Background: AI is rapidly reshaping cardiovascular medicine, particularly within imaging-intensive fields such as interventional cardiology. Advances in machine learning and deep learning have enabled automated analysis of complex cardiovascular datasets and opened new opportunities for decision support across the coronary care continuum. Objectives: This review summarises current and emerging applications of AI in interventional cardiology, spanning pre-procedural imaging, intraprocedural guidance, outcome prediction, and future technological developments. Key findings: In the pre-procedural setting, AI-enhanced coronary CT angiography, AI-derived fractional flow reserve, and automated plaque characterisation improve risk stratification and patient selection for invasive angiography. Within the catheterisation laboratory, AI applications include automated angiographic interpretation, intravascular ultrasound and optical coherence tomography analysis, lesion classification, optimisation of stent sizing and deployment, emerging roles in structural heart interventions, and periprocedural haemodynamic monitoring. Machine learning models are also increasingly used for risk stratification, predicting bleeding, contrast-induced nephropathy, and mortality after percutaneous coronary intervention. Despite these advances, important challenges remain, including regulatory oversight, algorithm validation, bias mitigation, data governance, clinician education, and unresolved liability issues. Conclusion: AI is evolving from a diagnostic imaging adjunct to a comprehensive decision-support platform in interventional cardiology. Emerging technologies such as real-time AI-assisted catheterisation laboratory systems, multimodal data integration, digital twin modelling, and AI-guided robotic interventions may further transform coronary intervention workflows, positioning AI as a procedural co-pilot that augments rather than replaces physician expertise.
The role of the gut microbiome in modulating systemic health has become a focal point in recent research, particularly in the context of venous thromboembolism (VTE), which includes deep vein thrombosis and pulmonary embolism. This review explores the potential of microbiome analysis in VTE diagnostics, highlighting recent insights into microbiome profiles associated with thrombotic risk, mechanisms linking gut microbial alterations to clot formation, and emerging diagnostic applications. Microbiome-based diagnostics, through tools such as metabolite profiling and microbial risk scoring, represent a promising, non-invasive approach to identifying individuals at high risk for VTE. However, challenges such as inter-individual variability and the influence of external factors on the microbiome need to be addressed. Advancing our understanding of the microbiome’s influence on thrombotic risk could pave the way for innovative, personalised diagnostics and preventive strategies. This review proposes a framework for integrating microbiome-derived biomarkers into existing VTE diagnostic algorithms, highlighting opportunities for precision medicine.
