Abstract Purpose of Review Artificial intelligence (AI) applications in (interventional) cardiology continue to emerge. This review summarizes the current state and future perspectives of AI for automated imaging analysis in invasive coronary angiography (ICA). Recent Findings Recently, 12 studies on AI for automated imaging analysis In ICA have been published. In these studies, machine learning (ML) models have been developed for frame selection, segmentation, lesion assessment, and functional assessment of coronary flow. These ML models have been developed on monocenter datasets (in range 31–14,509 patients) and showed moderate to good performance. However, only three ML models were externally validated. Summary Given the current pace of AI developments for the analysis of ICA, less-invasive, objective, and automated diagnosis of CAD can be expected in the near future. Further research on this technology in the catheterization laboratory may assist and improve treatment allocation, risk stratification, and cath lab logistics by integrating ICA analysis with other clinical characteristics.
Introduction: Women undergoing transcatheter aortic valve implantation (TAVI) for severe aortic stenosis are more likely to have a small aortic annulus compared with men. However, there is lack of data on the prognostic relevance of bioprostheses size in women. Hypothesis: To assess the impact of a small valve size on clinical outcomes in women undergoing TAVI. Methods: The Women's INternational Transcatheter Aortic Valve Implantation (WIN-TAVI) registry was the first study to investigate the safety and efficacy of TAVI in women with aortic stenosis.Patients were stratified according to the size of aortic valve bioprosthesis, with small valve defined by a size≤23 mm and non-small valve defined by a size>23 mm. The primary outcome of interests was the occurrence of Valve Academic Research Consortium (VARC)-2-efficacy endpoint, a composite of all-cause death, stroke, myocardial infarction, hospitalization for valve-related symptoms or heart failure or valve-related dysfunction at 1-year follow-up. Results: Out of 934 patients, 388 (41.5%) received a small valve and had a lower body mass index, lower surgical risk scores and were less likely to suffer from atrial fibrillation as compared with patients receiving a larger valve. The latter were more likely to require post-dilation (18.6% vs. 10.4%, p<0.001) and had a trend for higher rates of residual aortic regurgitation grade ≥2 (8.7% vs. 5.1%, p = 0.051). At 1 year, the risk of the VARC-2 efficacy endpoint was similar between the two groups (16.0% in small valve vs. 16.3% in non-small valve, p = 0.8808; adjusted hazard ratio 1.34, 95% confidence interval 0.90 to 2.00), Figure. No significant differences in the occurrence of the secondary endpoints were noted after multivariable adjustment. Conclusions: Among women undergoing TAVI, the valve size was associated with different baseline and procedural characteristics but was not an independent predictor of clinical outcomes.
