Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

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.

DEep LearnIng-based QuaNtification of epicardial adipose tissue predicts MACE in patients undergoing stress CMR

BACKGROUND AND AIMS

This study investigated the additional prognostic value of epicardial adipose tissue (EAT) volume for major adverse cardiovascular events (MACE) in patients undergoing stress cardiac magnetic resonance (CMR) imaging.

METHODS

730 consecutive patients [mean age: 63 ± 10 years; 616 men] who underwent stress CMR for known or suspected coronary artery disease were randomly divided into derivation (n = 365) and validation (n = 365) cohorts. MACE was defined as non-fatal myocardial infarction and cardiac deaths. A deep learning algorithm was developed and trained to quantify EAT volume from CMR. EAT volume was adjusted for height (EAT volume index). A composite CMR-based risk score by Cox analysis of the risk of MACE was created.

RESULTS

In the derivation cohort, 32 patients (8.7 %) developed MACE during a follow-up of 2103 days. Left ventricular ejection fraction (LVEF) < 35 % (HR 4.407 [95 % CI 1.903-10.202]; p<0.001), stress perfusion defect (HR 3.550 [95 % CI 1.765-7.138]; p<0.001), late gadolinium enhancement (LGE) (HR 4.428 [95%CI 1.822-10.759]; p = 0.001) and EAT volume index (HR 1.082 [95 % CI 1.045-1.120]; p<0.001) were independent predictors of MACE. In a multivariate Cox regression analysis, adding EAT volume index to a composite risk score including LVEF, stress perfusion defect and LGE provided additional value in MACE prediction, with a net reclassification improvement of 0.683 (95%CI, 0.336-1.03; p<0.001). The combined evaluation of risk score and EAT volume index showed a higher Harrel C statistic as compared to risk score (0.85 vs. 0.76; p<0.001) and EAT volume index alone (0.85 vs.0.74; p<0.001). These findings were confirmed in the validation cohort.

CONCLUSIONS

In patients with clinically indicated stress CMR, fully automated EAT volume measured by deep learning can provide additional prognostic information on top of standard clinical and imaging parameters.

Predictors of non-calcified plaque presence and future adverse cardiovascular events in symptomatic rural Appalachian patients with a zero coronary artery calcium score

BACKGROUND

Coronary artery calcium (CAC) scoring is a proven predictor for future adverse cardiovascular events (CVE) in asymptomatic individuals. Data is emerging regarding the usefulness of non-calcified plaque (NCP) assessment on cardiac computed tomography (CCT) angiography in symptomatic patients with a zero CAC score for further risk assessment.

METHODS

A retrospective review from January 2019 to January 2022 of 696 symptomatic patients with no known CAD and a zero CAC score identified 181 patients with NCP and 515 patients without NCP by a visual assessment on CCT angiography. The primary endpoint was to identify predictors for NCP presence and adverse CVEs (death, myocardial infarction, or cerebrovascular accident) within two years.

RESULTS

Based on logistic regression, age (OR 1.039, 95% CI [1.020-1.058], p ​< ​0.001), diabetes mellitus (OR 2.192, 95% CI [1.307-3.676], p ​< ​0.003), tobacco use (OR 1.748, 95% CI [1.157-2.643], p ​< ​0.008), low-density lipoprotein cholesterol level (OR 1.009, 95% CI [1.003-1.015], p ​< ​0.002), and hypertension (OR 1.613, 95% CI [1.024-2.540], p ​< ​0.039) were found to be predictors of NCP presence. NCP patients had a higher pretest probability for CAD using the Morise risk score (p ​< ​0.001∗), with NCP detection increasing as pretest probability increased from low to high (OR 55.79, 95% CI [24.26-128.26], p ​< ​0.001∗). 457 patients (66%) reached a full two-year period after CCT angiography completion, with NCP patients noted to have shorter follow-up times and higher rates of elective coronary angiography, intervention, and CVEs. The presence of NCP (aOR 2.178, 95% CI [1.025-4.627], p ​< ​0.043) was identified as an independent predictor for future adverse CVEs when adjusted for diabetes mellitus, age, and hypertension.

CONCLUSION

NCP was identified at high rates (26%) in our symptomatic Appalachian population with no known CAD and a zero CAC score. NCP was identified as an independent predictor of future adverse CVEs within two years.

Artificial intelligence in coronary artery calcium measurement: Barriers and solutions for implementation into daily practice

Coronary artery calcification (CAC) measurement is a valuable predictor of cardiovascular risk. However, its measurement can be time-consuming and complex, thus driving the desire for artificial intelligence (AI)-based approaches. The aim of this review is to explore the current status of CAC volume measurement using AI-based systems for the automated prediction of cardiovascular events. We also make proposals for the implementation of these systems into clinical practice. Research to date on applying AI to CAC scoring has shown the potential for automation and risk stratification, and, overall, efficacy and a high level of agreement with categorisation by trained clinicians have been demonstrated. However, research in this field has not been uniform or directed. One contributing factor may be a lack of integration and communication between computer scientists and cardiologists. Clinicians, institutions, and organisations should work together towards applying this technology to improve processes, preserve healthcare resources, and improve patient outcomes.

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

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.

Artificial Intelligence in Coronary CT Angiography: Current Status and Future Prospects

Coronary heart disease (CHD) is the leading cause of mortality in the world. Early detection and treatment of CHD are crucial. Currently, coronary CT angiography (CCTA) has been the prior choice for CHD screening and diagnosis, but it cannot meet the clinical needs in terms of examination quality, the accuracy of reporting, and the accuracy of prognosis analysis. In recent years, artificial intelligence (AI) has developed rapidly in the field of medicine; it played a key role in auxiliary diagnosis, disease mechanism analysis, and prognosis assessment, including a series of studies related to CHD. In this article, the application and research status of AI in CCTA were summarized and the prospects of this field were also described.

Non-invasive Multimodality Imaging of Coronary Vulnerable Patient

Atherosclerotic plaque rupture or erosion remain the primary mechanism responsible for myocardial infarction and the major challenge of cardiovascular researchers is to develop non-invasive methods of accurate risk prediction to identify vulnerable plaques before the event occurs. Multimodal imaging, by CT-TEP or CT-SPECT, provides both morphological and activity information about the plaque and cumulates the advantages of anatomic and molecular imaging to identify vulnerability features among coronary plaques. However, the rate of acute coronary syndromes remains low and the mechanisms leading to adverse events are clearly more complex than initially assumed. Indeed, recent studies suggest that the detection of a state of vulnerability in a patient is more important than the detection of individual sites of vulnerability as a target of focal treatment. Despite this evolution of concepts, multimodal imaging offers a strong potential to assess patient's vulnerability. Here we review the current state of multimodal imaging to identify vulnerable patients, and then focus on emerging imaging techniques and precision medicine.

Multimodality Imaging in Ischemic Chronic Cardiomyopathy

Ischemic chronic cardiomyopathy (ICC) is still one of the most common cardiac diseases leading to the development of myocardial ischemia, infarction, or heart failure. The application of several imaging modalities can provide information regarding coronary anatomy, coronary artery disease, myocardial ischemia and tissue characterization. In particular, coronary computed tomography angiography (CCTA) can provide information regarding coronary plaque stenosis, its composition, and the possible evaluation of myocardial ischemia using fractional flow reserve CT or CT perfusion. Cardiac magnetic resonance (CMR) can be used to evaluate cardiac function as well as the presence of ischemia. In addition, CMR can be used to characterize the myocardial tissue of hibernated or infarcted myocardium. Echocardiography is the most widely used technique to achieve information regarding function and myocardial wall motion abnormalities during myocardial ischemia. Nuclear medicine can be used to evaluate perfusion in both qualitative and quantitative assessment. In this review we aim to provide an overview regarding the different noninvasive imaging techniques for the evaluation of ICC, providing information ranging from the anatomical assessment of coronary artery arteries to the assessment of ischemic myocardium and myocardial infarction. In particular this review is going to show the different noninvasive approaches based on the specific clinical history of patients with ICC.