Roadmap on the use of artificial intelligence for imaging of vulnerable atherosclerotic plaque in coronary arteries

A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images

BACKGROUND

The adrenal glands are small retroperitoneal organs, few reference standards exist for adrenal CT measurements in clinical practice. This study aims to develop a deep learning (DL) model for automated adrenal gland segmentation on non-contrast CT images, and to conduct a preliminary large-scale study on age-related volume changes in normal adrenal glands using the model output values.

METHODS

The model was trained and evaluated on a development dataset of annotated non-contrast CT scans of bilateral adrenal glands, utilizing nnU-Net for segmentation task. The ground truth was manually established by two experienced radiologists, and the model performance was assessed using the Dice similarity coefficient (DSC). Additionally, five radiologists provided annotations on a subset of 20 randomly selected cases to measure inter-observer variability. Following validation, the model was applied to a large-scale normal adrenal glands dataset to segment adrenal glands.

RESULTS

The DL model development dataset contained 1301 CT examinations. In the test set, the median DSC scores for the segmentation model of left and right adrenal glands were 0.899 and 0.904 respectively, and in the independent test set were 0.900 and 0.896. Inter-observer DSC for radiologist manual segmentation did not differ from automated machine segmentation (P = 0.541). The large-scale normal adrenal glands dataset contained 2000 CT examinations, the graph shows that adrenal gland volume increases first and then decreases with age.

CONCLUSION

The developed DL model demonstrates accurate adrenal gland segmentation, and enables a comprehensive study of age-related adrenal gland volume variations.

Development of a surrogate model for predicting atherosclerotic plaque progression based on agent based modeling data

BackgroundAtherosclerosis of the coronary arteries is a chronic, progressive condition characterized by the buildup of plaque within the arterial walls. Coronary artery disease (CAD), more specifically coronary atherosclerosis (CATS), is one of the leading causes of death worldwide. Computational modeling frameworks have been used for simulation of atherosclerotic plaque progression and with the advancement of agent-based modeling (ABM) the simulation results became more accurate. However, there is a need for optimization of resources for predictive modeling, hence surrogate models are being built to substitute lengthy computational models without compromising the results.ObjectiveThis study explores the development of a surrogate model for atherosclerotic plaque progression using ABM simulation data.MethodThe dataset used for this study contains samples from latin-hypercube sampling based generated simulation parameters used in conjunction with 15 patient-specific geometries and corresponding plaque progression data. The developed surrogate model is based on deep learning using artificial neural networks (ANN).ResultsThe surrogate model achieved an accuracy of 95.4% in benchmarking with the ABM model it was built upon which indicates the robustness of the framework.ConclusionAdoption of surrogate models with high accuracy in practice opens an avenue for utilization of high-fidelity decision support systems for predicting atherosclerotic plaque progression in real-time.

Inflammation in coronary atherosclerosis: diagnosis and treatment

Coronary atherosclerosis is a chronic condition characterised by the development of an atherosclerotic plaque in the inner layer of the coronary artery, mainly associated with cholesterol accumulation and favoured by endothelial dysfunction related to other cardiovascular risk factors, such as smoking, diabetes and hypertension. A key actor in this process is the systemic inflammatory response, which can make plaques either grow slowly over the course of years (like a 'mountain'), obstructing coronary flow, and causing stable coronary artery disease, or make them explode (like a 'volcano') with subsequent abrupt thrombosis causing an acute coronary syndrome. This central role of inflammation in coronary atherosclerosis has led to its consideration as a modifiable cardiovascular risk factor and a therapeutic target. Classic anti-inflammatory drugs have been tested in clinical trials with some encouraging results, and new drugs specifically designed to tackle inflammation are currently being under investigation in ongoing trials. The objectives of this review are to (1) summarise the role of inflammatory biomarkers and imaging techniques to detect inflammation at each stage of plaque progression, and (2) explore currently available and upcoming anti-inflammatory therapies.

Progress in the Application of Artificial Intelligence in Ultrasound-Assisted Medical Diagnosis

The integration of artificial intelligence (AI) into ultrasound medicine has revolutionized medical imaging, enhancing diagnostic accuracy and clinical workflows. This review focuses on the applications, challenges, and future directions of AI technologies, particularly machine learning (ML) and its subset, deep learning (DL), in ultrasound diagnostics. By leveraging advanced algorithms such as convolutional neural networks (CNNs), AI has significantly improved image acquisition, quality assessment, and objective disease diagnosis. AI-driven solutions now facilitate automated image analysis, intelligent diagnostic assistance, and medical education, enabling precise lesion detection across various organs while reducing physician workload. AI's error detection capabilities further enhance diagnostic accuracy. Looking ahead, the integration of AI with ultrasound is expected to deepen, promoting trends in standardization, personalized treatment, and intelligent healthcare, particularly in underserved areas. Despite its potential, comprehensive assessments of AI's diagnostic accuracy and ethical implications remain limited, necessitating rigorous evaluations to ensure effectiveness in clinical practice. This review provides a systematic evaluation of AI technologies in ultrasound medicine, highlighting their transformative potential to improve global healthcare outcomes.

Artificial intelligence for the analysis of intracoronary optical coherence tomography images: a systematic review

Intracoronary optical coherence tomography (OCT) is a valuable tool for, among others, periprocedural guidance of percutaneous coronary revascularization and the assessment of stent failure. However, manual OCT image interpretation is challenging and time-consuming, which limits widespread clinical adoption. Automated analysis of OCT frames using artificial intelligence (AI) offers a potential solution. For example, AI can be employed for automated OCT image interpretation, plaque quantification, and clinical event prediction. Many AI models for these purposes have been proposed in recent years. However, these models have not been systematically evaluated in terms of model characteristics, performances, and bias. We performed a systematic review of AI models developed for OCT analysis to evaluate the trends and performances, including a systematic evaluation of potential sources of bias in model development and evaluation.

Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study

OBJECTIVES

The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagnostic performance of a recently updated deep learning model (CorEx-2.0) for quantifying coronary stenosis, compared separately with two expert CCTA readers as references.

METHODS

This single-center retrospective study included 50 patients that underwent CCTA to rule out obstructive coronary artery disease between 2017-2022. Two expert CCTA readers and CorEx-2.0 independently assessed all 150 vessels using Coronary Artery Disease-Reporting and Data System (CAD-RADS). Inter-reader agreement analysis and diagnostic performance of CorEx-2.0, compared with each expert reader as references, were evaluated using percent agreement, Cohen's kappa for the binary CAD-RADS classification (CAD-RADS 0-3 versus 4-5) at patient level, and linearly weighted kappa for the 6-group CAD-RADS classification at vessel level.

RESULTS

Overall, 50 patients and 150 vessels were evaluated. Inter-reader agreement using the binary classification at patient level was 91.8% (45/49) with a Cohen's kappa of 0.80. For the 6-group classification at vessel level, inter-reader agreement was 67.6% (100/148) with a linearly weighted kappa of 0.77. CorEx-2.0 showed 100% sensitivity for detecting CAD-RADS ≥ 4 and kappa values of 0.86 versus both readers using the binary classification at patient level. For the 6-group classification at vessel level, CorEx-2.0 demonstrated weighted kappa values of 0.71 versus reader 1 and 0.73 versus reader 2.

CONCLUSION

CorEx-2.0 identified all patients with severe stenosis (CAD-RADS ≥ 4) compared with expert readers and approached expert reader performance at vessel level (weighted kappa > 0.70).

KEY POINTS

Question Can deep learning models improve objectivity in coronary stenosis grading and reporting as coronary CT angiography (CTA) workloads rise? Findings The deep learning model (CorEx-2.0) identified all patients with severe stenoses when compared with expert readers and approached expert reader performance at vessel level. Clinical relevance CorEx-2.0 is a reliable tool for identifying patients with severe stenoses (≥ 70%), underscoring the potential of using this deep learning model to prioritize coronary CTA reading by flagging patients at risk of severe obstructive coronary artery disease.

Predicting vulnerable coronary arteries: A combined radiomics-biomechanics approach

BACKGROUND AND OBJECTIVE

Nowadays, vulnerable coronary plaque detection from coronary computed tomography angiography (CCTA) is suboptimal, although being crucial for preventing major adverse cardiac events. Moreover, despite the suggestion of various vulnerability biomarkers, encompassing image and biomechanical factors, accurate patient stratification remains elusive, and a comprehensive approach integrating multiple markers is lacking. To this aim, this study introduces an innovative approach for assessing vulnerable coronary arteries and patients by integrating radiomics and biomechanical markers through machine learning methods.

METHODS

The study included 40 patients (7 high-risk and 33 low-risk) who underwent both CCTA and coronary optical coherence tomography (OCT). The dataset comprised 49 arteries (with 167 plaques), 7 of which (with 28 plaques) identified as vulnerable by OCT. Following image preprocessing and segmentation, CCTA-based radiomic features were extracted and a finite element analysis was performed to compute the biomechanical features. A novel machine learning pipeline was implemented to stratify coronary arteries and patients. For each stratification task, three independent predictive models were developed: a radiomic, a biomechanical and a combined radiomic-biomechanical model. Both k-nearest neighbors (KNN) and decision tree (DT) classifiers were considered.

RESULTS

The best radiomic model (KNN) detected all 7 vulnerable arteries and patients and was associated with a balanced accuracy of 0.86 (sensitivity=1, specificity=0.71) for the artery model and of 0.83 (sensitivity=1, specificity=0.67) for the patient model. The best biomechanical model (DT) detected 6 over 7 vulnerable arteries and patients and remarkably increased the specificity, resulting in a balanced accuracy of 0.89 (sensitivity=0.86, specificity=0.93) for the artery model and of 0.88 (sensitivity=0.86, specificity=0.91) for the patient model. Notably, the combined approach optimized the performance, with an increase in the balance accuracy up to 0.94 for the artery model and up to 0.92 for the patient model, being associated with sensitivity=1 and high specificity (0.88 and 0.85 for artery and patient models, respectively).

CONCLUSION

This investigation highlights the promise of radio-mechanical coronary artery phenotyping for patient stratification. If confirmed from larger studies, our approach enables a more personalized management of the disease, with the early identification of high-risk individuals and the reduction of unnecessary interventions for low-risk individuals.

Correlation between hemodynamics assessed by FAI combined with CT-FFR and plaque characteristics in coronary artery stenosis

BACKGROUND

While both CT-FFR and FAI are found to be associated with the development of CAD, their relationship with hemodynamics and plaque characteristics remains unclear. The present study aims to investigate the relationship between hemodynamics assessed by FAI combined with CT-FFR and plaque characteristics in functionally significant coronary artery stenosis.

METHODS

This retrospective study included 130 patients with suspected coronary heart disease, who were admitted to the Department of Cardiology of our hospital and underwent coronary computed tomography angiography (CCTA) from January 2022 to December 2023. Clinical baseline data and relevant auxiliary examination results were collected, and CCTA, FAI, and CT-FFR data were analyzed to investigate the relationship between these imaging parameters and both the hemodynamics and plaque characteristics of coronary artery lesions.

RESULTS

From 130 patients, a total of 207 diseased vessels were analyzed and classified based on CAD-RADS grading: 128 vessels exhibited stenosis of less than 50%, and 79 exhibited stenosis exceeding 50%. Patients with more than one lesion of > 50% stenosis were classified into the myocardial ischemia group (44 cases), and the rest were categorized as the non-myocardial ischemia group (86 cases). Compared to the non-myocardial ischemia group, patients in the myocardial ischemia group were significantly older (p < 0.001). No significant difference was found between the two groups in sex, cardiovascular risk factors, or the indicator of stenotic vessel distribution. The minimum CT-FFR in vessels with < 50% stenosis was higher than in vessels with > 50% stenosis, ΔCT-FFR was lower in vessels with < 50% stenosis than in vessels with > 50% stenosis, and the median CT-FFR was significantly lower in vessels with > 50% stenosis than in vessels with < 50% stenosis (p < 0.001). Additionally, FAI-LAD, FAI-LCX, FAI-RCA, and FAI-Mean were found to be significantly higher in vessels with > 50% stenosis compared to vessels with < 50% stenosis (p < 0.05). A negative correlation was observed between the minimum CT-FFR among three main coronary arteries (LAD, LCX, RCA) and CAD-RADS classification, while both ΔCT-FFR and FAI were positively correlated with CAD-RADS classification (p < 0.05). Non-calcified plaques were more prevalent in the vessels with > 50% stenosis, primarily located in the LAD, while calcified plaques were predominantly observed in vessels with < 50% stenosis (p < 0.001). In addition, in vessels with > 50% stenosis, plaques were longer, the degree of luminal stenosis was greater, and both the total volume and burden of plaques were significantly greater than in vessels with < 50% stenosis (p < 0.001). Lastly, the FAIlesion value in the vessels with > 50% stenosis was higher than in vessels with < 50% stenosis (p < 0.001).

CONCLUSION

FAI is associated with coronary artery stenosis and myocardial ischemia, and may serve as a novel indicator for identifying myocardial ischemia. Both FAI and CT-FFR demonstrated strong predictive abilities in significant coronary stenosis.

PlaqueViT: a vision transformer model for fully automatic vessel and plaque segmentation in coronary computed tomography angiography

OBJECTIVES

To develop and evaluate a deep learning model for segmentation of the coronary artery vessels and coronary plaques in coronary computed tomography angiography (CCTA).

MATERIALS AND METHODS

CCTA image data from the Swedish CardioPulmonary BioImage Study (SCAPIS) was used for model development (n = 463 subjects) and testing (n = 123) and for an interobserver study (n = 65). A dataset from Linköping University Hospital (n = 28) was used for external validation. The model's ability to detect coronary artery disease (CAD) was tested in a separate SCAPIS dataset (n = 684). A deep ensemble (k = 6) of a customized 3D vision transformer model was used for voxelwise classification. The Dice coefficient, the average surface distance, Pearson's correlation coefficient, analysis of segmented volumes by intraclass correlation coefficient (ICC), and agreement (sensitivity and specificity) were used to analyze model performance.

RESULTS

PlaqueViT segmented coronary plaques with a Dice coefficient = 0.55, an average surface distance = 0.98 mm and ICC = 0.93 versus an expert reader. In the interobserver study, PlaqueViT performed as well as the expert reader (Dice coefficient = 0.51 and 0.50, average surface distance = 1.31 and 1.15 mm, ICC = 0.97 and 0.98, respectively). PlaqueViT achieved 88% agreement (sensitivity 97%, specificity 76%) in detecting any coronary plaque in the test dataset (n = 123) and 89% agreement (sensitivity 95%, specificity 83%) in the CAD detection dataset (n = 684).

CONCLUSION

We developed a deep learning model for fully automatic plaque detection and segmentation that identifies and delineates coronary plaques and the arterial lumen with similar performance as an experienced reader.

KEY POINTS

Question A tool for fully automatic and voxelwise segmentation of coronary plaques in coronary CTA (CCTA) is important for both clinical and research usage of the CCTA examination. Findings Segmentation of coronary artery plaques by PlaqueViT was comparable to an expert reader's performance. Clinical relevance This novel, fully automatic deep learning model for voxelwise segmentation of coronary plaques in CCTA is highly relevant for large population studies such as the Swedish CardioPulmonary BioImage Study.

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. Each section is organized into questions and statements that address key steps of the cardiac imaging workflow, including ethical, legal, and environmental sustainability considerations. A technology readiness level range of 1 to 9 summarizes the maturity level of AI tools and reflects the progression from preliminary research to clinical implementation. This document aims to bridge the gap between burgeoning research developments and limited clinical applications of AI tools in cardiac CT and MRI.

The Role of Coronary Computed Tomography Angiography in the Diagnosis, Risk Stratification, and Management of Patients with Diabetes and Chest Pain

Coronary artery disease (CAD) affects over 200 million individuals globally, accounting for approximately 9 million deaths annually. Patients living with diabetes mellitus exhibit an up to fourfold increased risk of developing CAD compared to individuals without diabetes. Furthermore, CAD is responsible for 40 to 80 percent of the observed mortality rates among patients with type 2 diabetes. Patients with diabetes typically present with non-specific clinical complaints in the setting of myocardial ischemia, and as such, it is critical to select appropriate diagnostic tests to identify those at risk for major adverse cardiac events (MACEs) and for determining optimal management strategies. Studies indicate that patients with diabetes often exhibit more advanced atherosclerosis, a higher calcified plaque burden, and smaller epicardial vessels. The diagnostic performance of coronary computed tomographic angiography (CCTA) in identifying significant stenosis is well-established, and as such, CCTA has been incorporated into current clinical guidelines. However, the predictive accuracy of obstructive CAD in patients with diabetes has been less extensively characterized. CCTA provides detailed insights into coronary anatomy, plaque burden, epicardial vessel stenosis, high-risk plaque features, and other features associated with a higher incidence of MACEs. Recent evidence supports the efficacy of CCTA in diagnosing CAD and improving patient outcomes, leading to its recommendation as a primary diagnostic tool for stable angina and risk stratification. However, its specific benefits in patients with diabetes require further elucidation. This review examines several key aspects of the utility of CCTA in patients with diabetes: (i) the diagnostic accuracy of CCTA in detecting obstructive CAD, (ii) the effect of CCTA as a first-line test for individualized risk stratification for cardiovascular outcomes, (iii) its role in guiding therapeutic management, and (iv) future perspectives in risk stratification and the role of artificial intelligence.

Artificial Intelligence and Radiologist Burnout

IMPORTANCE

Understanding the association of artificial intelligence (AI) with physician burnout is crucial for fostering a collaborative interactive environment between physicians and AI.

OBJECTIVE

To estimate the association between AI use in radiology and radiologist burnout.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study conducted a questionnaire survey between May and October 2023, using the national quality control system of radiology in China. Participants included radiologists from 1143 hospitals. Radiologists reporting regular or consistent AI use were categorized as the AI group. Statistical analysis was performed from October 2023 to May 2024.

EXPOSURE

AI use in radiology practice.

MAIN OUTCOMES AND MEASURES

Burnout was defined by emotional exhaustion (EE) or depersonalization according to the Maslach Burnout Inventory. Workload was assessed based on working hours, number of image interpretations, hospital level, device type, and role in the workflow. AI acceptance was determined via latent class analysis considering AI-related knowledge, attitude, confidence, and intention. Propensity score-based mixed-effect generalized linear logistic regression was used to estimate the associations between AI use and burnout and its components. Interactions of AI use, workload, and AI acceptance were assessed on additive and multiplicative scales.

RESULTS

Among 6726 radiologists included in this study, 2376 (35.3%) were female and 4350 (64.7%) were male; the median (IQR) age was 41 (34-48) years; 3017 were in the AI group (1134 [37.6%] female; median [IQR] age, 40 [33-47] years) and 3709 in the non-AI group (1242 [33.5%] female; median [IQR] age, 42 [34-49] years). The weighted prevalence of burnout was significantly higher in the AI group compared with the non-AI group (40.9% vs 38.6%; P < .001). After adjusting for covariates, AI use was significantly associated with increased odds of burnout (odds ratio [OR], 1.20; 95% CI, 1.10-1.30), primarily driven by its association with EE (OR, 1.21; 95% CI, 1.10-1.34). A dose-response association was observed between the frequency of AI use and burnout (P for trend < .001). The associations were more pronounced among radiologists with high workload and lower AI acceptance. A significant negative interaction was noted between high AI acceptance and AI use.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study of radiologist burnout, frequent AI use was associated with an increased risk of radiologist burnout, particularly among those with high workload or lower AI acceptance. Further longitudinal studies are needed to provide more evidence.

The role of artificial intelligence in coronary CT angiography

Coronary CT angiography (CCTA) offers an efficient and reliable tool for the non-invasive assessment of suspected coronary artery disease through the analysis of coronary artery plaque and stenosis. However, the detailed manual analysis of CCTA is a burdensome task requiring highly skilled experts. Recent advances in artificial intelligence (AI) have made significant progress toward a more comprehensive automated analysis of CCTA images, offering potential improvements in terms of speed, performance and scalability. This work offers an overview of the recent developments of AI in CCTA. We cover methodological advances for coronary artery tree and whole heart analysis, and provide an overview of AI techniques that have shown to be valuable for the analysis of cardiac anatomy and pathology in CCTA. Finally, we provide a general discussion regarding current challenges and limitations, and discuss prospects for future research.

Diagnostic Methods of Atherosclerotic Plaque and the Assessment of Its Prognostic Significance-A Narrative Review

Cardiovascular diseases (CVD) are a leading cause of death. The most notable cause of CVD is an atherosclerotic plaque. The aim of this review is to provide an overview of different diagnostic methods for atherosclerotic plaque relevant to the assessment of cardiovascular risk. The methods can be divided into invasive and non-invasive. This review focuses on non-invasive with attention paid to ultrasonography, contrast-enhanced ultrasonography, intravascular ultrasonography, and assessment of intima-media complex, coronary computed tomography angiography, and magnetic resonance. In the review, we discuss a number of Artificial Intelligence technologies that support plaque imaging.

Impact of hypertension on coronary artery plaques and FFR-CT in type 2 diabetes mellitus patients: evaluation utilizing artificial intelligence processed coronary computed tomography angiography

Objective

This study utilized artificial intelligence (AI) to quantify coronary computed tomography angiography (CCTA) images, aiming to compare plaque characteristics and CT-derived fractional flow reserve (FFR-CT) in type 2 diabetes mellitus (T2DM) patients with or without hypertension (HTN).

Methods

A retrospective analysis was conducted on 1,151 patients with suspected coronary artery disease who underwent CCTA at a single center. Patients were grouped into T2DM (n = 133), HTN (n = 442), T2DM (HTN+) (n = 256), and control (n = 320). AI assessed various CCTA parameters, including plaque components, high-risk plaques (HRPs), FFR-CT, severity of coronary stenosis using Coronary Artery Disease Reporting and Data System 2.0 (CAD-RADS 2.0), segment involvement score (SIS), and segment stenosis score (SSS). Statistical analysis compared these parameters among groups.

Results

The T2DM (HTN+) group had the highest plaque volume and length, SIS, SSS, and CAD-RADS 2.0 classification. In the T2DM group, 54.0% of the plaque volume was noncalcified and 46.0% was calcified, while in the HTN group, these values were 24.0 and 76.0%, respectively. The T2DM (HTN+) group had more calcified plaques (35.7% noncalcified, 64.3% calcified) than the T2DM group. The average necrotic core volume was 4.25 mm3 in the T2DM group and 5.23 mm3 in the T2DM (HTN+) group, with no significant difference (p > 0.05). HRPs were more prevalent in both T2DM and T2DM (HTN+) compared to HTN and control groups (p < 0.05). The T2DM (HTN+) group had a higher likelihood (26.1%) of FFR-CT ≤0.75 compared to the T2DM group (13.8%). FFR-CT ≤0.75 correlated with CAD-RADS 2.0 (OR = 7.986, 95% CI = 5.466-11.667, cutoff = 3, p < 0.001) and noncalcified plaque volume (OR = 1.006, 95% CI = 1.003-1.009, cutoff = 29.65 mm3, p < 0.001). HRPs were associated with HbA1c levels (OR = 1.631, 95% CI = 1.387-1.918).

Conclusion

AI analysis of CCTA identifies patterns in quantitative plaque characteristics and FFR-CT values. Comorbid HTN exacerbates partially calcified plaques, leading to more severe coronary artery stenosis in patients with T2DM. T2DM is associated with partially noncalcified plaques, whereas HTN is linked to partially calcified plaques.

[Artificial intelligence in cardiovascular radiology : Image acquisition, image reconstruction and workflow optimization]

BACKGROUND

Artificial intelligence (AI) has the potential to fundamentally change radiology workflow.

OBJECTIVES

This review article provides an overview of AI applications in cardiovascular radiology with a focus on image acquisition, image reconstruction, and workflow optimization.

MATERIALS AND METHODS

First, established applications of AI are presented for cardiovascular computed tomography (CT) and magnetic resonance imaging (MRI). Building on this, we describe the range of applications that are currently being developed and evaluated. The practical benefits, opportunities, and potential risks of artificial intelligence in cardiovascular imaging are critically discussed. The presentation is based on the relevant specialist literature and our own clinical and scientific experience.

RESULTS

AI-based techniques for image reconstruction are already commercially available and enable dose reduction in cardiovascular CT and accelerated image acquisition in cardiac MRI. Postprocessing of cardiovascular CT and MRI examinations can already be considerably simplified using established AI-based segmentation algorithms. In contrast, the practical benefits of many AI applications aimed at the diagnosis of cardiovascular diseases are less evident. Potential risks such as automation bias and considerations regarding cost efficiency should also be taken into account.

CONCLUSIONS

In a market characterized by great expectations and rapid technical development, it is important to realistically assess the practical benefits of AI applications for your own hospital or practice.

Uncovering atherosclerotic cardiovascular disease by PET imaging

Assessing atherosclerosis severity is essential for precise patient stratification. Specifically, there is a need to identify patients with residual inflammation because these patients remain at high risk of cardiovascular events despite optimal management of cardiovascular risk factors. Molecular imaging techniques, such as PET, can have an essential role in this context. PET imaging can indicate tissue-based disease status, detect early molecular changes and provide whole-body information. Advances in molecular biology and bioinformatics continue to help to decipher the complex pathogenesis of atherosclerosis and inform the development of imaging tracers. Concomitant advances in tracer synthesis methods and PET imaging technology provide future possibilities for atherosclerosis imaging. In this Review, we summarize the latest developments in PET imaging techniques and technologies for assessment of atherosclerotic cardiovascular disease and discuss the relationship between imaging readouts and transcriptomics-based plaque phenotyping.

Knowledge, Attitude and Practice of Radiologists Regarding Artificial Intelligence in Medical Imaging

Purpose

This study aimed to investigate the knowledge, attitudes, and practice (KAP) of radiologists regarding artificial intelligence (AI) in medical imaging in the southeast of China.

Methods

This cross-sectional study was conducted among radiologists in the Jiangsu, Zhejiang, and Fujian regions from October to December 2022. A self-administered questionnaire was used to collect demographic data and assess the KAP of participants towards AI in medical imaging. A structural equation model (SEM) was used to analyze the relationships between KAP.

Results

The study included 452 valid questionnaires. The mean knowledge score was 9.01±4.87, the attitude score was 48.96±4.90, and 75.22% of participants actively engaged in AI-related practices. Having a master's degree or above (OR=1.877, P=0.024), 5-10 years of radiology experience (OR=3.481, P=0.010), AI diagnosis-related training (OR=2.915, P<0.001), and engaging in AI diagnosis-related research (OR=3.178, P<0.001) were associated with sufficient knowledge. Participants with a junior college degree (OR=2.139, P=0.028), 5-10 years of radiology experience (OR=2.462, P=0.047), and AI diagnosis-related training (OR=2.264, P<0.001) were associated with a positive attitude. Higher knowledge scores (OR=5.240, P<0.001), an associate senior professional title (OR=4.267, P=0.026), 5-10 years of radiology experience (OR=0.344, P=0.044), utilizing AI diagnosis (OR=3.643, P=0.001), and engaging in AI diagnosis-related research (OR=6.382, P<0.001) were associated with proactive practice. The SEM showed that knowledge had a direct effect on attitude (β=0.481, P<0.001) and practice (β=0.412, P<0.001), and attitude had a direct effect on practice (β=0.135, P<0.001).

Conclusion

Radiologists in southeastern China hold a favorable outlook on AI-assisted medical imaging, showing solid understanding and enthusiasm for its adoption, despite half lacking relevant training. There is a need for more AI diagnosis-related training, an efficient standardized AI database for medical imaging, and active promotion of AI-assisted imaging in clinical practice. Further research with larger sample sizes and more regions is necessary.

Visualization of Vulnerable Coronary Plaque and Prevention of Plaque Rupture

In daily clinical practice, assessing anatomical findings and the presence or absence of ischemia is pivotal for determining the need for percutaneous coronary intervention. However, concurrently, comprehending vulnerability can greatly assist in predicting future cardiovascular events and formulating preventive strategies for individual patients. This review aims to describe the vulnerability of coronary artery plaques, primarily focusing on vulnerable plaques through pathological, morphological, and physiological viewpoints. Our review emphasizes the usefulness of coronary imaging modalities for the diagnosis of vulnerable plaques and the assessment of their rupture risk, as well as the possibility of percutaneous coronary intervention as a management strategy for plaque stabilization. Our findings show that there have been sporadic accounts of the potential of preventing cardiovascular events through early invasive treatments in patients with moderate or greater ischemia and utilizing new-generation stents to seal lipid core plaques. Thus, it is anticipated that direct intervention targeting coronary plaques, coupled with strict low-density lipoprotein-cholesterol lowering therapy, can play a vital role in suppressing future cardiovascular events and archiving zero perioperative myocardial infarction.

Computed tomographic angiography measures of coronary plaque in clinical trials: opportunities and considerations to accelerate drug translation

Atherosclerotic coronary artery disease (CAD) is the causal pathological process driving most major adverse cardiovascular events (MACE) worldwide. The complex development of atherosclerosis manifests as intimal plaque which occurs in the presence or absence of traditional risk factors. There are numerous effective medications for modifying CAD but new pharmacologic therapies require increasingly large and expensive cardiovascular outcome trials to assess their potential impact on MACE and to obtain regulatory approval. For many disease areas, nearly a half of drugs are approved by the U.S. Food & Drug Administration based on beneficial effects on surrogate endpoints. For cardiovascular disease, only low-density lipoprotein cholesterol and blood pressure are approved as surrogates for cardiovascular disease. Valid surrogates of CAD are urgently needed to facilitate robust evaluation of novel, beneficial treatments and inspire investment. Fortunately, advances in non-invasive imaging offer new opportunity for accelerating CAD drug development. Coronary computed tomography angiography (CCTA) is the most advanced candidate, with the ability to measure accurately and reproducibly characterize the underlying causal disease itself. Indeed, favourable changes in plaque burden have been shown to be associated with improved outcomes, and CCTA may have a unique role as an effective surrogate endpoint for therapies that are designed to improve CAD outcomes. CCTA also has the potential to de-risk clinical endpoint-based trials both financially and by enrichment of participants at higher likelihood of MACE. Furthermore, total non-calcified, and high-risk plaque volume, and their change over time, provide a causally linked measure of coronary artery disease which is inextricably linked to MACE, and represents a robust surrogate imaging biomarker with potential to be endorsed by regulatory authorities. Global consensus on specific imaging endpoints and protocols for optimal clinical trial design is essential as we work towards a rigorous, sustainable and staged pathway for new CAD therapies.

Relationship between the circulating N-terminal pro B-type natriuretic peptide and the risk of carotid artery plaque in different glucose metabolic states in patients with coronary heart disease: a CSCD-TCM plus study in China

OBJECTIVE

Circulating N-terminal pro B-type natriuretic peptide (NT-proBNP) is a marker for heart failure in patients with coronary heart disease (CHD) and associated with glycemic abnormalities. Studies on the association and diagnostic value of NT-proBNP in carotid plaques (CAP) in patients with CHD are limited.

METHODS

The relationships between NT-proBNP and the risk of CAP in different glucose metabolic states, sexes, and age categories were also examined using 5,093 patients diagnosed with CHD. The NT-proBNP tertiles were used to divide patients into three groups in which the NT-proBNP levels, blood glucose levels, the occurrence of CAP, and the number and nature of CAP were measured using normoglycemic (NG), prediabetes (Pre-DM), and diabetes mellitus (DM) glucose metabolic statuses. Logistic regression analyses were used to compare the relationship between NT-proBNP and the risk of CAP occurrence and the number and nature of CAP. The diagnostic value of NT-proBNP for CAP risk was measured using receiver operating characteristic (ROC) curves.

RESULTS

We found a 37% relative increase in the correlation between changes in NT-proBNP per standard deviation (SD) and the incidence of CAP. After adjusting for potential confounders, NT-proBNP at the T3 level was found to be associated with an increased CAP odds ratio (OR) when T1 was used as the reference. This relationship was also present in males, patients aged > 60 years, or both pre-DM and DM states. NT-proBNP was more likely to present as hypoechoic plaques at T1 and as mixed plaques at T3. We also measured the diagnostic accuracy of CAP for NT-proBNP in patients with CHD, with an AUC value of 0.627(95% CI 0.592-0.631), sensitivity of 50.7%, and specificity of 68.0%.

CONCLUSION

An increase in NT-proBNP was significantly associated with the risk of CAP in patients with CHD, especially in males and patients aged > 60 years, and exhibited specific characteristics under different glucose metabolism states. Trial registration The study was approved by the Ethics Committee of Tianjin University of Traditional Chinese Medicine (Approval number TJUTCM-EC20210007) and certified by the Chinese Clinical Trials Registry on April 4, 2022 (Registration number ChiCTR2200058296) and March 25, 2022 by ClinicalTrials.gov (registration number NCT05309343).