Value of Machine Learning-based Coronary CT Fractional Flow Reserve Applied to Triple-Rule-Out CT Angiography in Acute Chest Pain

Revolutionising Acute Cardiac Care With Artificial Intelligence: Opportunities and Challenges

This article reviews the application of artificial intelligence (AI) in acute cardiac care, highlighting its potential to transform patient outcomes in the face of the global burden of cardiovascular diseases. It explores how AI algorithms can rapidly and accurately process data for the prediction and diagnosis of acute cardiac conditions. The review examines AI's impact on patient health across various diagnostic tools such as echocardiography, electrocardiography, coronary angiography, cardiac computed tomography, and magnetic resonance imaging, discusses the regulatory landscape for AI in health care, and categorises AI algorithms by their risk levels. Furthermore, it addresses the challenges of data quality, generalisability, bias, transparency, and regulatory considerations, underscoring the necessity for inclusive data and robust validation processes. The review concludes with future perspectives on integrating AI into clinical workflows and the ongoing need for research, regulation, and innovation to harness AI's full potential in improving acute cardiac care.

Predictive value of coronary artery computed tomography-derived fractional flow reserve for cardiovascular events in patients with coronary artery disease

BACKGROUND

Coronary computed tomography-derived fractional flow reserve (FFR-CT) assesses whether coronary artery lesions will result in myocardial ischemia. This study aimed to evaluate the predictive value of FFR-CT for cardiovascular events in patients with coronary artery disease (CAD).

METHODS

Data were collected retrospectively from patients with CAD who underwent FFR-CT at our hospital from January 2020 to February 2022 (1-year average follow-up). Patients were divided into ischemic (FFR-CT ≤ 0.80) and non-ischemic (FFR-CT > 0.80) groups. The incidence of endpoint events (cardiac death, acute myocardial infarction, unplanned revascularization, unstable angina, and stable angina) was calculated. The FFR-CT value was correlated with endpoint events using Cox regression models and Kaplan-Meier survival curves.

RESULTS

We recruited 134 patients (93 [69.4%] and 41 [30.6%] patients in the ischemic and non-ischemic groups, respectively). The ischemic group had a higher proportion of men, patients with type 2 diabetes and hypertension, and patients taking antiplatelet drugs and β‑blockers than did the non-ischemic group (all p < 0.05), whereas other parameters were comparable. Multivariate Cox regression analysis revealed no significant differences in cardiac death, acute myocardial infarction, unplanned revascularization, and unstable angina between the groups. The incidence of stable angina events (hazard ratio: 3.092, 95% confidence interval: 1.362-7.022, p = 0.007) was significantly higher in the ischemic group. Kaplan-Meier survival analysis revealed a significant difference in event-free survival for stable angina between the groups (p = 0.002).

CONCLUSION

In patients with CAD, FFR-CT showed an independent predictive value for stable angina within 1 year of examination.

Recent advances in artificial intelligence for cardiac CT: Enhancing diagnosis and prognosis prediction

Recent advances in artificial intelligence (AI) for cardiac computed tomography (CT) have shown great potential in enhancing diagnosis and prognosis prediction in patients with cardiovascular disease. Deep learning, a type of machine learning, has revolutionized radiology by enabling automatic feature extraction and learning from large datasets, particularly in image-based applications. Thus, AI-driven techniques have enabled a faster analysis of cardiac CT examinations than when they are analyzed by humans, while maintaining reproducibility. However, further research and validation are required to fully assess the diagnostic performance, radiation dose-reduction capabilities, and clinical correctness of these AI-driven techniques in cardiac CT. This review article presents recent advances of AI in the field of cardiac CT, including deep-learning-based image reconstruction, coronary artery motion correction, automatic calcium scoring, automatic epicardial fat measurement, coronary artery stenosis diagnosis, fractional flow reserve prediction, and prognosis prediction, analyzes current limitations of these techniques and discusses future challenges.

Artificial intelligence in atherosclerotic disease: Applications and trends

Atherosclerotic cardiovascular disease (ASCVD) is the most common cause of death globally. Increasing amounts of highly diverse ASCVD data are becoming available and artificial intelligence (AI) techniques now bear the promise of utilizing them to improve diagnosis, advance understanding of disease pathogenesis, enable outcome prediction, assist with clinical decision making and promote precision medicine approaches. Machine learning (ML) algorithms in particular, are already employed in cardiovascular imaging applications to facilitate automated disease detection and experts believe that ML will transform the field in the coming years. Current review first describes the key concepts of AI applications from a clinical standpoint. We then provide a focused overview of current AI applications in four main ASCVD domains: coronary artery disease (CAD), peripheral arterial disease (PAD), abdominal aortic aneurysm (AAA), and carotid artery disease. For each domain, applications are presented with refer to the primary imaging modality used [e.g., computed tomography (CT) or invasive angiography] and the key aim of the applied AI approaches, which include disease detection, phenotyping, outcome prediction, and assistance with clinical decision making. We conclude with the strengths and limitations of AI applications and provide future perspectives.

Diagnostic Performance of On-Site Computed Tomography Derived Fractional Flow Reserve on Non-Culprit Coronary Lesions in Patients with Acute Coronary Syndrome

The role of coronary computed tomography angiography (CCTA) derived fractional flow reserve (CT-FFR) in the assessment of non-culprit lesions (NCL) in patients with acute coronary syndrome (ACS) is debated. In this prospective clinical study, a total of 68 ACS patients with 89 moderate (30−70% diameter stenosis) NCLs were enrolled to evaluate the diagnostic accuracy of on-site CT-FFR compared to invasive fractional flow reserve (FFRi) and dobutamine stress echocardiography (DSE) as reference standards. CT-FFR and FFRi values ≤0.80, as well as new or worsening wall motion abnormality in ≥2 contiguous segments on the supplying area of an NCL on DSE, were considered positive for ischemia. Sensitivity, specificity, positive, and negative predictive value of CT-FFR relative to FFRi and DSE were 51%, 89%, 75%, and 74% and 37%, 77%, 42%, and 74%, respectively. CT-FFR value (β = 0.334, p < 0.001) and CT-FFR drop from proximal to distal measuring point [(CT-FFR drop), β = −0.289, p = 0.002)] were independent predictors of FFRi value in multivariate linear regression analysis. Based on comparing their receiver operating characteristics area under the curve (AUC) values, CT-FFR value and CT-FFR drop provided better discriminatory power than CCTA-based minimal lumen diameter stenosis to distinguish between an NCL with positive and negative FFRi [0.77 (95% Confidence Intervals, CI: 0.67−0.86) and 0.77 (CI: 0.67−0.86) vs. 0.63 (CI: 0.52−0.73), p = 0.029 and p = 0.043, respectively]. Neither CT-FFR value nor CT-FFR drop was predictive of regional wall motion score index at peak stress (β = −0.440, p = 0.441 and β = 0.403, p = 0.494) or was able to confirm ischemia on the territory of an NCL revealed by DSE (AUC = 0.54, CI: 0.43−0.64 and AUC = 0.55, CI: 0.44−0.65, respectively). In conclusion, on-site CT-FFR is superior to conventional CCTA-based anatomical analysis in the assessment of moderate NCLs; however, its diagnostic capacity is not sufficient to make it a gatekeeper to invasive functional evaluation. Moreover, based on its comparison with DSE, CT-FFR might not yield any information on the microvascular dysfunction in the territory of an NCL.

Coronary Computed Tomography Angiography-derived Fractional Flow Reserve: An Expert Consensus Document of Chinese Society of Radiology

Invasive fractional flow reserve (FFR) measured by a pressure wire is a reference standard for evaluating functional stenosis in coronary artery disease. Coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) uses advanced computational analysis methods to noninvasively obtain FFR results from a single conventional coronary computed tomography angiography data to evaluate the hemodynamic significance of coronary artery disease. More and more evidence has found good correlation between the results of noninvasive CT-FFR and invasive FFR. CT-FFR has proven its potential in optimizing patient management, improving risk stratification and prognosis, and reducing total health care costs. However, there is still a lack of standardized interpretation of CT-FFR technology in real-world clinical settings. This expert consensus introduces the principle, workflow, and interpretation of CT-FFR; summarizes the state-of-the-art application of CT-FFR; and provides suggestions and recommendations for the application of CT-FFR with the aim of promoting the standardized application of CT-FFR in clinical practice.

Radiomics in Cardiovascular Disease Imaging: from Pixels to the Heart of the Problem

Purpose of Review

This review of the literature aims to present potential applications of radiomics in cardiovascular radiology and, in particular, in cardiac imaging.

Recent Findings

Radiomics and machine learning represent a technological innovation which may be used to extract and analyze quantitative features from medical images. They aid in detecting hidden pattern in medical data, possibly leading to new insights in pathophysiology of different medical conditions. In the recent literature, radiomics and machine learning have been investigated for numerous potential applications in cardiovascular imaging. They have been proposed to improve image acquisition and reconstruction, for anatomical structure automated segmentation or automated characterization of cardiologic diseases.

Summary

The number of applications for radiomics and machine learning is continuing to rise, even though methodological and implementation issues still limit their use in daily practice. In the long term, they may have a positive impact in patient management.

Current and Future Applications of Artificial Intelligence in Coronary Artery Disease

Cardiovascular diseases (CVDs) carry significant morbidity and mortality and are associated with substantial economic burden on healthcare systems around the world. Coronary artery disease, as one disease entity under the CVDs umbrella, had a prevalence of 7.2% among adults in the United States and incurred a financial burden of 360 billion US dollars in the years 2016–2017. The introduction of artificial intelligence (AI) and machine learning over the last two decades has unlocked new dimensions in the field of cardiovascular medicine. From automatic interpretations of heart rhythm disorders via smartwatches, to assisting in complex decision-making, AI has quickly expanded its realms in medicine and has demonstrated itself as a promising tool in helping clinicians guide treatment decisions. Understanding complex genetic interactions and developing clinical risk prediction models, advanced cardiac imaging, and improving mortality outcomes are just a few areas where AI has been applied in the domain of coronary artery disease. Through this review, we sought to summarize the advances in AI relating to coronary artery disease, current limitations, and future perspectives.

Radiogenomics and Artificial Intelligence Approaches Applied to Cardiac Computed Tomography Angiography and Cardiac Magnetic Resonance for Precision Medicine in Coronary Heart Disease: A Systematic Review

The risk of coronary heart disease (CHD) clinical manifestations and patient management is estimated according to risk scores accounting multifactorial risk factors, thus failing to cover the individual cardiovascular risk. Technological improvements in the field of medical imaging, in particular, in cardiac computed tomography angiography and cardiac magnetic resonance protocols, laid the development of radiogenomics. Radiogenomics aims to integrate a huge number of imaging features and molecular profiles to identify optimal radiomic/biomarker signatures. In addition, supervised and unsupervised artificial intelligence algorithms have the potential to combine different layers of data (imaging parameters and features, clinical variables and biomarkers) and elaborate complex and specific CHD risk models allowing more accurate diagnosis and reliable prognosis prediction. Literature from the past 5 years was systematically collected from PubMed and Scopus databases, and 60 studies were selected. We speculated the applicability of radiogenomics and artificial intelligence through the application of machine learning algorithms to identify CHD and characterize atherosclerotic lesions and myocardial abnormalities. Radiomic features extracted by cardiac computed tomography angiography and cardiac magnetic resonance showed good diagnostic accuracy for the identification of coronary plaques and myocardium structure; on the other hand, few studies exploited radiogenomics integration, thus suggesting further research efforts in this field. Cardiac computed tomography angiography resulted the most used noninvasive imaging modality for artificial intelligence applications. Several studies provided high performance for CHD diagnosis, classification, and prognostic assessment even though several efforts are still needed to validate and standardize algorithms for CHD patient routine according to good medical practice.

Prospects for cardiovascular medicine using artificial intelligence

As the importance of artificial intelligence (AI) in the clinical setting increases, the need for clinicians to understand AI is also increasing. This review focuses on the fundamental principles of AI and the current state of cardiovascular AI. Various types of cardiovascular AI have been developed for evaluating examinations such as X-rays, electrocardiogram, echocardiography, computed tomography, and magnetic resonance imaging. Cardiovascular AI achieves high accuracy in diagnostic support and prognosis prediction. Furthermore, it can even detect abnormalities that were previously difficult for cardiologists to detect. Randomized controlled trials begin to be reported to verify the usefulness of cardiovascular AI. The day is approaching when cardiovascular AI will be commonly used in clinical practice. Various types of medical AI will be used for cardiovascular care; however, it will not replace medical doctors. We need to understand the strengths and weaknesses of medical AI so that cardiologists can effectively use AI to improve the medical care of patients.