Applications of artificial intelligence-based models in vulnerable carotid plaque

Carotid atherosclerotic disease is a widely acknowledged risk factor for ischemic stroke, making it a major concern on a global scale. To alleviate the socio-economic impact of carotid atherosclerotic disease, crucial objectives include prioritizing prevention efforts and early detection. So far, the degree of carotid stenosis has been regarded as the primary parameter for risk assessment and determining appropriate therapeutic interventions. Histopathological and imaging-based studies demonstrated important differences in the risk of cardiovascular events given a similar degree of luminal stenosis, identifying plaque structure and composition as key determinants of either plaque vulnerability or stability. The application of Artificial Intelligence (AI)-based techniques to carotid imaging can offer several solutions for tissue characterization and classification. This review aims to present a comprehensive overview of the main concepts related to AI. Additionally, we review the existing literature on AI-based models in ultrasound (US), computed tomography (CT), and Magnetic Resonance Imaging (MRI) for vulnerable plaque detection, and we finally examine the advantages and limitations of these AI approaches

Volumetric analysis of carotid plaque components and cerebral microbleeds: a correlative study

PURPOSE: The purpose of this work was to explore the association between carotid plaque volume (total and the subcomponents) and cerebral microbleeds (CMBs). MATERIALS AND METHODS: Seventy-two consecutive (male 53; median age 64) patients were retrospectively analyzed. Carotid arteries were studied by using a 16-detector-row computed tomography scanner whereas brain was explored with a 1.5 Tesla system. CMBs were studied using a T2*-weighted gradient-recalled echo sequence. CMBs were classified as from absent (grade 1) to severe (grade 4). Component types of the carotid plaque were defined according to the following Hounsfield unit (HU) ranges: lipid less than 60 HU; fibrous tissue from 60 to 130 HU; calcification greater than 130 HU, and plaque volumes of each component were calculated. Each carotid artery was analyzed by 2 observers. RESULTS: The prevalence of CMBs was 35.3%. A statistically significant difference was observed between symptomatic (40%) and asymptomatic (11%) patients (P value = .001; OR = 6.07). Linear regression analysis demonstrated an association between the number of CMBs and the symptoms (P = .0018). Receiver operating characteristics curve analysis found an association between the carotid plaque subcomponents and CMBs (Az = .608, .621, and .615 for calcified, lipid, and mixed components, respectively), and Mann-Whitney test confirmed this association in particular for the lipid components (P value = .0267). CONCLUSIONS: Results of this study confirm the association between CMBs and symptoms and that there is an increased number of CMBs in symptomatic patients. Moreover, we found that an increased volume of the fatty component is associated with the presence and number of CMBs

CT imaging features of carotid artery plaque vulnerability

Despite steady advances in medical care, cardiovascular disease remains one of the main causes of death and long-term morbidity worldwide. Up to 30% of strokes are associated with the presence of carotid atherosclerotic plaques. While the degree of stenosis has long been recognized as the main guiding factor in risk stratification and therapeutical decisions, recent evidence suggests that features of unstable, or 'vulnerable', plaques offer better prognostication capabilities. This paradigmatic shift has motivated researchers to explore the potentialities of non-invasive diagnostic tools to image not only the lumen, but also the vascular wall and the structural characteristics of the plaque. The present review will offer a panoramic on the imaging modalities currently available to characterize carotid atherosclerotic plaques and, in particular, it will focus on the increasingly important role covered by multidetector computed tomographic angiography

Atrial and Ventricular Involvement in Acute Myocarditis Patients with Preserved Ejection Fraction: A Single-Center Cardiovascular Magnetic Resonance Study

Cardiac magnetic resonance (CMR) is commonly employed to confirm the diagnosis of acute myocarditis (AM). However, the impact of atrial and ventricular function in AM patients with preserved ejection fraction (EF) deserves further investigation. Therefore, the aim of this study was to explore the incremental diagnostic value of combining atrial and strain functions using CMR in patients with AM and preserved EF. This retrospective study collected CMR scans of 126 consecutive patients with AM (meeting the Lake Louise criteria) and with preserved EF, as well as 52 age- and sex-matched control subjects. Left atrial (LA) and left ventricular (LV) strain functions were assessed using conventional cine-SSFP sequences. In patients with AM and preserved EF, impaired ventricular and atrial strain functions were observed compared to control subjects. These impairments remained significant even in multivariable analysis. The combined model of atrial and ventricular functions proved to be the most effective in distinguishing AM patients with preserved ejection fraction from control subjects, achieving an area under the curve of 0.77 and showing a significant improvement in the likelihood ratio. These findings suggest that a combined analysis of both atrial and ventricular functions may improve the diagnostic accuracy for patients with AM and preserved EF

Intra- and inter-operator reproducibility of automated cloud-based carotid lumen diameter ultrasound measurement

Background: Common carotid artery lumen diameter (LD) ultrasound measurement systems are either manual or semi-automated and lack reproducibility and variability studies. This pilot study presents an automated and cloud-based LD measurements software system (AtheroCloud) and evaluates its: (i) intra/inter-operator reproducibility and (ii) intra/inter-observer variability. Methods: 100 patients (83 M, mean age: 68 ± 11 years), IRB approved, consisted of L/R CCA artery (200 ultrasound images), acquired using a 7.5-MHz linear transducer. The intra/inter-operator reproducibility was verified using three operator's readings. Near-wall and far carotid wall borders were manually traced by two observers for intra/inter-observer variability analysis. Results: The mean coefficient of correlation (CC) for intra- and inter-operator reproducibility between all the three automated reading pairs were: 0.99 (P < 0.0001) and 0.97 (P < 0.0001), respectively. The mean CC for intra- and inter-observer variability between both the manual reading pairs were 0.98 (P < 0.0001) and 0.98 (P < 0.0001), respectively. The Figure-of-Merit between the mean of the three automated readings against the four manuals were 98.32%, 99.50%, 98.94% and 98.49%, respectively. Conclusions: The AtheroCloud LD measurement system showed high intra/inter-operator reproducibility hence can be adapted for vascular screening mode or pharmaceutical clinical trial mode

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

Neuroimaging, Networking and Systems Biology: The New Way to Investigate Pathologies with Neurological System Implications. The example of Tourette Syndrome as a Pilot Study

Purpose: Recently, many academic research groups have focused their attention on changes in human brain networks related to several kinds of pathologies and diseases, generating the new discipline termed “Network Medicine”. Purpose of this paper is to investigate the ability of the Network Medicine to give deeper insights in the functionality of brain activity. Material and Methods: In the proposed study of Tourette syndrome, we have investigated with the functional magnetic resonance imaging the possibility that the mechanisms associated with the monitoring and internal control of movements were compromised in individuals with Tourette syndrome; we enrolled 20 Tourette Syndrome patients in comparison with a healthy Controls group of 15 subjects matching for age and sex distribution. We proposed, for the fMRI analysis, a novel task based on the execution of switching between complex movements on demand. Results: The elementary activation model found that the effort related to the task in comparing Tourettic vs Controls mainly concerns the areas of the Gyrus of the Cingulum, the precuneus and the thalamic area of the ventral-lateral nucleus. In particular, the BA11 plays an essential role in the Tourette Patients related to the continue tentative to correct the TIC. Considering the status of the pilot study of this work, we remark the power of proposed methods to investigate the complex interaction of the brain networks. Conclusion: Alteration in brain activity for a population of Tourette Syndrome patients is evaluable by the use of complex indexes, results confirm the literature about this pathology and these medical physics methods can be applied to all neurological diseases investigation by opportune task-driven experiments or by resting state fc-MRI experiments

COVLIAS 3.0: cloud-based quantized hybrid UNet3+ deep learning for COVID-19 lesion detection in lung computed tomography

Background and noveltyWhen RT-PCR is ineffective in early diagnosis and understanding of COVID-19 severity, Computed Tomography (CT) scans are needed for COVID diagnosis, especially in patients having high ground-glass opacities, consolidations, and crazy paving. Radiologists find the manual method for lesion detection in CT very challenging and tedious. Previously solo deep learning (SDL) was tried but they had low to moderate-level performance. This study presents two new cloud-based quantized deep learning UNet3+ hybrid (HDL) models, which incorporated full-scale skip connections to enhance and improve the detections.MethodologyAnnotations from expert radiologists were used to train one SDL (UNet3+), and two HDL models, namely, VGG-UNet3+ and ResNet-UNet3+. For accuracy, 5-fold cross-validation protocols, training on 3,500 CT scans, and testing on unseen 500 CT scans were adopted in the cloud framework. Two kinds of loss functions were used: Dice Similarity (DS) and binary cross-entropy (BCE). Performance was evaluated using (i) Area error, (ii) DS, (iii) Jaccard Index, (iii) Bland–Altman, and (iv) Correlation plots.ResultsAmong the two HDL models, ResNet-UNet3+ was superior to UNet3+ by 17 and 10% for Dice and BCE loss. The models were further compressed using quantization showing a percentage size reduction of 66.76, 36.64, and 46.23%, respectively, for UNet3+, VGG-UNet3+, and ResNet-UNet3+. Its stability and reliability were proved by statistical tests such as the Mann–Whitney, Paired t-Test, Wilcoxon test, and Friedman test all of which had a p < 0.001.ConclusionFull-scale skip connections of UNet3+ with VGG and ResNet in HDL framework proved the hypothesis showing powerful results improving the detection accuracy of COVID-19