Cardiovascular Imaging, Climate Change, and Environmental Sustainability.

Environmental exposures including poor air quality and extreme temperatures are exacerbated by climate change and are associated with adverse cardiovascular outcomes. Concomitantly, the delivery of health care generates substantial atmospheric greenhouse gas (GHG) emissions contributing to the climate crisis. Therefore, cardiac imaging teams must be aware not only of the adverse cardiovascular health effects of climate change, but also the downstream environmental ramifications of cardiovascular imaging. The purpose of this review is to highlight the impact of climate change on cardiovascular health, discuss the environmental impact of cardiovascular imaging, and describe opportunities to improve environmental sustainability of cardiac MRI, cardiac CT, echocardiography, cardiac nuclear imaging, and invasive cardiovascular imaging. Overarching strategies to improve environmental sustainability in cardiovascular imaging include prioritizing imaging tests with lower GHG emissions when more than one test is appropriate, reducing low-value imaging, and turning equipment off when not in use. Modality-specific opportunities include focused MRI protocols and low-field-strength applications, iodine contrast media recycling programs in cardiac CT, judicious use of US-enhancing agents in echocardiography, improved radiopharmaceutical procurement and waste management in nuclear cardiology, and use of reusable supplies in interventional suites. Finally, future directions and research are highlighted, including life cycle assessments over the lifespan of cardiac imaging equipment and the impact of artificial intelligence tools. Keywords: Heart, Safety, Sustainability, Cardiovascular Imaging Supplemental material is available for this article. © RSNA, 2024.

Perfect Match: Radiomics and Artificial Intelligence in Cardiac Imaging.

Cardiovascular diseases remain a significant health burden, with imaging modalities like echocardiography, cardiac computed tomography, and cardiac magnetic resonance imaging playing a crucial role in diagnosis and prognosis. However, the inherent heterogeneity of these diseases poses challenges, necessitating advanced analytical methods like radiomics and artificial intelligence. Radiomics extracts quantitative features from medical images, capturing intricate patterns and subtle variations that may elude visual inspection. Artificial intelligence techniques, including deep learning, can analyze these features to generate knowledge, define novel imaging biomarkers, and support diagnostic decision-making and outcome prediction. Radiomics and artificial intelligence thus hold promise for significantly enhancing diagnostic and prognostic capabilities in cardiac imaging, paving the way for more personalized and effective patient care. This review explores the synergies between radiomics and artificial intelligence in cardiac imaging, following the radiomics workflow and introducing concepts from both domains. Potential clinical applications, challenges, and limitations are discussed, along with solutions to overcome them.

Evaluation of responses to cardiac imaging questions by the artificial intelligence large language model ChatGPT.

PURPOSE: To assess ChatGPT's ability as a resource for educating patients on various aspects of cardiac imaging, including diagnosis, imaging modalities, indications, interpretation of radiology reports, and management. METHODS: 30 questions were posed to ChatGPT-3.5 and ChatGPT-4 three times in three separate chat sessions. Responses were scored as correct, incorrect, or clinically misleading categories by three observers-two board certified cardiologists and one board certified radiologist with cardiac imaging subspecialization. Consistency of responses across the three sessions was also evaluated. Final categorization was based on majority vote between at least two of the three observers. RESULTS: ChatGPT-3.5 answered seventeen of twenty eight questions correctly (61 %) by majority vote. Twenty one of twenty eight questions were answered correctly (75 %) by ChatGPT-4 by majority vote. Majority vote for correctness was not achieved for two questions. Twenty six of thirty questions were answered consistently by ChatGPT-3.5 (87 %). Twenty nine of thirty questions were answered consistently by ChatGPT-4 (97 %). ChatGPT-3.5 had both consistent and correct responses to seventeen of twenty eight questions (61 %). ChatGPT-4 had both consistent and correct responses to twenty of twenty eight questions (71 %). CONCLUSION: ChatGPT-4 had overall better performance than ChatGTP-3.5 when answering cardiac imaging questions with regard to correctness and consistency of responses. While both ChatGPT-3.5 and ChatGPT-4 answers over half of cardiac imaging questions correctly, inaccurate, clinically misleading and inconsistent responses suggest the need for further refinement before its application for educating patients about cardiac imaging.

The Role of Multimodality Imaging in the Evaluation of Heart Failure and Surgical Transplant Planning of Patients with Adult Congenital Heart Disease.

Cardiac imaging is pivotal in evaluating ventricular function, residual lesions, and long-term complications in patients with adult congenital heart disease (ACHD). Longitudinal imaging in ACHD is key for the timely identification of patients requiring evaluation for advanced therapies. The guidelines recommend routine imaging surveillance. In all patients undergoing evaluation with cardiac imaging, it is critical that studies are performed at centers with expertise and that the imaging protocols are tailored to the specific condition. The authors briefly highlight the utility and diagnostic yield of different modalities, review pertinent considerations for special populations, and focus on imaging for transplant planning.

How Machine Learning Might Help Improve Cardiac Imaging.

In this Medical News interview, University of California, San Francisco, cardiologist Rima Arnaout, joins JAMA Editor in Chief Kirsten Bibbins-Domingo to discuss the transformative potential of AI on cardiac imaging.

European Association of Cardiovascular Imaging survey on the evaluation of mitral regurgitation.

AIMS: To evaluate the diagnosis and imaging of patients with mitral regurgitation (MR) and the management in routine clinical practice across Europe, the European Association of Cardiovascular Imaging Scientific Initiatives Committee performed a survey across European centres. In particular, the routine use of echocardiography, advanced imaging modalities, heart valve clinics, and heart valve teams was explored. METHODS AND RESULTS: A total of 61 responders, mainly from tertiary centres or university hospitals, from 26 different countries responded to the survey, which consisted of 22 questions. For most questions related to echocardiography and advanced imaging, the answers were relatively homogeneous and demonstrated good adherence to current recommendations. In particular, the centres used a multi-parametric echocardiographic approach and selected the effective regurgitant orifice and vena contracta width as their preferred assessments. 2D measurements are still the most widely used parameters to assess left ventricular structure; however, the majority use 3D trans-oesophageal echocardiography (TOE) to evaluate valve morphology in severe MR. The majority of centres reported the onsite availability and clinical use of ergometric stress echocardiography, cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR) imaging. Heart valve clinics and heart valve teams were also widely prevalent. CONCLUSION: Consistent with current guidelines, echocardiography (transthoracic echocardiography and TOE) remains the first-line and central imaging modality for the assessment of MR although the complementary use of 3D TOE, CCT, and CMR appears to be growing. Heart valve clinics and heart valve teams are now widely prevalent.

Cardiac imaging correlates and predictors of stroke in patients with atrial fibrillation: a meta-analysis.

BACKGROUND: New nonclinical parameters are needed to improve the current stroke risk stratification schemes for patients with atrial fibrillation. This study aimed to summarize data on potential cardiac imaging correlates and predictors of stroke or systemic embolism in patients with atrial fibrillation. METHODS: MEDLINE, EMBASE, and Web of Science were searched to identify all published studies providing relevant data through 16 November 2022. Random effects meta-analysis method was used to pool estimates. RESULTS: We included 64 studies reporting data from a pooled population of 56 639 patients. Left atrial spontaneous echo-contrast [adjusted odds ratio (aOR) 3.32, 95% confidence interval (CI) 1.98-5.49], nonchicken wing left atrial appendage (LAA) morphology (aOR 2.15, 95% CI 1.11-4.18), left atrial enlargement (aOR 2.12, 95% CI 1.45-3.08), and higher LAA orifice diameter (aOR 1.56, 95% CI 1.18-2.05) were highly associated with stroke. Other parameters associated with stroke included higher left atrial sphericity (aOR 1.14, 95% CI 1.01-1.29), higher left atrial volume (aOR 1.03, 95% CI 1.01-1.04), higher left atrial volume index (aOR 1.014, 95% CI 1.004-1.023), lower left atrial reservoir strain [adjusted hazard ratio (aHR) 0.86, 95% CI 0.76-0.98], higher left ventricular mass index (aOR 1.010, 95% CI 1.005-1.015) and E / e' ratio (aOR 1.12, 95% CI 1.07-1.16). There was no association between LAA volume (aOR 1.37, 95% CI 0.85-2.21) and stroke. CONCLUSION: These cardiac imaging parameters identified as potential predictors of thromboembolism may improve the accuracy of stroke risk stratification schemes in patients with atrial fibrillation. Further studies should evaluate the performance of holistic risk scores including clinical factors, biomarkers, and cardiac imaging.

Multimodality Cardiac Imaging and the Imaging Workforce in the United States: Diversity, Disparities, and Future Directions.

Innovations in cardiac imaging have fundamentally advanced the understanding and treatment of cardiovascular disease. These advances in noninvasive cardiac imaging have also expanded the role of the cardiac imager and dramatically increased the demand for imagers who are cross-trained in multiple modalities. However, we hypothesize that there is significant variation in the availability of cardiac imaging expertise and a disparity in the adoption of advanced imaging technologies across the United States. To evaluate this, we have brought together the leaders of cardiovascular imaging societies, imaging trainees, as well as collaborated with national imaging accreditation commissions and imaging certification boards to assess the state of cardiac imaging and the diversity of the imaging workforce in the United States. Aggregate data confirm the presence of critical gaps, such as limited access to imaging and imaging expertise in rural communities, as well as disparities in the imaging workforce, notably among women and underrepresented minorities. Based on these results, we have proposed solutions to promote and maintain a robust and diverse community of cardiac imagers and improve equity and accessibility for cardiac imaging technologies.

Device innovation in cardiovascular medicine: a report from the European Society of Cardiology Cardiovascular Round Table.

Research performed in Europe has driven cardiovascular device innovation. This includes, but is not limited to, percutaneous coronary intervention, cardiac imaging, transcatheter heart valve implantation, and device therapy of cardiac arrhythmias and heart failure. An important part of future medical progress involves the evolution of medical technology and the ongoing development of artificial intelligence and machine learning. There is a need to foster an environment conducive to medical technology development and validation so that Europe can continue to play a major role in device innovation while providing high standards of safety. This paper summarizes viewpoints on the topic of device innovation in cardiovascular medicine at the European Society of Cardiology Cardiovascular Round Table, a strategic forum for high-level dialogue to discuss issues related to the future of cardiovascular health in Europe. Devices are developed and improved through an iterative process throughout their lifecycle. Early feasibility studies demonstrate proof of concept and help to optimize the design of a device. If successful, this should ideally be followed by randomized clinical trials comparing novel devices vs. accepted standards of care when available and the collection of post-market real-world evidence through registries. Unfortunately, standardized procedures for feasibility studies across various device categories have not yet been implemented in Europe. Cardiovascular imaging can be used to diagnose and characterize patients for interventions to improve procedural results and to monitor devices long term after implantation. Randomized clinical trials often use cardiac imaging-based inclusion criteria, while less frequently trials randomize patients to compare the diagnostic or prognostic value of different modalities. Applications using machine learning are increasingly important, but specific regulatory standards and pathways remain in development in both Europe and the USA. Standards are also needed for smart devices and digital technologies that support device-driven biomonitoring. Changes in device regulation introduced by the European Union aim to improve clinical evidence, transparency, and safety, but they may impact the speed of innovation, access, and availability. Device development programmes including dialogue on unmet needs and advice on study designs must be driven by a community of physicians, trialists, patients, regulators, payers, and industry to ensure that patients have access to innovative care.

Novel Techniques in Imaging Congenital Heart Disease: JACC Scientific Statement.

Recent years have witnessed exponential growth in cardiac imaging technologies, allowing better visualization of complex cardiac anatomy and improved assessment of physiology. These advances have become increasingly important as more complex surgical and catheter-based procedures are evolving to address the needs of a growing congenital heart disease population. This state-of-the-art review presents advances in echocardiography, cardiac magnetic resonance, cardiac computed tomography, invasive angiography, 3-dimensional modeling, and digital twin technology. The paper also highlights the integration of artificial intelligence with imaging technology. While some techniques are in their infancy and need further refinement, others have found their way into clinical workflow at well-resourced centers. Studies to evaluate the clinical value and cost-effectiveness of these techniques are needed. For techniques that enhance the value of care for congenital heart disease patients, resources will need to be allocated for education and training to promote widespread implementation.

Multimodality Imaging Approach Evaluation of the Congenital Pericardial Defect: a Contemporary Review.

PURPOSE OF REVIEW: This review presents the etiology, clinical manifestations, diagnostic approach, and treatment of congenital pericardial defects. It also highlights the critical role of echocardiography, cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR) in the diagnosis and management approach. RECENT FINDINGS: Congenital pericardial defects are rare. Although most cases are found incidentally, some cases could potentially be associated with serious outcomes including sudden cardiac death. The diagnosis is often challenging due to non-specific clinical manifestations and electrocardiogram findings. Echocardiography is the first-line imaging investigation for the evaluation of this condition. Advanced cardiac imaging modalities, including CCT and CMR, play important adjuvant roles in establishing the diagnosis and assists with prognostication.