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Updated: May 26, 2026

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

Published on: August 12, 2025

Exploring a Prediction Model Based on Patient Record Data in Carotid Stenosis Risk Assessment.

Kjersti Hervik^1,2, Tom Wilsgaard³, Truls Myrmel^1,2

¹Department of Heart, Lung and Vascular Surgery, University Hospital of North Norway, Tromsø, Norway.

Vascular Health and Risk Management

|May 25, 2026

Summary

This summary is machine-generated.

This study explores using artificial intelligence to analyze free-text patient records for improved stroke risk prediction in carotid stenosis patients. An advanced statistical model demonstrated better risk prediction than traditional methods.

Keywords:

carotid endarterectomy carotid stenosis multiple endpoint analysis patient journal data stroke time dependent variables

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Last Updated: May 26, 2026

A Magnetic Resonance Imaging-based Computational Protocol for Analysis of Plaque Morphology and Hemodynamics in Patients with Carotid Artery Stenosis

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Published on: May 15, 2020

Area of Science:

Medical Informatics
Cardiovascular Research
Artificial Intelligence in Medicine

Background:

Digital patient records offer vast real-world data potential, but confidentiality issues have hindered big data extraction.
Artificial intelligence (AI) may enable data extraction from free-text records while preserving patient confidentiality.
Improved risk prediction models are needed, particularly for conditions like carotid stenosis.

Purpose of the Study:

To investigate the suitability of free-text patient data for risk prediction in carotid stenosis.
To evaluate a novel statistical model for incorporating free-text data into clinical risk prediction tools.
To compare the performance of the proposed model against traditional Cox regression analysis for stroke risk.

Main Methods:

A test dataset was manually created from free-text data of patients with carotid stenosis.
Parameters were extracted from digital patient records to assess individual risk profiles.
An extended regression model, accounting for time dependency and multiple endpoints, was evaluated against Cox regression.

Main Results:

The extended regression model identified a statistically significant risk associated with high-grade carotid stenosis lesions for stroke.
For 70-99% stenosis, asymptomatic patients showed hazard ratios of 4.98 (right-sided) and 5.23 (left-sided) for ipsilateral events.
Symptomatic patients had a hazard ratio of 2.25 for recurrent events, suggesting improved prediction with the advanced model.

Conclusions:

Free-text data, when analyzed with appropriate statistical models, can be a valuable source for risk prediction.
The proposed advanced statistical model offers enhanced stroke risk prediction capabilities for patients with carotid stenosis.
This approach holds promise for developing clinically applicable risk prediction tools using real-world, unstructured patient data.