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Objectives Patients with SLE have an increased risk of developing cardiovascular disease (CVD). Multiple studies have shown that these patients have increased numbers of carotid plaques and greater intima-media thickness (IMT) than healthy controls. Measures such as total plaque area (TPA) and plaque echogenicity may be more sensitive and more relevant to cardiovascular risk than presence of plaque and IMT alone. Our objective was to produce the first report of TPA and echogenicity in a population of patients with SLE. Methods One hundred patients with SLE and no history of clinical CVD were recruited. Clinical, serological and treatment variables were recorded and serum was tested for antibodies to apolipoprotein A-1 and high-density lipoprotein. Both carotid and both femoral artery bifurcations of each patient were scanned to determine IMT, TPA and echogenicity of plaques. Univariable and multivariable statistical analyses were carried out to define factors associated with each of these outcomes. Results Thirty-six patients had carotid and/or femoral plaque. Increasing age was associated with presence of plaque and increased IMT. Triglyceride levels were associated with presence of plaque. Mean (s. d.) TPA was 60.8 (41.6) mm2. Patients taking prednisolone had higher TPA. Most plaques were echolucent, but increased echogenicity was associated with prednisolone therapy and persistent disease activity. Conclusion TPA and plaque echogenicity in patients with SLE are associated with different factors than those associated with presence of plaque and IMT. Longitudinal studies may show whether these outcome measures add value in the management of cardiovascular risk in SLE. [ABSTRACT FROM AUTHOR]

The objectives of this study are to (1) examine the "10-year cardiovascular risk" in the common carotid artery (CCA) versus carotid bulb using an integrated calculator called "AtheroEdge Composite Risk Score 2.0" (AECRS2.0) and (2) evaluate the performance of AECRS2.0 against "conventional cardiovascular risk calculators." These objectives are met by measuring (1) image-based phenotypes and AECRS2.0 score computation and (2) performance evaluation of AECRS2.0 against 12 conventional cardiovascular risk calculators. The Asian–Indian cohort (n = 379) with type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), or hypertension were retrospectively analyzed by acquiring the 1516 carotid ultrasound scans (mean age: 55 ± 10.1 years, 67% males, ∼92% with T2DM, ∼83% with CKD [stage 1-5], and 87.5% with hypertension [stage 1-2]). The carotid bulb showed a higher 10-year cardiovascular risk compared to the CCA by 18% (P <.0001). Patients with T2DM and/or CKD also followed a similar trend. The carotid bulb demonstrated a superior risk assessment compared to CCA in patients with T2DM and/or CKD by showing: (1) ∼13% better than CCA (0.93 vs 0.82, P =.0001) and (2) ∼29% better compared with 12 types of risk conventional calculators (0.93 vs 0.72, P =.06). [ABSTRACT FROM AUTHOR]

Manual ultrasound (US)-based methods are adapted for lumen diameter (LD) measurement to estimate the risk of stroke but they are tedious, error prone, and subjective causing variability. We propose an automated deep learning (DL)-based system for lumen detection. The system consists of a combination of two DL systems: encoder and decoder for lumen segmentation. The encoder employs a 13-layer convolution neural network model (CNN) for rich feature extraction. The decoder employs three up-sample layers of fully convolution network (FCN) for lumen segmentation. Three sets of manual tracings were used during the training paradigm leading to the design of three DL systems. Cross-validation protocol was implemented for all three DL systems. Using the polyline distance metric, the precision of merit for three DL systems over 407 US scans was 99.61%, 97.75%, and 99.89%, respectively. The Jaccard index and Dice similarity of DL lumen segmented region against three ground truth (GT) regions were 0.94, 0.94, and 0.93 and 0.97, 0.97, and 0.97, respectively. The corresponding AUC for three DL systems was 0.95, 0.91, and 0.93. The experimental results demonstrated superior performance of proposed deep learning system over conventional methods in literature. Graphical abstract ᅟ. [ABSTRACT FROM AUTHOR]

Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings. [ABSTRACT FROM AUTHOR]