Artificial intelligence for cardiac imaging is ready for widespread clinical use: Pro Con debate AI for cardiac imaging.

Artificial intelligence (AI) has made significant strides in cardiac imaging, offering advancements in image acquisition, risk prediction, and workflow automation. However, its readiness for widespread clinical adoption remains debated. This review explores both sides of the argument across key domains.

Usefulness of electron density imaging from dual-energy CT for detecting pulmonary thromboembolism in unenhanced CT.

Objective: This study aimed to evaluate the diagnostic value of electron density (ED) imaging in detecting pulmonary thromboembolism (PTE) on unenhanced dual-energy CT (UCT) and to analyze stage-dependent changes in ED values.

Materials And Methods: In the in vitro study, venous blood samples were incubated to form thrombi and scanned to assess attenuation and ED changes across different thrombus stages. The in vivo study retrospectively analyzed patients who underwent UCT within 1 day of CT pulmonary angiography, measuring HU and ED values of PTE.

Artificial Intelligence and Its Effect on Radiology Residency Education: Current Challenges, Opportunities, and Future Directions.

Artificial intelligence has become an impressive force manifesting itself in the radiology field, improving workflows, and influencing clinical decision making. With this increasing presence, a closer look at how residents can be properly exposed to this technology is needed. Within this article, we aim to discuss the three pillars central to a trainee's experience including education on AI, AI education tools, and clinical implementation of AI.

Is a score enough? Pitfalls and solutions for AI severity scores.

Severity scores, which often refer to the likelihood or probability of a pathology, are commonly provided by artificial intelligence (AI) tools in radiology. However, little attention has been given to the use of these AI scores, and there is a lack of transparency into how they are generated. In this comment, we draw on key principles from psychological science and statistics to elucidate six human factors limitations of AI scores that undermine their utility: (1) variability across AI systems; (2) variability within AI systems; (3) variability between radiologists; (4) variability within radiologists; (5) unknown distribution of AI scores; and (6) perceptual challenges.

Corrigendum to "Association of thoracic aortic calcium with incident cardiovascular disease and all-cause mortality across the spectrum of coronary artery calcium burden" [American Journal of Preventive Cardiology 21 (2025) 100916].

[This corrects the article DOI: 10.1016/j.ajpc.

Impact of Deep Learning-Based Image Conversion on Fully Automated Coronary Artery Calcium Scoring Using Thin-Slice, Sharp-Kernel, Non-Gated, Low-Dose Chest CT Scans: A Multi-Center Study.

Objective: To evaluate the impact of deep learning-based image conversion on the accuracy of automated coronary artery calcium quantification using thin-slice, sharp-kernel, non-gated, low-dose chest computed tomography (LDCT) images collected from multiple institutions.

Materials And Methods: A total of 225 pairs of LDCT and calcium scoring CT (CSCT) images scanned at 120 kVp and acquired from the same patient within a 6-month interval were retrospectively collected from four institutions. Image conversion was performed for LDCT images using proprietary software programs to simulate conventional CSCT.

From Acquisition to Prognosis: The Role of AI in Cardiac Magnetic Resonance Imaging Evaluation of Ischemic Cardiomyopathy.

Acute and chronic ischemic cardiomyopathy (ICM) still represents a leading cause of morbidity and mortality. Cardiac magnetic resonance (CMR) imaging plays a central role in the diagnosis and management of ICM, offering detailed visualization of cardiac structures and function. The evolving role of artificial intelligence (AI) in enhancing CMR exams, from acquisition to prognosis, is rapidly expanding in clinical practice, particularly in CMR of patients with ICM, emphasizing the integration of AI algorithms to optimize imaging workflows in standard protocols.

AI in Action: A Road Map From the Radiology AI Council for Effective Model Evaluation and Deployment.

As the integration of artificial intelligence (AI) into radiology workflows continues to evolve, establishing standardized processes for the evaluation and deployment of AI models is crucial to ensure success. This article outlines the creation of a Radiology AI Council at a large academic center and subsequent development of framework in the form of a rubric to formalize the evaluation of radiology AI models and onboard them into clinical workflows. The rubric aims to address the challenges faced during the deployment of AI models, such as real-world model performance, workflow implementation, resource allocation, return on investment, and impact to the broader health system.

Systematic review on the impact of deep learning-driven worklist triage on radiology workflow and clinical outcomes.

Objectives: To perform a systematic review on the impact of deep learning (DL)-based triage for reducing diagnostic delays and improving patient outcomes in peer-reviewed and pre-print publications.

Materials And Methods: A search was conducted of primary research studies focused on DL-based worklist optimization for diagnostic imaging triage published on multiple databases from January 2018 until July 2024. Extracted data included study design, dataset characteristics, workflow metrics including report turnaround time and time-to-treatment, and patient outcome differences.

Automated, Standardized, Quantitative Analysis of Cardiovascular Borders on Chest X-Rays Using Deep Learning.

Background: The analysis of cardiovascular borders (CVBs) in chest x-rays (CXRs) traditionally relied on subjective assessment and does not have established normal ranges.

Objectives: The authors aimed to develop a deep learning-based method for quantifying CVBs on CXRs and to explore its clinical utility.

Methods: This study used a prevalidated deep learning to analyze CVBs.

VPAL: A novel method to reduce reconstruction time for 5D free-running imaging.

Purpose: Ferumoxytal-enhanced 5D free-running whole heart CMR provides image quality comparable to CTA, but requires hours-long reconstruction time, preventing clinical usage. This study developed a variable projection augmented Lagrangian (VPAL) method for 5D motion-resolved image reconstruction and compared it with alternating direction method of multipliers (ADMM) in five numerical simulations and 15 in-vivo pediatric data set.

Approach: Relative error of the reconstructed images against the ground-truth images was assessed in numerical simulations.

AI-Quantitative CT Coronary Plaque Features Associate With a Higher Relative Risk in Women: CONFIRM2 Registry.

Background: Coronary plaque features are imaging biomarkers of cardiovascular risk, but less is known about sex-specific patterns in their prognostic value. This study aimed to define sex differences in the coronary atherosclerotic phenotypes assessed by artificial intelligence-based quantitative computed tomography (AI-QCT) and the associated risk of major adverse cardiovascular events (MACEs).

Methods: Global multicenter registry including symptomatic patients with suspicion of coronary artery disease referred for coronary computed tomography angiography.

Beam hardening of K-edge contrast agents: a phantom study comparing clinical energy-integrating detector and photon-counting detector CT systems.

Background: Beam hardening (BH) artifacts negatively influence computed tomography (CT) measurements, especially when due to dense materials or materials with high effective atomic numbers. Photon-counting detectors (PCD) are more susceptible to BH due to equal weighting of photons regardless of their energies. The problem is further confounded by the use of contrast agents (CAs) with K-edge in the diagnostic CT energy range.

3D cinematic reconstructions of cardiovascular CT presented in augmented reality: subjective assessment of clinical feasibility and potential use cases.

Augmented reality (AR) is a new technique enabling interaction with three-dimensional (3D) holograms of cinematic rendering (CR) reconstructions. Research in this field is in its very early steps, and data is scarce. We evaluated image quality, usability, and potential applications of AR in cardiovascular image datasets.

Artificial Intelligence Applications in Cardiac CT Imaging for Ischemic Disease Assessment.

Artificial intelligence (AI) has transformed medical imaging by detecting insights and patterns often imperceptible to the human eye, enhancing diagnostic accuracy and efficiency. In cardiovascular imaging, numerous AI models have been developed for cardiac computed tomography (CCT), a primary tool for assessing coronary artery disease (CAD). CCT provides comprehensive, non-invasive assessment, including plaque burden, stenosis severity, and functional assessments such as CT-derived fractional flow reserve (FFRct).

Association of thoracic aortic calcium with incident cardiovascular disease and all-cause mortality across the spectrum of coronary artery calcium burden.

Background: Calcification of the ascending and/or descending thoracic aorta is easily measured via non-contrast cardiac computed tomography (CT), commonly performed for quantification of coronary artery calcium (CAC). We assessed whether thoracic aortic calcium (TAC) further improves long-term cardiovascular disease (CVD) risk stratification beyond CAC alone.

Methods: Cardiac CT was performed among 6,783 asymptomatic Multi-Ethnic Study of Atherosclerosis participants at baseline.

Use of AI in Cardiac CT and MRI: A Scientific Statement from the ESCR, EuSoMII, NASCI, SCCT, SCMR, SIIM, and RSNA.

Artificial intelligence (AI) offers promising solutions for many steps of the cardiac imaging workflow, from patient and test selection through image acquisition, reconstruction, and interpretation, extending to prognostication and reporting. Despite the development of many cardiac imaging AI algorithms, AI tools are at various stages of development and face challenges for clinical implementation. This scientific statement, endorsed by several societies in the field, provides an overview of the current landscape and challenges of AI applications in cardiac CT and MRI.

Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases.

Background: This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT).

Methods: Twenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions.

Coronary Artery Calcium for Risk Stratification Among Persons With Very High HDL Cholesterol.

Background: Compared to normal high-density lipoprotein (HDL) cholesterol values, very high HDL cholesterol is associated with a higher incidence of mortality and atherosclerotic cardiovascular disease (ASCVD). As such, clinical risk stratification among persons with very high HDL cholesterol is challenging.

Objectives: Among persons with very high HDL cholesterol, the purpose was to determine the prevalence of coronary artery calcium (CAC) and compare the association between traditional risk factors vs CAC for all-cause mortality and ASCVD.

Confidence-Aware Severity Assessment of Lung Disease from Chest X-Rays Using Deep Neural Network on a Multi-Reader Dataset.

In this study, we present a method based on Monte Carlo Dropout (MCD) as Bayesian neural network (BNN) approximation for confidence-aware severity classification of lung diseases in COVID-19 patients using chest X-rays (CXRs). Trained and tested on 1208 CXRs from Hospital 1 in the USA, the model categorizes severity into four levels (i.e.