BACKGROUND AND PURPOSE: CT is considered the standard reference both for quantification and characterization of carotid artery calcifications. Our aim was to investigate the relationship among different types of calcium configurations detected with CT within the plaque with a novel classification and to investigate the prevalence of cerebrovascular events. MATERIALS AND METHODS: Seven hundred ninety patients (men = 332; mean age, 69.7 [SD, 13] years; 508 symptomatic for cerebrovascular symptoms and 282 asymptomatic) who underwent computed tomography of the carotid arteries were retrospectively included in this institutional review board–approved study. The plaque was classified into 6 types according to the different types of calcium configurations as the following: type 1, complete absence of calcification within the plaque; type 2, intimal or superficial calcifications; type 3, deep or bulky calcifications; type 4, adventitial calcifications with internal soft plaque of <2 mm thickness; type 5, mixed patterns with intimal and bulky calcifications; and type 6, positive rim sign. RESULTS: The highest prevalence of cerebrovascular events was observed for type 6, for which 89 of the 99 cases were symptomatic. Type 6 plaque had the highest degree of correlation with TIA, stroke, symptoms, and ipsilateral infarct for both sides with a higher prevalence in younger patients. The frequency of symptoms observed by configuration type significantly differed between right and left plaques, with symptoms observed more frequently in type 6 calcification on the right side (50/53; 94%) than on the left side (39/46; 85%, P < .001). CONCLUSIONS: We propose a novel carotid artery plaque configuration classification that is associated with the prevalence of cerebrovascular events. If confirmed in longitudinal analysis, this classification could be used to stratify the risk of occurrence of ischemic events.

BACKGROUND: This study aimed to develop and validate a computed tomography angiography based machine learning model that uses plaque composition data and degree of carotid stenosis to detect symptomatic carotid plaques in patients with carotid atherosclerosis. METHODS: The machine learning based model was trained using degree of stenosis and the volumes of 13 computed tomography angiography derived intracarotid plaque subcomponents (eg, lipid, intraplaque hemorrhage, calcium) to identify plaques associated with cerebrovascular events. The model was internally validated through repeated 10-fold cross-validation and tested on a dedicated testing cohort according to discrimination and calibration. RESULTS: This retrospective, single-center study evaluated computed tomography angiography scans of 268 patients with both symptomatic and asymptomatic carotid atherosclerosis (163 for the derivation set and 106 for the testing set) performed between March 2013 and October 2019. The area-under-receiver-operating characteristics curve by machine learning on the testing cohort (0.89) was significantly higher than the areas under the curve of traditional logit analysis based on the degree of stenosis (0.51, P <0.001), presence of intraplaque hemorrhage (0.69, P <0.001), and plaque composition (0.78, P <0.001), respectively. Comparable performance was obtained on internal validation. The identified plaque components and associated cutoff values that were significantly associated with a higher likelihood of symptomatic status after adjustment were the ratio of intraplaque hemorrhage to lipid volume (≥50%, 38.5 [10.1–205.1]; odds ratio, 95% CI) and percentage of intraplaque hemorrhage volume (≥10%, 18.5 [5.7–69.4]; odds ratio, 95% CI). CONCLUSIONS: This study presented an interpretable machine learning model that accurately identifies symptomatic carotid plaques using computed tomography angiography derived plaque composition features, aiding clinical decision-making.

In recent years, cardiovascular imaging examinations have experienced exponential growth due to technological innovation, and this trend is consistent with the most recent chest pain guidelines. Contrast media have a crucial role in cardiovascular magnetic resonance (CMR) imaging, allowing for more precise characterization of different cardiovascular diseases. However, contrast media have contraindications and side effects that limit their clinical application in determinant patients. The application of artificial intelligence (AI)-based techniques to CMR imaging has led to the development of non-contrast models. These AI models utilize non-contrast imaging data, either independently or in combination with clinical and demographic data, as input to generate diagnostic or prognostic algorithms. In this review, we provide an overview of the main concepts pertaining to AI, review the existing literature on non-contrast AI models in CMR, and finally, discuss the strengths and limitations of these AI models and their possible future development.

Artificial intelligence (AI) is rapidly being applied to the medical field, especially in the cardiovascular domain. AI approaches have demonstrated their applicability in the detection, diagnosis, and management of several cardiovascular diseases, enhancing disease stratification and typing. Cardiomyopathies are a leading cause of heart failure and life-threatening ventricular arrhythmias. Identifying the etiologies is fundamental for the management and diagnostic pathway of these heart muscle diseases, requiring the integration of various data, including personal and family history, clinical examination, electrocardiography, and laboratory investigations, as well as multimodality imaging, making the clinical diagnosis challenging. In this scenario, AI has demonstrated its capability to capture subtle connections from a multitude of multiparametric datasets, enabling the discovery of hidden relationships in data and handling more complex tasks than traditional methods. This review aims to present a comprehensive overview of the main concepts related to AI and its subset. Additionally, we review the existing literature on AI-based models in the differential diagnosis of cardiomyopathy phenotypes, and we finally examine the advantages and limitations of these AI approaches.

(1) Objective: Myocarditis can be associated with ventricular arrhythmia (VA), individual non-invasive risk stratification through cardiovascular magnetic resonance (CMR) is of great clinical significance. Our study aimed to explore whether left atrial (LA) and left ventricle (LV) myocardial strain serve as independent predictors of VA in patients with myocarditis. (2) Methods: This retrospective study evaluated CMR scans in 141 consecutive patients diagnosed with myocarditis based on the updated Lake Louise criteria (29 females, mean age 41 ± 20). The primary endpoint was VA; this encompassed ventricular fibrillation, sustained ventricular tachycardia, nonsustained ventricular tachycardia, and frequent premature ventricular complexes. LA and LV strain function were performed on conventional cine SSFP sequences. (3) Results: After a median follow-up time of 23 months (interquartile range (18–30)), 17 patients with acute myocarditis reached the primary endpoint. In the multivariable Cox regression analysis, LA reservoir (hazard ratio [HR] and 95% confidence interval [CI]: 0.93 [0.87–0.99], p = 0.02), LA booster (0.87 95% CI [0.76–0.99], p = 0.04), LV global longitudinal (1.26 95% CI [1.02–1.55], p = 0.03), circumferential (1.37 95% CI [1.08–1.73], p = 0.008), and radial strain (0.89 95% CI [0.80–0.98], p = 0.01) were all independent determinants of VA. Patients with LV global circumferential strain > −13.3% exhibited worse event-free survival compared to those with values ≤ −13.3% (p < 0.0001). (4) Conclusions: LA and LV strain mechanism on CMR are independently associated with VA events in patients with myocarditis, independent to LV ejection fraction, and late gadolinium enhancement location. Incorporating myocardial strain parameters into the management of myocarditis may improve risk stratification.

Objective: The purpose of this study was to explore the impact of pericardial T1 mapping as a potential supportive non-contrast cardiovascular magnetic resonance (CMR) parameter in the diagnosis of acute pericarditis. Additionally, we investigated the relationship between T1 mapping values in acute pericarditis patients and their demographic data, cardiovascular risk factors, clinical parameters, cardiac biomarkers, and cardiac function. Method: This retrospective study included CMR scans in 35 consecutive patients with acute pericarditis (26 males, 45.54 ± 23.38 years). Moreover, we included 17 sex- and age-matched healthy controls (12 males, mean age 47.78 ±19.38 years). CMR-derived pericardial T1 mapping values, which included all pericardial structures within the pericardial layers—encompassing both pericardial effusion and pericardial layer thickness—were analyzed and compared between acute pericarditis patients and controls. Results: Compared to the matched control group, acute pericarditis patients demonstrated significantly lower pericardial T1 mapping values (2137 ms ± 519 vs. 3268 ms ± 362, p = 0.001). In the multivariable analysis, the pericardial T1 mapping value was independently associated with the severity of pericardial late gadolinium enhancement (LGE) (β coefficient = −3.271, p = 0.003). The receiver operating characteristic curve analysis showed that the diagnostic performance of pericardial T1 mapping in discriminating acute pericarditis patients was excellent, with an area under the curve of 0.97 (95% CI = 0.94–0.98), using a threshold of 2862.5 ms. Conclusions: Pericardial T1 mapping values could serve as an additional non-contrast CMR parameter for identifying patients with acute pericarditis, demonstrating an independent association with the severity of pericardial LGE.