Demographics of carotid atherosclerotic plaque features imaged by computed tomography

Extracranial Carotid Plaque Calcification and Cerebrovascular Ischemia: A Systematic Review and Meta-Analysis

BACKGROUND

Although coronary calcification quantification is an established approach for cardiovascular risk assessment, the value of quantifying carotid calcification is less clear. As a result, we performed a systematic review and meta-analysis to evaluate the association between extracranial carotid artery plaque calcification burden and ipsilateral cerebrovascular ischemic events.

METHODS

A comprehensive literature search was performed in the following databases: Ovid MEDLINE(R) 1946 to July 6, 2022; OVID Embase 1974 to July 6, 2022; and The Cochrane Library (Wiley). We performed meta-analyses including studies in which investigators performed a computed tomography assessment of calcification volume, percentage, or other total calcium burden summarizable in a single continuous imaging biomarker and determined the association of these features with the occurrence of ipsilateral stroke or transient ischemic attack.

RESULTS

Our overall meta-analysis consisted of 2239 carotid arteries and 9 studies. The presence of calcification in carotid arteries ipsilateral to ischemic stroke or in stroke patients compared with asymptomatic patients did not demonstrate a significant association with ischemic cerebrovascular events (relative risk of 0.75 [95% CI, 0.44-1.28]; P=0.29). When restricted to studies of significant carotid artery stenosis (>50%), the presence of calcification was associated with a reduced risk of ischemic stroke (relative risk of 0.56 [95% CI, 0.38-0.85]; P=0.006). When the analysis was limited to studies of patients with mainly nonstenotic plaques, there was an increased relative risk of ipsilateral ischemic stroke of 1.72 ([95% CI, 1.01-2.91]; P=0.04). Subgroup meta-analyses of total calcium burden and morphological features of calcium showed wide variability in their strength of association with ischemic stroke and demonstrated significant heterogeneity.

CONCLUSIONS

The presence of calcification in carotid plaque confers a reduced association with ipsilateral ischemic events, although these results seem to be limited among carotid arteries with higher degrees of stenosis. Adoption of carotid calcification measures in clinical decision-making will require additional studies providing more reproducible and standardized methods of calcium characterization and testing these imaging strategies in prospective studies.

A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework

BACKGROUND

Early and automated detection of carotid plaques prevents strokes, which are the second leading cause of death worldwide according to the World Health Organization. Artificial intelligence (AI) offers automated solutions for plaque tissue characterization. Recently, solo deep learning (SDL) models have been used, but they do not take advantage of the tandem connectivity offered by AI's hybrid nature. Therefore, this study explores the use of hybrid deep learning (HDL) models in a multicenter framework, making this study the first of its kind.

METHODS

We hypothesize that HDL techniques perform better than SDL and transfer learning (TL) techniques. We propose two kinds of HDL frameworks: (i) the fusion of two SDLs (Inception with ResNet) or (ii) 10 other kinds of tandem models that fuse SDL with ML. The system Atheromatic™ 2.0_HDL (AtheroPoint, CA, USA) was designed on an augmentation framework and three kinds of loss functions (cross-entropy, hinge, and mean-square-error) during training to determine the best optimization paradigm. These 11 combined HDL models were then benchmarked against one SDL model and five types of TL models; thus, this study considers a total of 17 AI models.

RESULTS

Among the 17 AI models, the best performing HDL system was that comprising CNN and decision tree (DT), as its accuracy and area-under-the-curve were 99.78 ± 1.05% and 0.99 (p<0.0001), respectively. These values are 6.4% and 3.2% better than those recorded for the SDL and TL models, respectively. We validated the performance of the HDL models with diagnostics odds ratio (DOR) and Cohen and Kappa statistics; here, HDL outperformed DL and TL by 23% and 7%, respectively. The online system ran in <2 s.

CONCLUSION

HDL is a fast, reliable, and effective tool for characterizing the carotid plaque for early stroke risk stratification.

Hybrid deep learning segmentation models for atherosclerotic plaque in internal carotid artery B-mode ultrasound

The automated and accurate carotid plaque segmentation in B-mode ultrasound (US) is an essential part of stroke risk stratification. Previous segmented methods used AtheroEdge™ 2.0 (AtheroPoint™, Roseville, CA) for the common carotid artery (CCA). This study focuses on automated plaque segmentation in the internal carotid artery (ICA) using solo deep learning (SDL) and hybrid deep learning (HDL) models. The methodology consists of a novel design of 10 types of SDL/HDL models (AtheroEdge™ 3.0 systems (AtheroPoint™, Roseville, CA) with a depth of four layers each. Five of the models use cross-entropy (CE)-loss, and the other five models use Dice similarity coefficient (DSC)-loss functions derived from UNet, UNet+, SegNet, SegNet-UNet, and SegNet-UNet+. The K10 protocol (Train:Test:90%:10%) was applied for all 10 models for training and predicting (segmenting) the plaque region, which was then quantified to compute the plaque area in mm². Further, the data augmentation effect was analyzed. The database consisted of 970 ICA B-mode US scans taken from 99 moderate to high-risk patients. Using the difference area threshold of 10 mm² between ground truth (GT) and artificial intelligence (AI), the area under the curve (AUC) values were 0.91, 0.911, 0.908, 0.905, and 0.898, all with a p-value of <0.001 (for CE-loss models) and 0.883, 0.889, 0.905, 0.889, and 0.907, all with a p-value of <0.001 (for DSC-loss models). The correlations between the AI-based plaque area and GT plaque area were 0.98, 0.96, 0.97, 0.98, and 0.97, all with a p-value of <0.001 (for CE-loss models) and 0.98, 0.98, 0.97, 0.98, and 0.98 (for DSC-loss models). Overall, the online system performs plaque segmentation in less than 1 s. We validate our hypothesis that HDL and SDL models demonstrate comparable performance. SegNet-UNet was the best-performing hybrid architecture.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

Comparing Symptomatic and Asymptomatic Carotid Artery Atherosclerosis in Patients With Bilateral Carotid Vulnerable Plaques Using Magnetic Resonance Imaging

We compared plaque characteristics between symptomatic and asymptomatic sides in patients with bilateral carotid vulnerable plaques using magnetic resonance imaging (MRI). Participants (n = 67; mean age: 65.8 ± 7.7 years, 61 males) with bilateral carotid vulnerable plaques were included. Vulnerable plaques were characterized by intraplaque hemorrhage (IPH), large lipid-rich necrotic core (LRNC), or fibrous cap rupture (FCR) on MRI. Symptomatic vulnerable plaques showed greater plaque burden, LRNC volume (median: 221.4 vs 134.8 mm³, P = .003), IPH volume (median: 32.2 vs 22.5 mm³, P = .030), maximum percentage (Max%) LRNC (median: 51.3% vs 41.8%, P = .002), Max%IPH (median: 13.4% vs 9.5%, P = .022), cumulative slices of LRNC (median: 10 vs 8, P = .005), and more juxtaluminal IPH and/or thrombus (29.9% vs 6.0%, P = .001) and FCR (37.3% vs 16.4%, P = .007) than asymptomatic ones. After adjusting for plaque burden, differences in juxtaluminal IPH and/or thrombus (odds ratio [OR]: 5.49, 95% CI: 1.61-18.75, P = .007) and FCR (OR: 2.90, 95% CI: 1.16-7.24, P = .022) between bilateral sides remained statistically significant. For patients with bilateral carotid vulnerable plaques, symptomatic plaques had greater burden, more juxtaluminal IPH and/or thrombus, and FCR compared with asymptomatic ones. The differences in juxtaluminal IPH and/or thrombus and FCR between bilateral sides were independent of plaque burden.

3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0

BACKGROUND AND PURPOSE

Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)-based plaque tissue classification and characterization system.

METHODS

We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra.

RESULTS

After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%-10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p < 0.0001). It took <2 s to perform the online CADx process on a supercomputer.

CONCLUSIONS

The performance order of the three AI systems was DCNN > tCNN > ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization.

Carotid plaque imaging profiling in subjects with risk factors (diabetes and hypertension)

Carotid artery stenosis (CAS) due to the presence of atherosclerotic plaque (AP) is a frequent medical condition and a known risk factor for stroke, and it is also known from literature that several risk factors promote the AP development, in particular aging, smoke, male sex, hypertension, hyperlipidemia, smoke, diabetes type 1 and 2, and genetic factors. The study of carotid atherosclerosis is continuously evolving: even if the strategies of treatment still depends mainly on the degree of stenosis (DoS) determined by the plaque, in the last years the attention has moved to the study of the plaque components in order to identify the so called "vulnerable" plaque: features like the fibrous cap status and thickness, the volume of the lipid-rich necrotic core and the presence of intraplaque hemorrhage (IPH) are risk factors for plaque rupture, that can be studied with modern imaging techniques. The aim of this review is to give a general overview of the principle histological and imaging features of the subcomponent of carotid AP (CAP), focalizing in particular on the features of CAP of patients affected by hypertension and diabetes (in particular type 2 diabetes mellitus).