Truace
Subscribe

New AI tool aims to flag risky heart plaques from routine angiograms, skipping costly imaging

Study title: Radial wall strain for residual risk stratification after percutaneous coronary intervention

Background Percutaneous coronary intervention effectively treats flow-limiting stenoses; however, untreated non-target vessels with vulnerable plaques remain a major contributor to future adverse cardiovascular events. Plaque strain is a promising marker of plaque vulnerability but traditionally requires complex modelling and expensive intracoronary imaging.

Read the study abstract

Background Percutaneous coronary intervention effectively treats flow-limiting stenoses; however, untreated non-target vessels with vulnerable plaques remain a major contributor to future adverse cardiovascular events. Plaque strain is a promising marker of plaque vulnerability but traditionally requires complex modelling and expensive intracoronary imaging. We developed a novel artificial intelligence algorithm for subpixel-level lumen delineation, enabling real-time automated assessment of radial wall strain (…

Truvace record1 min read1 sourceEvidence: Moderate · 68/100 — Controlled or validated comparison · 1 independent sourcePeer-reviewedTRV-2026-1167 · cite or check it
Radial wall strain for residual risk stratification after percutaneous coronary intervention"Iraqi Police Build Relationships in Basra" by DVIDSHUB, CC BY 2.0.

In brief

We developed a novel artificial intelligence algorithm for subpixel-level lumen delineation, enabling real-time automated assessment of radial wall strain (RWS) from routine angiography. A prespecified cutoff value of maximal RWS (RWS max ) ≥13% was used to define increased plaque vulnerability.

Main points

  1. Background Percutaneous coronary intervention effectively treats flow-limiting stenoses; however, untreated non-target vessels with vulnerable plaques remain a major contributor to future adverse cardiovascular events.
  2. Plaque strain is a promising marker of plaque vulnerability but traditionally requires complex modelling and expensive intracoronary imaging.
  3. We developed a novel artificial intelligence algorithm for subpixel-level lumen delineation, enabling real-time automated assessment of radial wall strain (RWS) from routine angiography.

The gain

An AI algorithm enables real-time automated radial wall strain assessment from routine angiography, avoiding complex modelling and costly intracoronary imaging, to flag vulnerable plaques.

The full story

Background Percutaneous coronary intervention effectively treats flow-limiting stenoses; however, untreated non-target vessels with vulnerable plaques remain a major contributor to future adverse cardiovascular events. Plaque strain is a promising marker of plaque vulnerability but traditionally requires complex modelling and expensive intracoronary imaging.

We developed a novel artificial intelligence algorithm for subpixel-level lumen delineation, enabling real-time automated assessment of radial wall strain (RWS) from routine angiography. Aims This post hoc study sought to determine the prognostic value of RWS for predicting future cardiac events in non-target vessels over 5 years, using the high-quality TARGET All Comers randomised trial.

Sources

  1. Peer-reviewedEuroIntervention2026-09-21

The debate