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Today, the 10-year cardiovascular risk largely relies on conventional cardiovascular risk factors (CCVRFs) and suffers from the effect of atherosclerotic wall changes. In this study, we present a novel risk calculator AtheroEdge Composite Risk Score (AECRS1.0), designed by fusing CCVRF with ultrasound image-based phenotypes. Ten-year risk was computed using the Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score. AECRS1.0 was computed by measuring the 10-year five carotid phenotypes such as IMT (ave., max., min.), IMT variability, and total plaque area (TPA) by fusing eight CCVRFs and then compositing them. AECRS1.0 was then benchmarked against the five conventional cardiovascular risk calculators by computing the receiver operating characteristics (ROC) and area under curve (AUC) values with a 95% CI. Two hundred four IRB-approved Japanese patients' left/right common carotid arteries (407 ultrasound scans) were collected with a mean age of 69 ± 11 years. The calculators gave the following AUC: FRS, 0.615; UKPDS56, 0.576; UKPDS60, 0.580; RRS, 0.590; PCRS, 0.613; and AECRS1.0, 0.990. When fusing CCVRF, TPA reported the highest AUC of 0.81. The patients were risk-stratified into low, moderate, and high risk using the standardized thresholds. The AECRS1.0 demonstrated the best performance on a Japanese diabetes cohort when compared with five conventional calculators. Graphical abstract AECRS1.0: Carotid ultrasound image phenotype-based 10-year cardiovascular risk calculator. The figure provides brief overview of the proposed carotid image phenotype-based 10-year cardiovascular risk calculator called AECRS1.0. AECRS1.0 was also benchmarked against five conventional cardiovascular risk calculators (Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score).

Chronic venous disease (CVD) is widespread, underdiagnosed, and can progress to chronic venous insufficiency and venous ulcers, which can require extensive treatment and hospitalization. These conditions negatively impact patient quality of life and place substantial burdens on healthcare resources. The two main risk factors for CVD are age and obesity. Thus, with the growing prevalence of obesity and the increasing longevity of the population, the burden of CVD is expected to increase dramatically in the coming decades. Appropriate lifestyle changes and care, which may include treatment with venoactive drugs, can slow disease progression, improve quality of life, and are likely to reduce healthcare costs. Physicians should be aware of this growing problem and of the effective treatments available for CVD. We recommend the accompanying short summaries from a symposium held at the recent European Venous Forum as a means for our colleagues to learn more about the burden and suffering associated with CVD. Funding: Servier.

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.

Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor–based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators. In recent years, a link between carotid atherosclerosis and rheumatoid arthritis has been widely discussed by multiple studies. Imaging the carotid artery using 2-D ultrasound is a noninvasive, economic, and efficient imaging approach that provides an atherosclerotic plaque tissue–specific image. Such images can help to morphologically characterize the plaque type and accurately measure vital phenotypes such as media wall thickness and wall variability. Intelligence-based paradigms such as machine learning– and deep learning–based techniques not only automate the risk characterization process but also provide an accurate CV risk stratification for better management of RA patients. This review provides a brief understanding of the pathogenesis of RA and its association with carotid atherosclerosis imaged using the B-mode ultrasound technique. Lacunas in traditional risk scores and the role of machine learning–based tissue characterization algorithms are discussed and could facilitate cardiovascular risk assessment in RA patients. The key takeaway points from this review are the following: (i) inflammation is a common link between RA and atherosclerotic plaque buildup, (ii) carotid ultrasound is a better choice to characterize the atherosclerotic plaque tissues in RA patients, and (iii) intelligence-based paradigms are useful for accurate tissue characterization and risk stratification of RA patients.

Monitoring of cerebrovascular diseases via carotid ultrasound has started to become a routine. The measurement of image-based lumen diameter (LD) or inter-adventitial diameter (IAD) is a promising approach for quantification of the degree of stenosis. The manual measurements of LD/IAD are not reliable, subjective and slow. The curvature associated with the vessels along with non-uniformity in the plaque growth poses further challenges. This study uses a novel and generalized approach for automated LD and IAD measurement based on a combination of spatial transformation and scale-space. In this iterative procedure, the scale-space is first used to get the lumen axis which is then used with spatial image transformation paradigm to get a transformed image. The scale-space is then reapplied to retrieve the lumen region and boundary in the transformed framework. Then, inverse transformation is applied to display the results in original image framework. Two hundred and two patients’ left and right common carotid artery (404 carotid images) B-mode ultrasound images were retrospectively analyzed. The validation of our algorithm has done against the two manual expert tracings. The coefficient of correlation between the two manual tracings for LD was 0.98 (p

We propose an approach for providing well-calibrated confidence measures for determining cerebrovascular risk stratification based on characteristics from noninvasive ultrasound imaging of carotid plaques. An important challenge we address is the class imbalance problem inherent in the particular task. The proposed approach is based on a novel framework, called conformal prediction (CP), for developing techniques that output sets of predictions guaranteed to contain the true classification of a new case with a prespecified probability. We follow a modified version of the CP framework, called Label-conditional Mondrian conformal prediction (LCMCP), so that the guarantee provided by CP does not only hold for all instances together, but also hold for the instances of each class independently, thus making prediction sets unbiased. Furthermore, LCMCP is combined with an underbagging ensemble of artificial neural networks so that its outputs are based on unbiased estimates. The important positive properties of the proposed approach are demonstrated experimentally on a dataset of patients that were followed up for eight years and had asymptomatic internal carotid artery stenosis at the baseline.

To summarize and critically evaluate the variety of risk stratification methods for VTE prophylaxis in non-orthopaedic surgical patients. A literature search was made using the Medline/Pubmed database. Current approaches to the stratification of patients into defined risk categories include a. Risk Assessment Models (RAMs), b. use of scoring systems and c. use of operation type. A wealth of data supports their use, although some have not undergone rigorous assessment, including external validation. An incremental risk increase in patients with a higher score supports the use of scoring systems, but similar findings have also been reported for RAMs. More complex systems have been developed in an effort to reclassify patients previously thought to be at low risk, and not in need of specific or complex thromboprophylaxis regimens. Similarly, many patients previously thought to be at moderate risk are reclassified to the high risk group. The increase in VTE risk among patients with a very high risk score calls for research into methods that can effectively reduce this, not only during hospitalization or at 30 days, but for the entire three-month hazard period. Equally warranted is the head to head comparison of the various risk stratification methods, including ease of use and VTE reduction efficacy.

Functional and structural changes in the common carotid artery are biomarkers for cardiovascular risk. Current methods for measuring functional changes include pulse wave velocity, compliance, distensibility, strain, stress, stiffness, and elasticity derived from arterial waveforms. The review is focused on the ultrasound-based carotid artery elasticity and stiffness measurements covering the physics of elasticity and linking it to biological evolution of arterial stiffness. The paper also presents evolution of plaque with a focus on the pathophysiologic cascade leading to arterial hardening. Using the concept of strain, and image-based elasticity, the paper then reviews the lumen diameter and carotid intima-media thickness measurements in combined temporal and spatial domains. Finally, the review presents the factors which influence the understanding of atherosclerotic disease formation and cardiovascular risk including arterial stiffness, tissue morphological characteristics, and image-based elasticity measurement.

Embedding of diagnostic and health care information requires secure encryption and watermarking. This research paper presents a comprehensive study for the behavior of some well established watermarking algorithms in frequency domain for the preservation of stroke-based diagnostic parameters. Two different sets of watermarking algorithms namely: two correlation-based (binary logo hiding) and two singular value decomposition (SVD)-based (gray logo hiding) watermarking algorithms are used for embedding ownership logo. The diagnostic parameters in atherosclerotic plaque ultrasound video are namely: (a) bulb identification and recognition which consists of identifying the bulb edge points in far and near carotid walls; (b) carotid bulb diameter; and (c) carotid lumen thickness all along the carotid artery. The tested data set consists of carotid atherosclerotic movies taken under IRB protocol from University of Indiana Hospital, USA-AtheroPoint™ (Roseville, CA, USA) joint pilot study. ROC (receiver operating characteristic) analysis was performed on the bulb detection process that showed an accuracy and sensitivity of 100 % each, respectively. The diagnostic preservation (DPsystem) for SVD-based approach was above 99 % with PSNR (Peak signal-to-noise ratio) above 41, ensuring the retention of diagnostic parameter devalorization as an effect of watermarking. Thus, the fully automated proposed system proved to be an efficient method for watermarking the atherosclerotic ultrasound video for stroke application.

Quantitative assessment of calcified atherosclerotic volume within the coronary artery wall is vital for cardiac interventional procedures. The goal of this study is to automatically measure the calcium volume, given the borders of coronary vessel wall for all the frames of the intravascular ultrasound (IVUS) video. Three soft computing fuzzy classification techniques were adapted namely Fuzzy c-Means (FCM), K-means, and Hidden Markov Random Field (HMRF) for automated segmentation of calcium regions and volume computation. These methods were benchmarked against previously developed threshold-based method. IVUS image data sets (around 30,600 IVUS frames) from 15 patients were collected using 40 MHz IVUS catheter (Atlantis® SR Pro, Boston Scientific®, pullback speed of 0.5 mm/s). Calcium mean volume for FCM, K-means, HMRF and threshold-based method were 37.84 ± 17.38 mm(3), 27.79 ± 10.94 mm(3), 46.44 ± 19.13 mm(3) and 35.92 ± 16.44 mm(3) respectively. Cross-correlation, Jaccard Index and Dice Similarity were highest between FCM and threshold-based method: 0.99, 0.92 ± 0.02 and 0.95 + 0.02 respectively. Student's t-test, z-test and Wilcoxon-test are also performed to demonstrate consistency, reliability and accuracy of the results. Given the vessel wall region, the system reliably and automatically measures the calcium volume in IVUS videos. Further, we validated our system against a trained expert using scoring: K-means showed the best performance with an accuracy of 92.80%. Out procedure and protocol is along the line with method previously published clinically.

Intermittent pneumatic leg compression (IPC) increases arterial calf inflow and foot skin blood flux in normal subjects and claudicants. Our hypothesis was that IPC could enhance foot skin blood flux after infrainguinal grafting and thus promote distal perfusion in limbs with tissue loss. The aim of this study was to compare the effects of three IPC modes [applied to the foot (IPCfoot), the calf (IPCcalf), or both (IPCfoot+calf)] on foot skin perfusion in healthy individuals, claudicants, and patients after infrainguinal arterial revascularization performed for critical or subcritical limb ischemia. Altogether, 20 healthy limbs, 22 claudicating limbs, and 36 limbs of arteriopaths with prior successful autologous femoropopliteal and femorodistal (18 each) grafts were examined. Five-minute laser Doppler recordings were obtained from the pulp of the big toe in the sitting position, at rest, and during random applications of IPCfoot, IPCcalf, and IPCfoot+calf delivered at 120 mmHg for 4 seconds three times per minute. Foot skin blood flux increased using all IPC modes (p0.001), with IPCfoot and IPCfoot+calf generating higher flux levels than IPCcalf (p0.01) in all groups. Intergroup differences of flux with each of the three IPC modes were not significant. IPCfoot and IPCfoot+calf similarly (p0.14) produced a higher percentage flux increase than IPCcalf in all groups (p0.004). Controls had a higher percentage flux increase with both IPCcalf and IPCfoot than did claudicants (p? 0.016). No differences were documented between normal and grafted limbs (p0.05). The percentage flux increase with IPCfoot+calf and IPCcalf was significantly higher in femorodistal grafts than in femoropopliteal ones (p ? 0.026). IPC enhances skin blood flux in limbs with infrainguinal bypass, claudication, and normal arteries, with IPCfoot and IPCfoot+calf being more effective than IPCcalf. Our findings suggest that IPC may be beneficial in limbs with impaired distal perfusion and thus may have clinical implications in the treatment of leg ulcers either prior to or after revascularization.

This article describes the technique of measuring ambulatory venous pressures and their value in the diagnosis and assessment of the severity of venous hypertension as well as their contribution toward our understanding of venous hemodynamics and the pathophysiology of venous disorders. It demonstrates that ambulatory venous pressures are not only of diagnostic significance, but also of prognostic significance. Competent popliteal valves will protect the leg from venous hypertension despite proximal (iliofemoral) occlusion. Although the measurement of ambulatory venous pressures is the “gold standard,” it is invasive and cannot be repeated frequently nor can it be used as a screening test. It has been employed in the validation of a number of noninvasive tests now used for the routine screening of patients. Nevertheless, ambulatory venous pressure measurements are essential if the beneficial hemodynamic effect of surgical reconstructive procedures is to be convincingly demonstrated.