Key result
Artificial intelligence improves diagnostic accuracy across the ACS pathway—such as increasing occlusion MI detection to 80.6% vs 32.5% with STEMI criteria—though prospective outcome evidence remains limited.
Why the study?
Artificial intelligence is being investigated across the ACS care pathway, including early diagnosis, lesion characterization, procedural guidance, and risk prediction, warranting an examination of current evidence for clinically actionable and interventional applications.
Does artificial intelligence improve early diagnosis, culprit lesion characterization, coronary physiological assessment, and risk stratification in patients with suspected or established acute coronary syndromes?
Does artificial intelligence improve early diagnosis, culprit lesion characterization, coronary physiological assessment, and risk stratification in patients with suspected or established acute coronary syndromes?
Answers are grounded in this paper and cite their sources.
While AI substantially improves diagnostic discrimination, plaque phenotyping, and wire-free physiological assessment in ACS, prospective trials are now required to prove that acting on AI outputs improves patient outcomes.
Artificial intelligence (AI) is being investigated across the acute coronary syndrome (ACS) care pathway, including early diagnosis, coronary lesion characterization, procedural guidance, and risk prediction. This comprehensive review examines the current evidence across the entire ACS trajectory, with particular emphasis on clinically actionable and interventional applications. AI-enabled electrocardiography (ECG) may improve detection of myocardial infarction, including occlusion myocardial infarction (OMI) that does not meet conventional STEMI criteria. Models combining clinical data and high-sensitivity troponin may improve early rule-out and rule-in. In coronary imaging, AI can quantify plaque characteristics and assist with the interpretation of OCT and IVUS, while image-derived physiology may help assess non-culprit lesions. However, better diagnostic accuracy does not necessarily translate into better revascularization decisions. AI-based models can also estimate post-ACS ischemic, bleeding, and mortality risk, although evidence that their use improves treatment or outcomes remains limited. The next priority is prospective evaluation of whether these tools improve decisions, workflow, and patient outcomes across diverse clinical settings.
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Marchetta et al. (2026) conducted a review in Acute coronary syndromes (ACS). Artificial intelligence and machine learning applications (AI-ECG, biomarker integration, AI-CCTA, intracoronary imaging AI, and computational coronary physiology) vs. Conventional diagnostic pathways, standard ECG criteria, fixed troponin thresholds, manual imaging interpretation, and invasive pressure-wire FFR was evaluated. Artificial intelligence improves diagnostic accuracy across the ACS pathway—such as increasing occlusion MI detection to 80.6% vs 32.5% with STEMI criteria—though prospective outcome evidence remains limited.
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