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Artificial Intelligence for Cardiac Ischemia Detection: A Review of Biosensor, Biomarker, and Imaging Applications

Ischemic heart disease is the leading cause of global mortality, yet accurate and timely detection remains constrained by incomplete signal capture, noise, operator dependence, and imperfect reference standards. Artificial intelligence (AI) may enhance cardiovascular sensing by extracting latent diagnostic information from routinely acquired signals. This narrative review examines AI-enabled approaches to ischemia detection across three sensing modalities: the electrocardiogram as a bioelectric sensor, cardiac t…

By the Truvace desk2 min read1 sourcePeer-reviewedTRV-2026-1434· cite & verify
Artificial Intelligence for Cardiac Ischemia Detection: A Review of Biosensor, Biomarker, and Imaging Applications
Cardio Surgery 1 by Dominique Cappronnier. CC BY-SA 3.0 · https://creativecommons.org/licenses/by-sa/3.0

In brief

This narrative review surveys how AI is being applied to detect cardiac ischemia through three kinds of sensing: the electrocardiogram as a bioelectric sensor, cardiac troponin as a biochemical marker, and imaging such as echocardiography, CT, MRI and nuclear imaging.

The review matters because ischemic heart disease is the leading cause of global mortality, so better and earlier detection could be consequential. The evidence is still thin, though: it rests mainly on retrospective diagnostic-performance studies, and benefits for workflow, patient outcomes and cost remain largely unmeasured.

Main points

  1. Ischemic heart disease is described as the leading cause of global mortality, and detection is limited by incomplete signal capture, noise, operator dependence and imperfect reference standards.
  2. The review covers three modalities: ECG, cardiac troponin, and cardiac imaging including echocardiography, CT, MRI and nuclear imaging.
  3. For each modality the authors assess the sensing pipeline, reference standards, the endpoint measured (from acute coronary occlusion to functional ischemia) and reported performance.
  4. Emerging directions include foundation models and multimodal fusion.
  5. Adoption will need prospective multicenter trials, standardized benchmarks and evidence of clinical and economic benefit.

The gain

AI can pull latent diagnostic information from routinely acquired cardiac signals, which could improve ischemia detection where noise, operator dependence and incomplete signal capture limit accuracy.

The rundown

The authors separate diagnostic accuracy from downstream effects on clinical workflow, patient outcomes and cost, and they note that the endpoint being detected differs across studies, ranging from acute coronary occlusion to functional ischemia.

They discuss translational barriers including limited prospective validation, regulatory hurdles and uncertain cost-effectiveness, alongside newer directions such as foundation models and multimodal fusion.

Most of the evidence comes from retrospective, frequently single-center studies of diagnostic performance. Few studies have measured workflow effects, and fewer still have measured patient outcomes or cost.

Sources

  1. Peer-reviewedSensors2026-10-14

The debate