Aortic valve stenosis (AS) is a prevalent and progressive valvular disease that poses significant diagnostic challenges, particularly in low-flow, low-gradient (LF-LG) states. Accurate assessment of AS severity is crucial for timely intervention and improved clinical outcomes. This narrative review critically evaluates the limitations of conventional echocardiographic techniques and explores the role of multimodal imaging—including advanced echocardiography, computed tomography (CT), and cardiac magnetic resonance (CMR)—in enhancing diagnostic accuracy. Special emphasis is placed on the unique challenges of LF-LG AS, where standard Doppler-derived assessments may misclassify disease severity, necessitating a more integrative diagnostic approach. By addressing these key diagnostic uncertainties and proposing a multimodal framework for improved assessment, this review provides a comprehensive update on best practices in AS evaluation, with the goal of optimizing clinical decision making and patient outcomes.

Coronary microvascular dysfunction (CMD) is presently recognized as a condition characterized by a reduction in coronary blood flow. CMD is associated with poor cardiac outcomes, and mounting evidence suggests that coronary microcirculation may be impaired in various cardiology pathologies. Non-invasive assessment of CMD remains challenging, and several imaging modalities—positron emission tomography (PET), computed tomography (CT), cardiac magnetic resonance (CMR), myocardial contrast echocardiography (MCE), and transthoracic Doppler echocardiography (TTE)—are proven to quantify coronary flow reserve (CFR) and myocardial blood flow (MBF), both valuable markers of CMD. However, each modality is limited by availability, standardization, and diagnostic utility. Furthermore, a growing number of studies have attempted to correlate CMD indices with systolic myocardial function markers, such as left ventricular ejection fraction, global longitudinal strain, and myocardial work indices. This review refers to the current evidence on CMD imaging and examines the association between CMD indices and systolic function in cardiovascular diseases. Understanding the impact of CMD on myocardial function is essential, as it highlights the coronary microcirculation impairment pathway as a potential target for therapeutic strategies.

The rapid expansion of digital technologies and artificial intelligence (AI) has profoundly transformed cardiovascular imaging, enabling more precise, efficient, and reproducible assessment of cardiac structure and function. This narrative review summarizes recent advances in AI-driven methods across echocardiography, cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging, with emphasis on image acquisition, automated quantification, and diagnostic and prognostic interpretation. We reviewed contemporary literature describing machine-learning and deep-learning applications for image reconstruction, segmentation, radiomics, and multimodal data integration. Current evidence demonstrates that AI improves image quality, reduces acquisition and analysis time, and enables automated, highly reproducible measurements of chamber volumes, function, tissue characterization, coronary anatomy, and myocardial perfusion, while facilitating advanced pattern recognition for differential diagnosis and risk stratification. Furthermore, digital platforms support remote acquisition, tele-echocardiography, and AI-assisted training of non-expert operators. Despite these advances, challenges remain regarding external validation, generalizability across vendors and populations, explainability, data governance, and regulatory compliance. In conclusion, AI and digital technologies are reshaping cardiovascular imaging by enhancing accuracy, efficiency, and accessibility, but their safe and effective clinical integration requires robust multicenter validation, transparent reporting, and ethical-legal frameworks that ensure trust, equity, and accountability.

Background/Objectives: Cardiorenal syndrome (CRS) is associated with substantially higher morbidity and mortality than either isolated cardiac or renal dysfunction. The application of classical and novel biomarkers has been tested in prompt diagnosis and monitoring of patients with CRS. Methods: This is a comprehensive literature review following a structured approach. We searched MEDLINE and Embase databases from January 2000 to December 2025. Results: The pathophysiology of CRS is complex, and the present review attempts to shed light on the clinical interpretation of the most widely used biomarkers as indices of diagnosis and prognosis. Among them, troponin is elevated in CRS and its absolute levels retain prognostic value, while changes in its levels over time may assist in the diagnosis of acute coronary syndrome. Natriuretic peptides are highly influenced by coexistence of chronic kidney disease (CKD) and in this context have considerable diagnostic and prognostic value. The combination of cystatin C, a biomarker of renal dysfunction, with cardiac biomarkers may create a powerful risk algorithm. Most recently, gene profiling and proteomics have the potential to stratify patients with CRS; however, more data from large cohorts are required for their validation. The therapeutic modulation of biomarkers in CRS patients may help elucidate the underlying pathophysiologic mechanisms. Sodium-glucose cotransporter-2 inhibitors (SGLT2i) have emerged as first-line therapy for patients with CRS, despite the fact their mechanisms are mostly unknown. Significant changes in the aforementioned biomarkers and the inflammatory factors may explain their emerging beneficial effects on both heart failure and CKD. Conclusions: The use of biomarkers has increased rapidly in recent years for diagnosis, surveillance and prognostic stratification in CRS.