Recent Trends in Artificial Intelligence-Assisted Coronary Atherosclerotic Plaque Characterization

Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images

PURPOSE

The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.

METHODS

Two deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods.

RESULTS

For the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method.

CONCLUSION

The two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.

Precision medicine: Hype or hope?

In recent years, precision medicine has steadily risen to the forefront of many aspects of medicine, including cardiology [1]. While this field has exponentially expanded and advanced in the last few years, a lot of questions remain regarding exact definition, usage, and clinical applications [2,3]. This review will provide a brief synopsis of the current state of precision medicine, its limitations, future directions, as well as analyze emerging clinical applications in cardiology.

Review of intravascular lithotripsy for treating coronary, peripheral artery, and valve calcifications

Management of intracoronary calcium (ICC) continues to be a challenge for interventional cardiologists. There have been significant advances in calcium treatment devices. However, there still exists a knowledge gap regarding which devices to choose for the treatment of ICC. The purpose of this manuscript is to review the principles of intravascular lithotripsy (IVL) and clinical data. The technique of IVL will then be compared to alternative calcium treatment devices. Clinical data will be reviewed concerning the treatment of coronary, peripheral artery and valvular calcifications. Controversies to be discussed include how to incorporate IVL into your practice, what is the best approach for treating calcium subtypes, how to approach under-expanded stents, what is the ideal technique for performing IVL, how safe is IVL, whether imaging adds value when performing IVL, and how IVL fits into a treatment program for peripheral arteries and calcified valves.

Advances in Artificial Intelligence-Assisted Coronary Computed Tomographic Angiography for Atherosclerotic Plaque Characterization

Coronary artery disease is a leading cause of death worldwide. Major adverse cardiac events are associated not only with coronary luminal stenosis but also with atherosclerotic plaque components. Coronary computed tomography angiography (CCTA) enables non-invasive evaluation of atherosclerotic plaque along the entire coronary tree. However, precise and efficient assessment of plaque features on CCTA is still a challenge for physicians in daily practice. Artificial intelligence (AI) refers to algorithms that can simulate intelligent human behavior to improve clinical work efficiency. Recently, cardiovascular imaging has seen remarkable advancements with the use of AI. AI-assisted CCTA has the potential to facilitate the clinical workflow, offer objective and repeatable quantitative results, accelerate the interpretation of reports, and guide subsequent treatment. Several AI algorithms have been developed to provide a comprehensive assessment of atherosclerotic plaques. This review serves to highlight the cutting-edge applications of AI-assisted CCTA in atherosclerosis plaque characterization, including detecting obstructive plaques, assessing plaque volumes and vulnerability, monitoring plaque progression, and providing risk assessment. Finally, this paper discusses the current problems and future directions for implementing AI in real-world clinical settings.

CT Coronary Angiography: Technical Approach and Atherosclerotic Plaque Characterization

Coronary computed tomography angiography (CCTA) currently represents a robust imaging technique for the detection, quantification and characterization of coronary atherosclerosis. However, CCTA remains a challenging task requiring both high spatial and temporal resolution to provide motion-free images of the coronary arteries. Several CCTA features, such as low attenuation, positive remodeling, spotty calcification, napkin-ring and high pericoronary fat attenuation index have been proved as associated to high-risk plaques. This review aims to explore the role of CCTA in the characterization of high-risk atherosclerotic plaque and the recent advancements in CCTA technologies with a focus on radiomics plaque analysis.

Management of Residual Risk in Chronic Coronary Syndromes. Clinical Pathways for a Quality-Based Secondary Prevention

Chronic coronary syndrome (CCS), which encompasses a broad spectrum of clinical presentations of coronary artery disease (CAD), is the leading cause of morbidity and mortality worldwide. Recent guidelines for the management of CCS emphasize the dynamic nature of the CAD process, replacing the term "stable" with "chronic", as this disease is never truly "stable". Despite significant advances in the treatment of CAD, patients with CCS remain at an elevated risk of major cardiovascular events (MACE) due to the so-called residual cardiovascular risk. Several pathogenetic pathways (thrombotic, inflammatory, metabolic, and procedural) may distinctly contribute to the residual risk in individual patients and represent a potential target for newer preventive treatments. Identifying the level and type of residual cardiovascular risk is essential for selecting the most appropriate diagnostic tests and follow-up procedures. In addition, new management strategies and healthcare models could further support available treatments and lead to important prognostic benefits. This review aims to provide an overview of the diagnostic and therapeutic challenges in the management of patients with CCS and to promote more effective multidisciplinary care.

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 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.

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed.

Automated Coronary Optical Coherence Tomography Feature Extraction with Application to Three-Dimensional Reconstruction

Coronary optical coherence tomography (OCT) is an intravascular, near-infrared light-based imaging modality capable of reaching axial resolutions of 10-20 µm. This resolution allows for accurate determination of high-risk plaque features, such as thin cap fibroatheroma; however, visualization of morphological features alone still provides unreliable positive predictive capability for plaque progression or future major adverse cardiovascular events (MACE). Biomechanical simulation could assist in this prediction, but this requires extracting morphological features from intravascular imaging to construct accurate three-dimensional (3D) simulations of patients' arteries. Extracting these features is a laborious process, often carried out manually by trained experts. To address this challenge, numerous techniques have emerged to automate these processes while simultaneously overcoming difficulties associated with OCT imaging, such as its limited penetration depth. This systematic review summarizes advances in automated segmentation techniques from the past five years (2016-2021) with a focus on their application to the 3D reconstruction of vessels and their subsequent simulation. We discuss four categories based on the feature being processed, namely: coronary lumen; artery layers; plaque characteristics and subtypes; and stents. Areas for future innovation are also discussed as well as their potential for future translation.