostic Accuracy (AUC)

Area Under Curve for RWSmax in identifying vulnerable plaques.

0 Symptomatic Vulnerable Plaques

Percentage of symptomatic patients with vulnerable plaques.

0 Sensitivity

Sensitivity of RWSmax at optimal cutoff for vulnerability detection.

Deep Analysis & Enterprise Applications

Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.

Overview
Medical Imaging

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.

16.786x Increased Risk of Vulnerability with RWSmax ≥ 14.9%

The odds ratio for plaque instability when RWSmax values are ≥ 14.9% compared to values < 14.9%.

Enterprise Process Flow

Patient Screening & Inclusion
DSA & HR-VWI Data Collection
RWSmax Calculation (DSA)
Plaque Vulnerability Classification (HR-VWI)
Correlation & Consistency Analysis
Optimal RWSmax Threshold Determination
Clinical Symptom Correlation
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.

Estimated Annual Savings $500,000
Annual Hours Reclaimed 25,000

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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