The coronary angiogram is the gold standard for evaluating the severity of coronary artery disease stenoses. Presently, the assessment is conducted visually by cardiologists, a method that lacks standardization. This study introduces DeepCoro, a ground-breaking AI-driven pipeline that integrates advanced vessel tracking and a video-based Swin3D model that was trained and validated on a dataset comprised of 182,418 coronary angiography videos spanning 5 years. DeepCoro achieved a notable precision of 71.89% in identifying coronary artery segments and demonstrated a mean absolute error of 20.15% (95% CI: 19.88–20.40) and a classification AUROC of 0.8294 (95% CI: 0.8215–0.8373) in stenosis percentage prediction compared to traditional cardiologist assessments. When compared to two expert interventional cardiologists, DeepCoro achieved lower variability than the clinical reports (19.09%; 95% CI: 18.55–19.58 vs 21.00%; 95% CI: 20.20–21.76, respectively). In addition, DeepCoro can be fine-tuned to a different modality type. When fine-tuned on quantitative coronary angiography assessments, DeepCoro attained an even lower mean absolute error of 7.75% (95% CI: 7.37–8.07), underscoring the reduced variability inherent to this method. This study establishes DeepCoro as an innovative video-based, adaptable tool in coronary artery disease analysis, significantly enhancing the precision and reliability of stenosis assessment.

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

The Gerbode defect is a rare abnormal communication between the left ventricle (LV) and right atrium (RA). The lesion is either congenital or acquired. Acquired defects are largely iatrogenic or infective in origin. We present two cases of acquired Gerbode defects with similar clinical presentations but very different outcomes. Patient 1 A 64-year-old male presented with features of decompensated cardiac failure and a low-grade temperature. Dehiscence of a recently implanted bioprosthetic aortic valve and high-velocity LV to RA jet (Gerbode defect) was found on echocardiography. Blood cultures grew Staphylococcus warneri and the diagnosis of infective endocarditis was established. The patient was treated with intravenous antibiotics and the aortic valve and Gerbode defect were successfully surgically repaired. Patient 2 An 81-year-old male presented after being found on the floor at home. On admission, he was clinically septic with evidence of decompensated heart failure. No clear infective focus was initially found. Transthoracic echocardiography revealed severe left ventricular impairment, with a normal bioprosthetic aortic valve. He was treated with intravenous antibiotics, but later deteriorated with evidence of embolic phenomena. Repeat echocardiography revealed a complex infective aortic root lesion with bioprosthetic valve dehiscence and flow demonstrated from the LV to RA. Unfortunately, the patient succumbed to the infection and cardiac complications. The Gerbode defect is a rare but important complication of infective endocarditis and valve surgery. Care needs to be taken to assess for Gerbode defect shunts on echocardiogram, especially in the context of previous cardiac surgery.

The number of cancer survivors in the United States is projected to increase by 31% by 2030. With advances in early screening, diagnosis and therapeutic strategies, a steadily increasing number of patients are surviving cancer. Coronary artery disease (CAD) is now one of the leading causes of death amongst cancer survivors, with the latter group of patients having a higher risk of CAD compared to the general population. Our review covers a range of specific challenges faced by doctors when considering percutaneous coronary interventions (PCI) in cancer patients; clinical outcomes in cancer patients undergoing PCI, as well as some important technical considerations to be made when making decisions regarding the management strategy in this special population of patients.

Background: Percutaneous coronary intervention (PCI) for bifurcation lesions can be technically challenging and is associated with higher risk. There is little data on sex‐based differences in strategy and outcomes in bifurcation PCI.Aims: We sought to assess whether differences exist between women and men in the treatment and outcomes of bifurcation PCI.Methods: We collected data on 4006 patients undergoing bifurcation PCI, from the e‐ULTIMASTER study, a prospective, multicentre study enrolling patients from 2014 to 2018. We divided the bifurcation cohort according to sex, with 1‐year follow‐up of outcomes (target lesion failure [TLF], target vessel failure [TVF], and patient‐oriented composite endpoint [POCE]).Findings: Women were older (69.2 ± 10.9 years vs. 64.4 ± 11.0 years), with a greater burden of cardiovascular comorbidities. For true and non‐true bifurcation lesions, women and men were equally likely to undergo a single stent approach (true: 63.2% vs. 63.6%, p = 0.79, non‐true: 95.4% vs. 94.3%, p = 0.32), with similar rates of final kissing balloon (FKB) (37.2% vs. 35.5%, p = 0.36) and proximal optimization (POT) (34.4% vs. 34.2%, p = 0.93) in cases where two stents were used. Lastly, after propensity score matching, there was no difference between women and men in the incidence of the composite endpoints of TLF (5.5% vs. 5.2%, RR 1.05 [95% CI 0.77−1.44], p = 0.75), TVF (6.2% vs. 6.3%, RR 0.99 [95% CI 0.74−1.32], p = 0.96), and POCE (9.9% vs. 9.5%, RR 1.05 [95% CI 0.83−1.31], p = 0.70).Conclusion: In this contemporary, real‐world study of bifurcation PCI, we report no difference in stent strategy between women and men, with similar outcomes at 1‐year.

Background: In the last decade, percutaneous coronary intervention (PCI) has evolved toward the treatment of complex disease in patients with multiple comorbidities. Whilst there are several definitions of complexity, it is unclear whether there is agreement between cardiologists in classifying complexity of cases. Inconsistent identification of complex PCI can lead to significant variation in clinical decision‐making.Aim: This study aimed to determine the inter‐rater agreement in rating the complexity and risk of PCI procedures.Method: An online survey was designed and disseminated amongst interventional cardiologists by the European Association of Percutaneous Cardiovascular Intervention (EAPCI) board. The survey presented four patient vignettes, with study participants assessing these cases to classify their complexity.Results: From 215 respondents, there was poor inter‐rater agreement in classifying the complexity level (k = 0.1) and a fair agreement (k = 0.31) in classifying the risk level. The experience level of participants did not show any significant impact on the inter‐rater agreement of rating the complexity level and the risk level. There was good level of agreement between participants in terms of rating 26 factors for classifying complex PCI. The top five factors were (1) impaired left ventricular function, (2) concomitant severe aortic stenosis, (3) last remaining vessel PCI, (4) requirement fort calcium modification and (5) significant renal impairment.Conclusion: Agreement among cardiologists in classifying complexity of PCI is poor, which may lead to suboptimal clinical decision‐making, procedural planning as well as long‐term management. Consensus is needed to define complex PCI, and this requires clear criteria incorporating both lesion and patient characteristics.