Background: This narrative review introduces the “From Plaque to Perfusion” framework, a clinically pragmatic approach that maps multimodality imaging technologies to critical decision points in the acute coronary syndrome (ACS) patient journey. By integrating non-invasive assessment, invasive procedural guidance, and post-event tissue characterisation, this framework provides a structured pathway for deep phenotyping of ACS. Artificial intelligence (AI) is highlighted as an essential enabling layer that enhances diagnostic precision, automates quantification, and supports scalable, data-driven care. Contemporary ACS management pathways, while effective, often leave residual clinical uncertainty. The diagnostic objective has evolved beyond confirming myocardial injury to comprehensively phenotyping the entire ACS cascade: defining the plaque substrate, identifying the culprit mechanism, and quantifying the myocardial consequence. This requires a systematic integration of advanced imaging modalities. Methods: This narrative review is based on a comprehensive literature search of major medical databases (PubMed/MEDLINE, Scopus, Embase, Google Scholar) for high-level evidence, including randomized controlled trials, meta-analyses, and international expert consensus documents published between January 2010 and February 2026. Results: The “From Plaque to Perfusion” framework consists of three core stages. First, non-invasive assessment with coronary computed tomography angiography (CCTA), fractional flow reserve (FFR-CT), and PET-CT defines plaque substrate and vascular inflammation. Second, invasive precision in the catheterization laboratory, guided by optical coherence tomography (OCT) and intravascular ultrasound (IVUS), resolves the culprit mechanism and optimizes percutaneous coronary intervention (PCI). Third, post-event tissue characterization with cardiac magnetic resonance (CMR) quantifies myocardial injury and refines prognosis. AI-driven platforms are shown to enhance each stage by automating analysis, standardizing interpretation, and providing actionable metrics for clinical decisions, including complex scenarios like Myocardial Infarction with Non-Obstructive Coronary Arteries (MINOCA). Conclusions: The “From Plaque to Perfusion” framework, enabled by AI, reframes ACS imaging as an integrated, mechanism-driven pathway. This approach moves beyond isolated test interpretation toward a scalable model of precision, phenotype-led care that promises to improve diagnostic certainty and personalize patient management.
Shahin et al. (Sat,) studied this question.