Carotid artery calcium score: Definition, classification, application, and limits.

INTRODUCTION: In the current paper, the "carotid artery calcium score" method is presented with the target to offer a metric method to quantify the amount of calcification in the carotid artery. MODEL AND DEFINITION: The Volume of Interest (VOI) should be extracted and those voxels, with a Hounsfield Unit (HU) value ≥130, should be considered. The total weight value is determined by calculating the sum of the HU attenuation values of all voxels with values ≥130 HU. This value should be multiplied by the conversion factor ("or voxel size") and divided by a weighting factor, the attenuation threshold to consider a voxel as calcified (and therefore 130 HU): this equation determines the Carotid Artery Calcium Score (CACS). RESULTS: In order to provide the demonstration of the potential feasibility of the model, the CACS was calculated in 131 subjects (94 males; mean age 72.7 years) for 235 carotid arteries (in 27 subjects, unilateral plaque was present) considered. The CACS value ranged from 0.67 to 11716. A statistically significant correlation was found (rho value = 0.663, p value = .0001) between the CACS in the right and left carotid plaques. Moreover, a statistically significant correlation between the age and the total CACS was present (rho value = 0.244, p value = .005), whereas no statistically significant difference was found in the distribution of CACS by gender (p = .148). The CACS was also tested at baseline and after contrast and no statistically significant difference was found. CONCLUSION: In conclusion, this method is of easy application, and it weights at the same time the volume and the degree of calcification in a unique parameter. This method needs to be tested to verify its potential utility, similar to the coronary artery calcium score, for the risk stratification of the occurrence of cerebrovascular events of the anterior circulation. Further studies using this new diagnostic tool to determine the prognostic value of carotid calcium quantification are needed.

Lessons from autopsy: Topographical variability of atherosclerosis plaques.

Atherosclerosis is a complex disease characterized by the accumulation of plaques in arterial walls. Understanding its pathogenesis remains incomplete, with factors like inflammation, oxidative stress, and hypertension playing critical roles. The disease exhibits preferential localization of plaques, with variability observed even within the same individual. Genetic, environmental, and lifestyle factors contribute to its heterogeneity. Histological plaque phenotypes vary widely, prompting classification schemes focusing on systemic and local factors deteriorating fibrous caps. Recent research highlights differences in plaque histology among arterial systems, suggesting unique pathophysiological mechanisms. This study reports on multiple atherosclerotic plaques detected at autopsy in various vascular sites of a single subject, emphasizing their histological diversity and underscoring the systemic nature of atherosclerosis.

Radiomics and artificial intelligence: General notions and applications in the carotid vulnerable plaque.

Carotid atherosclerosis plays a substantial role in cardiovascular morbidity and mortality. Given the multifaceted impact of this disease, there has been increasing interest in harnessing artificial intelligence (AI) and radiomics as complementary tools for the quantitative analysis of medical imaging data. This integrated approach holds promise not only in refining medical imaging data analysis but also in optimizing the utilization of radiologists' expertise. By automating time consuming tasks, AI allows radiologists to focus on more pertinent responsibilities. Simultaneously, the capacity of AI in radiomics to extract nuanced patterns from raw data enhances the exploration of carotid atherosclerosis, advancing efforts in terms of (1) early detection and diagnosis, (2) risk stratification and predictive modeling, (3) improving workflow efficiency, and (4) contributing to advancements in research. This review provides an overview of general concepts related to radiomics and AI, along with their application in the field of carotid vulnerable plaque. It also offers insights into various research studies conducted on this topic across different imaging techniques.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Structural Brain MR Imaging Alterations in Patients with Systemic Lupus Erythematous with and without Neuropsychiatric Events.

BACKGROUND AND PURPOSE: Systemic lupus erythematosus is a complex autoimmune disease known for its diverse clinical manifestations, including neuropsychiatric systemic lupus erythematosus, which impacts a patient's quality of life. Our aim was to explore the relationships among brain MR imaging morphometric findings, neuropsychiatric events, and laboratory values in patients with systemic lupus erythematosus, shedding light on potential volumetric biomarkers and diagnostic indicators for neuropsychiatric systemic lupus erythematosus. MATERIALS AND METHODS: Twenty-seven patients with systemic lupus erythematosus (14 with neuropsychiatric systemic lupus erythematosus, 13 with systemic lupus erythematosus), 24 women and 3 men (average age, 43 years, ranging from 21 to 62 years) were included in this cross-sectional study, along with 10 neuropsychiatric patients as controls. An MR imaging morphometric analysis, with the VolBrain online platform, to quantitatively assess brain structural features and their differences between patients with neuropsychiatric systemic lupus erythematosus and systemic lupus erythematosus, was performed. Correlations and differences between MR imaging morphometric findings and laboratory values, including disease activity scores, such as the Systemic Lupus Erythematosus Disease Activity Index and the Systemic Lupus International Collaborating Clinics Damage Index, were explored. An ordinary least squares regression analysis further explored the Systemic Lupus Erythematosus Disease Activity Index and Systemic Lupus International Collaborating Clinics Damage Index relationship with MR imaging features. RESULTS: For neuropsychiatric systemic lupus erythematosus and non-neuropsychiatric systemic lupus erythematosus, the brain regions with the largest difference in volumetric measurements were the insular central operculum volume (P value = .003) and the occipital cortex thickness (P = .003), which were lower in neuropsychiatric systemic lupus erythematosus. The partial correlation analysis showed that the most correlated morphometric features with neuropsychiatric systemic lupus erythematosus were subcallosal area thickness asymmetry (P < .001) and temporal pole thickness asymmetry (P = .011). The ordinary least squares regression analysis yielded an R 2 of 0.725 for the Systemic Lupus Erythematosus Disease Activity Index score, with calcarine cortex volume as a significant predictor, and an R 2 of 0.715 for the Systemic Lupus International Collaborating Clinics Damage Index score, with medial postcentral gyrus volume as a significant predictor. CONCLUSIONS: The MR imaging volumetric analysis, along with the correlation study and the ordinary least squares regression analysis, revealed significant differences in brain regions and their characteristics between patients with neuropsychiatric systemic lupus erythematosus and those with systemic lupus erythematosus, as well as between patients with different Systemic Lupus Erythematosus Disease Activity Index and Systemic Lupus International Collaborating Clinics Damage Index scores.

The fascinating theory of fetal programming of adult diseases: A review of the fundamentals of the Barker hypothesis.

The theory of fetal programming of adult diseases was first proposed by David J.P. Barker in the eighties of the previous century, to explain the higher susceptibility of some people toward the development of ischemic heart disease. According to his hypothesis, poor maternal living conditions during gestation represent an important risk factor for the onset of atherosclerotic heart disease later in life. The analysis of the early phases of fetal development is a fundamental tool for the risk stratification of children and adults, allowing the identification of susceptible or resistant subjects to multiple diseases later in life. Here, we provide a narrative summary of the most relevant evidence supporting the Barker hypothesis in multiple fields of medicine, including neuropsychiatric disorders, such as Parkinson disease and Alzheimer disease, kidney failure, atherosclerosis, coronary heart disease, stroke, diabetes, cancer onset and progression, metabolic syndrome, and infectious diseases including COVID-19. Given the consensus on the role of body weight at birth as a practical indicator of the fetal nutritional status during gestation, every subject with a low birth weight should be considered an "at risk" subject for the development of multiple diseases later in life. The hypothesis of the "physiological regenerative medicine," able to improve fetal organs' development in the perinatal period is discussed, in the light of recent experimental data indicating Thymosin Beta-4 as a powerful growth promoter when administered to pregnant mothers before birth.

Cine-cardiac magnetic resonance to distinguish between ischemic and non-ischemic cardiomyopathies: a machine learning approach.

OBJECTIVE: This work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR). METHODS: This retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM. RESULTS: A total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47-1.00]) and lowest Brier score (0.19, 95% CI [0.13-0.27]), showing competitive diagnostic accuracy and calibration. At the Youden's index, sensitivity was 0.72 (95% CI [0.68-0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients. CONCLUSION: The current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy. CLINICAL RELEVANCE STATEMENT: A machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition. KEY POINTS: • The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols. • Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies. • Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.

Atrial and ventricular strain using cardiovascular magnetic resonance in the prediction of outcomes of pericarditis patients: a pilot study.

OBJECTIVE: Our study aimed to explore with cardiovascular magnetic resonance (CMR) the impact of left atrial (LA) and left ventricular (LV) myocardial strain in patients with acute pericarditis and to investigate their possible prognostic significance in adverse outcomes. METHOD: This retrospective study performed CMR scans in 36 consecutive patients with acute pericarditis (24 males, age 52 [23-52]). The primary endpoint was the combination of recurrent pericarditis, constrictive pericarditis, and surgery for pericardial diseases defined as pericardial events. Atrial and ventricular strain function were performed on conventional cine SSFP sequences. RESULTS: After a median follow-up time of 16 months (interquartile range [13-24]), 12 patients with acute pericarditis reached the primary endpoint. In multivariable Cox regression analysis, LA reservoir and LA conduit strain parameters were all independent determinants of adverse pericardial diseases. Conversely, LV myocardial strain parameters did not remain an independent predictor of outcome. With receiving operating characteristics curve analysis, LA conduit and reservoir strain showed excellent predictive performance (area under the curve of 0.914 and 0.895, respectively) for outcome prediction at 12 months. CONCLUSION: LA reservoir and conduit mechanisms on CMR are independently associated with a higher risk of adverse pericardial events. Including atrial strain parameters in the management of acute pericarditis may improve risk stratification. CLINICAL RELEVANCE STATEMENT: Atrial strain could be a suitable non-invasive and non-contrast cardiovascular magnetic resonance parameter for predicting adverse pericardial complications in patients with acute pericarditis. KEY POINTS: • Myocardial strain is a well-validated CMR parameter for risk stratification in cardiovascular diseases. • LA reservoir and conduit functions are significantly associated with adverse pericardial events. • Atrial strain may serve as an additional non-contrast CMR parameter for stratifying patients with acute pericarditis.

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

aiGeneR 1.0: An Artificial Intelligence Technique for the Revelation of Informative and Antibiotic Resistant Genes in Escherichia coli.

BACKGROUND: There are several antibiotic resistance genes (ARG) for the Escherichia coli (E. coli) bacteria that cause urinary tract infections (UTI), and it is therefore important to identify these ARG. Artificial Intelligence (AI) has been used previously in the field of gene expression data, but never adopted for the detection and classification of bacterial ARG. We hypothesize, if the data is correctly conferred, right features are selected, and Deep Learning (DL) classification models are optimized, then (i) non-linear DL models would perform better than Machine Learning (ML) models, (ii) leads to higher accuracy, (iii) can identify the hub genes, and, (iv) can identify gene pathways accurately. We have therefore designed aiGeneR, the first of its kind system that uses DL-based models to identify ARG in E. coli in gene expression data. METHODOLOGY: The aiGeneR consists of a tandem connection of quality control embedded with feature extraction and AI-based classification of ARG. We adopted a cross-validation approach to evaluate the performance of aiGeneR using accuracy, precision, recall, and F1-score. Further, we analyzed the effect of sample size ensuring generalization of models and compare against the power analysis. The aiGeneR was validated scientifically and biologically for hub genes and pathways. We benchmarked aiGeneR against two linear and two other non-linear AI models. RESULTS: The aiGeneR identifies tetM (an ARG) and showed an accuracy of 93% with area under the curve (AUC) of 0.99 (p < 0.05). The mean accuracy of non-linear models was 22% higher compared to linear models. We scientifically and biologically validated the aiGeneR. CONCLUSIONS: aiGeneR successfully detected the E. coli genes validating our four hypotheses.

Atrial and Ventricular Strain Imaging Using CMR in the Prediction of Ventricular Arrhythmia in Patients with Myocarditis.

(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.

Artificial Intelligence in the Differential Diagnosis of Cardiomyopathy Phenotypes.

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.

Carotid stenosis and cryptogenic stroke.

OBJECTIVES: Cryptogenic stroke represents a type of ischemic stroke with an unknown origin, presenting a significant challenge in both stroke management and prevention. According to the Trial of Org 10,172 in Acute Stroke Treatment criteria, a stroke is categorized as being caused by large artery atherosclerosis only when there is >50% luminal narrowing of the ipsilateral internal carotid artery. However, nonstenosing carotid artery plaques can be an underlying cause of ischemic stroke. Indeed, emerging evidence documents that some features of plaque vulnerability may act as an independent risk factor, regardless of the degree of stenosis, in precipitating cerebrovascular events. This review, drawing from an array of imaging-based studies, explores the predictive values of carotid imaging modalities in the detection of nonstenosing carotid plaque (<50%), that could be the cause of a cerebrovascular event when some features of vulnerability are present. METHODS: Google Scholar, Scopus, and PubMed were searched for articles on cryptogenic stroke and those reporting the association between cryptogenic stroke and imaging features of carotid plaque vulnerability. RESULTS: Despite extensive diagnostic evaluations, the etiology of a considerable proportion of strokes remains undetermined, contributing to the recurrence rate and persistent morbidity in affected individuals. Advances in imaging modalities, such as magnetic resonance imaging, computed tomography scans, and ultrasound examination, facilitate more accurate detection of nonstenosing carotid artery plaque and allow better stratification of stroke risk, leading to a more tailored treatment strategy. CONCLUSIONS: Early detection of nonstenosing carotid plaque with features of vulnerability through carotid imaging techniques impacts the clinical management of cryptogenic stroke, resulting in refined stroke subtype classification and improved patient management. Additional research is required to validate these findings and recommend the integration of these state-of-the-art imaging methodologies into standard diagnostic protocols to improve stroke management and prevention.

Correlation of Cognitive Reappraisal and the Microstructural Properties of the Forceps Minor: A Deductive Exploratory Diffusion Tensor Imaging Study.

Cognitive reappraisal (CR) is a mechanism for emotion regulation, and the prefrontal cortex (PFC) plays a central role in the regulation of emotions. We tested the hypothesis of an association between CR function and microstructural properties of forceps minor (a commissural bundle within the PFC) in healthy subjects (HS). We analyzed a population of 65 young HS of a public dataset. The diffusion tensor imaging (DTI) sequence of every subject was analyzed to extract the derived shape (diameter and volume) and DTI metrics in terms of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of the forceps minor. The CR subscale of the German version of the Emotion Regulation Questionnaire (ERQ) was used for CR assessment. The Shapiro-Wilk test was applied to test the assumption of normality in all these parameters, adopting a statistical threshold at p < 0.05. Whenever appropriate a non-parametric two-tailed partial correlation analysis was applied to test for correlations between the CR ERQ score and the derived shape and DTI metrics, including age and sex as confounders, adopting a statistical threshold at p < 0.05. The non-parametric two-tailed partial correlation analysis revealed a mildly significant correlation with FA (ρ = 0.303; p = 0.016), a weakly significant negative correlation with MD (ρ = - 0.269; p = 0.033), and a mildly significant negative correlation with RD (ρ = - 0.305; p = 0.015). These findings suggest a correlation between DTI microstructural properties of forceps minor and CR.

Effect of late gadolinium enhancement on left atrial impairment in myocarditis patients.

OBJECTIVE: The aims of our study were to investigate the effect of the extent and location of late gadolinium enhancement (LGE) on the left atrium (LA) function in patients with acute myocarditis (AM) using cardiovascular magnetic resonance (CMR). METHOD: This retrospective study performed CMR scans in 113 consecutive patients (89 males, 24 females; mean age 45.8 ± 17.3 years) with AM that met the updated Lake Louise criteria. Reservoir, conduit, and booster LA functions were analyzed by CMR feature tracking using dedicated software. Besides LA strain measurements, myocardial scar location and extent were assigned and quantified by LGE imaging. RESULTS: AM patients with septal LGE had impaired reservoir, conduit, and conduit strain rate function in comparison with AM patients with non-septal LGE (p = 0.001, for all). In fully adjusted multivariable linear regression, reservoir and conduit were significantly associated with left ventricle (LV) LGE location (ß coefficient = 8.205, p = 0.007; ß coefficient = 5.185, p = 0.026; respectively). In addition, LA parameters decreased according to the increase in the extent of LV fibrosis (LGE ≤ 10%; LGE 11-19%; LGE ≥ 20%). After adjustment in multivariable linear regression, the association with LV LGE extent was no longer statistically significant. CONCLUSION: In patients with acute myocarditis, LA function abnormalities are significantly associated with LV LGE location, but not with LGE extent. Septal LGE is paralleled by a deterioration of LA reservoir and conduit function. CLINICAL RELEVANCE STATEMENT: Left atrium dysfunction is associated with the presence of late gadolinium enhancement in the left ventricle septum and can be useful in the clinical prognostication of patients with acute myocarditis, allowing individually tailored treatment. KEY POINTS: • Myocardial fibrosis is related to atrial impairment. • The location of myocardial fibrosis is the main determinant of atrial dysfunction in myocarditis patients. • The quantification of atrial mechanisms may provide more in-depth insight into myocarditis pathophysiology.

Impact of corpus callosum integrity on functional interhemispheric connectivity and cognition in healthy subjects.

To examine the corpus callosum's (CC) integrity in terms of fractional anisotropy (FA) and how it affects resting-state hemispheric connectivity (rs-IHC) and cognitive function in healthy individuals. Sixty-eight healthy individuals were recruited for the study. The global FA (gFA) and FA values of each CC tract (forceps minor, body, tapetum, and forceps major) were evaluated using diffusion-weighted imaging (DWI) sequences. The homotopic functional connectivity technique was used to quantify the effects of FA in the CC tracts on bilateral functional connectivity, including the confounding effect of gFA. Brain regions with higher or lower rs-IHC were identified using the threshold-free cluster enhancement family-wise error-corrected p-value of 0.05. The null hypothesis was rejected if the p-value was ≤ 0.05 for the nonparametric partial correlation technique. Several clusters of increased rs-IHC were identified in relation to the FA of individual CC tracts, each with a unique topographic distribution and extension. Only forceps minor FA values correlated with cognitive scores. The integrity of CC influences rs-IHC differently in healthy subjects. Specifically, forceps minor anisotropy impacts rs-IHC and cognition more than other CC tracts do.

Carotid Plaque-RADS: A Novel Stroke Risk Classification System.

BACKGROUND: Carotid artery atherosclerosis is highly prevalent in the general population and is a well-established risk factor for acute ischemic stroke. Although the morphological characteristics of vulnerable plaques are well recognized, there is a lack of consensus in reporting and interpreting carotid plaque features. OBJECTIVES: The aim of this paper is to establish a consistent and comprehensive approach for imaging and reporting carotid plaque by introducing the Plaque-RADS (Reporting and Data System) score. METHODS: A panel of experts recognized the necessity to develop a classification system for carotid plaque and its defining characteristics. Using a multimodality analysis approach, the Plaque-RADS categories were established through consensus, drawing on existing published reports. RESULTS: The authors present a universal classification that is applicable to both researchers and clinicians. The Plaque-RADS score offers a morphological assessment in addition to the prevailing quantitative parameter of "stenosis." The Plaque-RADS score spans from grade 1 (indicating complete absence of plaque) to grade 4 (representing complicated plaque). Accompanying visual examples are included to facilitate a clear understanding of the Plaque-RADS categories. CONCLUSIONS: Plaque-RADS is a standardized and reliable system of reporting carotid plaque composition and morphology via different imaging modalities, such as ultrasound, computed tomography, and magnetic resonance imaging. This scoring system has the potential to help in the precise identification of patients who may benefit from exclusive medical intervention and those who require alternative treatments, thereby enhancing patient care. A standardized lexicon and structured reporting promise to enhance communication between radiologists, referring clinicians, and scientists.

SBM vs VBM for highlighting similarities and differences between chronotype and Parkinson's MRI scans: a preliminary analysis.

OBJECTIVE: Voxel-Based Morphometry (VBM) and Source-Based Morphometry (SBM) are widely used techniques for analyzing structural Magnetic Resonance Imaging (MRI) data. VBM compares differences in gray and white matter volume, density, or concentration voxel-wise, while SBM identifies patterns of structural variation using independent component analysis. This study aims to compare the performance of VBM and SBM in detecting differences in brain structure across Parkinson's patients and healthy controls, grouped based on their chronotype. METHODS: Thirty-three subjects were divided into three groups: a Parkinson's Group (PG), an Early Chronotype Group (EG), and a Late Chronotype Group (LG). Circadian preference, daytime sleepiness, and sleep quality were assessed, and MRI data were acquired using a 3 T scanner. SBM and VBM were used to test differences and similarities in MRI scans and chronotypes. RESULTS: Results from SBM revealed significant clusters surviving the analysis, with the 1st component for the PG-EG and the 4th component for the PG-LG analysis showing the lowest p-value (< 0.05). Denser gray matter volume (GMV) or white matter volume (WMV) was observed in the Middle Frontal Gyrus and the Lentiform Nucleus through Talairach Coordinates analysis. CONCLUSIONS: This study emphasizes the importance of selecting appropriate methods for analyzing structural MRI data. VBM is effective in identifying local differences in brain structure, while SBM provides a more comprehensive view of structural variation, detecting patterns not captured by VBM. Future studies should consider utilizing both VBM and SBM to fully characterize brain structural differences in diverse clinical and cognitive populations. Further studies, with larger sample sizes and more balanced genders, genomic analysis, disease severity and duration, as well as medications' effect, are warranted.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look.

BACKGROUND AND MOTIVATION: Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS: Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS: UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.