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Is (AI-guided) coronary CT angiography ready for prime time in diabetes care and research?

Abstract Due to its increasing prevalence, the burden of diabetes-attributable cardiovascular disease is projected to increase substantially. At the same time, there is a growing need to better identify patients at the highest cardiovascular risk and to optimize the use of increasingly available—but costly—weight-, glucose-, and lipid-lowering therapies. We postulate that coronary computed tomography angiography (CCTA), potentially enhanced by novel artificial intelligence (AI)-based image analysis, could serve…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1822· cite & verify
Is (AI-guided) coronary CT angiography ready for prime time in diabetes care and research?
"2018.11.03 Continuous Glucose Monitor 14 day, Washington, DC USA 3458" by tedeytan is licensed under CC BY-SA 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/2.0/.

In brief

This perspective article argues that coronary CT angiography, potentially paired with AI-based image analysis, could help diabetes specialists quantify coronary artery disease burden in patients who face some of the highest cardiovascular risk. Falling cost and radiation exposure are cited as making the scan more practical.

The idea matters because diabetes-related cardiovascular disease is projected to grow and costly weight-, glucose-, and lipid-lowering drugs need better targeting. The piece is a proposal rather than a measured result, so real-world benefit, handling of contrast use, and incidental findings remain open questions.

Main points

  1. Diabetes-attributable cardiovascular disease burden is projected to rise substantially because of increasing prevalence.
  2. CCTA is presented as more feasible due to reductions in cost and radiation exposure.
  3. AI-guided analysis is suggested to cut radiologist workload and improve reproducibility.
  4. CCTA-derived endpoints might complement or partly replace major adverse cardiovascular event outcomes in trials.
  5. The authors discuss open issues including intravenous contrast use and management of incidental findings or significant stenoses.

The gain

AI-based image analysis of coronary CT angiography could reduce radiologist workload and improve reproducibility, making cardiovascular risk assessment in high-risk diabetes patients more scalable.

The rundown

The authors say there is a growing need to identify patients at highest cardiovascular risk and to use expensive therapies more efficiently. They postulate that CCTA could serve diabetologists as a tool for assessing atherosclerotic risk.

For research, they explore using CCTA measures as trial endpoints, because declining event rates mean traditional outcome trials increasingly require large sample sizes. A design combining non-inferiority on hard endpoints with superiority on CCTA measures is suggested as a way to reduce costs and speed evidence generation.

The article frames its conclusions as a discussion of promise and current limitations, stating that integrating AI-guided CCTA may enable more precise and scalable risk stratification in diabetes care.

Sources

  1. Peer-reviewedCardiovascular Diabetology2026-10-17

The debate