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Peer-reviewedCC BY 4.0Cells

Artificial Intelligence in Chronic Coronary Syndromes: Linking Cellular Pathways to Precision Risk Prediction

  • Ibrahim Antoun1,2
  • Georgia R. Layton3,4,5
  • Riyaz Somani1,3,4
  • Giovanni Mariscalco3
  • G. André Ng1,2,4,5
  • Mustafa Zakkar1,3,4
5 affiliations
  1. 1Division of Cardiovascular Sciences, University of Leicester, Leicester LE3 9QP, UK
  2. 2Department of Cardiology, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Leicester LE3 9QP, UK
  3. 3Department of Cardiac Surgery, University Hospitals of Leicester NHS Trust, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
  4. 4Leicester British Heart Foundation Centre of Research Excellence, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
  5. 5Leicester National Institute for Health and Care Research Biomedical Research Centre, Glenfield Hospital, Groby Road, Leicester LE3 9QP, UK
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Abstract

Chronic coronary syndromes (CCSs) are no longer defined only by fixed stenosis of the epicardial coronary arteries. CCSs reflect a dynamic biological process. Key contributors include endothelial activation, lipid retention, macrophage foam-cell formation, vascular smooth muscle cell phenotypic switching, and extracellular matrix remodelling. Microvascular rarefaction, perivascular adipose tissue inflammation, cardiometabolic comorbidity, and biological ageing add further complexity. This broader cellular and molecular model provides a rationale for artificial intelligence (AI), because conventional tools often assess anatomy, physiology, inflammation, and systemic biology in isolation. In this narrative review, we examine how AI and machine learning may support a more mechanistic and individualised approach to CCS by linking cellular pathways to measurable clinical signatures. We focus on AI-enabled plaque phenotyping, non-invasive physiology and ischaemia modelling, assessment of coronary microvascular dysfunction, perivascular inflammation mapping, and the integration of multi-omics with cardiometabolic risk profiling. Most of these tools, however, provide imaging-based or data-derived surrogates rather than direct measurements of endothelial activation, inflammation, or calcification biology. Within this limitation, current evidence suggests that AI can quantify subvisual high-risk plaque features, support non-invasive flow assessment, detect imaging patterns associated with microvascular dysfunction and perivascular inflammation, and, in research settings, relate molecular signatures to long-term cardiovascular risk. Clinical adoption remains constrained by retrospective study designs, limited external validation, heterogeneous imaging protocols, gaps in explainability, regulatory uncertainty, and algorithmic bias across sex, ethnicity, age, and resource settings. Future work should prioritise prospective trials, transparent reporting, federated validation, clinically interpretable outputs, and integration with guideline-based decision pathways. The greatest value of AI in CCS is likely to lie in augmenting mechanistic risk stratification and enabling precision prevention for patients whose risk is poorly captured by anatomy alone.

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Cite

BibTeXRIS
BibTeX
@article{antoun2026artificial,
  title = {{Artificial Intelligence in Chronic Coronary Syndromes: Linking Cellular Pathways to Precision Risk Prediction}},
  author = {Ibrahim Antoun and Georgia R. Layton and Riyaz Somani and Giovanni Mariscalco and G. André Ng and Mustafa Zakkar},
  journal = {Cells},
  year = {2026},
  month = nov,
  doi = {10.3390/cells15212011},
  url = {https://www.mdpi.com/2073-4409/15/21/2011},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Artificial Intelligence in Chronic Coronary Syndromes: Linking Cellular Pathways to Precision Risk Prediction
AU  - Ibrahim Antoun
AU  - Georgia R. Layton
AU  - Riyaz Somani
AU  - Giovanni Mariscalco
AU  - G. André Ng
AU  - Mustafa Zakkar
PY  - 2026
DA  - 2026/11/05
JO  - Cells
DO  - 10.3390/cells15212011
UR  - https://www.mdpi.com/2073-4409/15/21/2011
AB  - Chronic coronary syndromes (CCSs) are no longer defined only by fixed stenosis of the epicardial coronary arteries. CCSs reflect a dynamic biological process. Key contributors include endothelial activation, lipid retention, macrophage foam-cell formation, vascular smooth muscle cell phenotypic switching, and extracellular matrix remodelling. Microvascular rarefaction, perivascular adipose tissue inflammation, cardiometabolic comorbidity, and biological ageing add further complexity. This broader cellular and molecular model provides a rationale for artificial intelligence (AI), because conventional tools often assess anatomy, physiology, inflammation, and systemic biology in isolation. In this narrative review, we examine how AI and machine learning may support a more mechanistic and individualised approach to CCS by linking cellular pathways to measurable clinical signatures. We focus on AI-enabled plaque phenotyping, non-invasive physiology and ischaemia modelling, assessment of coronary microvascular dysfunction, perivascular inflammation mapping, and the integration of multi-omics with cardiometabolic risk profiling. Most of these tools, however, provide imaging-based or data-derived surrogates rather than direct measurements of endothelial activation, inflammation, or calcification biology. Within this limitation, current evidence suggests that AI can quantify subvisual high-risk plaque features, support non-invasive flow assessment, detect imaging patterns associated with microvascular dysfunction and perivascular inflammation, and, in research settings, relate molecular signatures to long-term cardiovascular risk. Clinical adoption remains constrained by retrospective study designs, limited external validation, heterogeneous imaging protocols, gaps in explainability, regulatory uncertainty, and algorithmic bias across sex, ethnicity, age, and resource settings. Future work should prioritise prospective trials, transparent reporting, federated validation, clinically interpretable outputs, and integration with guideline-based decision pathways. The greatest value of AI in CCS is likely to lie in augmenting mechanistic risk stratification and enabling precision prevention for patients whose risk is poorly captured by anatomy alone.
SN  - 2073-4409
ER  -