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© 2022 Elsevier B.V. All rights reserved., Jolobe supports that screening for asymptomatic carotid stenosis (AsxCS) should be extended to patients with non-valvular atrial fibrillation (NVAF). Indeed, a 2021 meta-analysis (n = 20 studies; 49,070 patients) demonstrated that the pooled prevalence of >50% AsxCS inpatients with NVAF was 12.4% (range: 4.4–24.3%). Although this issue was not discussed in our manuscript, the optimal management of patients presenting with a recent transient ischemic attack [TIA]/ischemic stroke with an ipsilateral 50–99% carotid stenosis and concomitant NVAF was addressed by the 2023 European Society for Vascular Surgery (ESVS) carotid guidelines. It was recognized that although no guidelines have provided recommendations regarding the management of these patients, a pragmatic approach is warranted.

BACKGROUND: It is generally assumed by practitioners and guideline authors that combined modalities (methods of treatment) are more effective than single modalities in preventing venous thromboembolism (VTE), defined as deep vein thrombosis (DVT) or pulmonary embolism (PE), or both. This is the second update of the review first published in 2008. OBJECTIVES: The aim of this review was to assess the efficacy of combined intermittent pneumatic leg compression (IPC) and pharmacological prophylaxis compared to single modalities in preventing VTE. SEARCH METHODS: The Cochrane Vascular Information Specialist searched the Cochrane Vascular Specialised Register, CENTRAL, MEDLINE, Embase, CINAHL, and AMED databases, and World Health Organization International Clinical Trials Registry Platform and ClinicalTrials.gov trials registers to 18 January 2021. We searched the reference lists of relevant articles for additional studies. SELECTION CRITERIA: We included randomised controlled trials (RCTs) or controlled clinical trials (CCTs) of combined IPC and pharmacological interventions used to prevent VTE compared to either intervention individually. DATA COLLECTION AND ANALYSIS: We independently selected studies, applied Cochrane's risk of bias tool, and extracted data. We resolved disagreements by discussion. We performed fixed‐effect model meta‐analyses with odds ratios (ORs) and 95% confidence intervals (CIs). We used a random‐effects model when there was heterogeneity. We assessed the certainty of the evidence using GRADE. The outcomes of interest were PE, DVT, bleeding and major bleeding. MAIN RESULTS: We included a total of 34 studies involving 14,931 participants, mainly undergoing surgery or admitted with trauma. Twenty‐five studies were RCTs (12,672 participants) and nine were CCTs (2259 participants). Overall, the risk of bias was mostly unclear or high. We used GRADE to assess the certainty of the evidence and this was downgraded due to the risk of bias, imprecision or indirectness. The addition of pharmacological prophylaxis to IPC compared with IPC alone reduced the incidence of symptomatic PE from 1.34% (34/2530) in the IPC group to 0.65% (19/2932) in the combined group (OR 0.51, 95% CI 0.29 to 0.91; 19 studies, 5462 participants, low‐certainty evidence). The incidence of DVT was 3.81% in the IPC group and 2.03% in the combined group showing a reduced incidence of DVT in favour of the combined group (OR 0.51, 95% CI 0.36 to 0.72; 18 studies, 5394 participants, low‐certainty evidence). The addition of pharmacological prophylaxis to IPC, however, increased the risk of any bleeding compared to IPC alone: 0.95% (22/2304) in the IPC group and 5.88% (137/2330) in the combined group (OR 6.02, 95% CI 3.88 to 9.35; 13 studies, 4634 participants, very low‐certainty evidence). Major bleeding followed a similar pattern: 0.34% (7/2054) in the IPC group compared to 2.21% (46/2079) in the combined group (OR 5.77, 95% CI 2.81 to 11.83; 12 studies, 4133 participants, very low‐certainty evidence). Tests for subgroup differences between orthopaedic and non‐orthopaedic surgery participants were not possible for PE incidence as no PE events were reported in the orthopaedic subgroup. No difference was detected between orthopaedic and non‐orthopaedic surgery participants for DVT incidence (test for subgroup difference P = 0.19). The use of combined IPC and pharmacological prophylaxis modalities compared with pharmacological prophylaxis alone reduced the incidence of PE from 1.84% (61/3318) in the pharmacological prophylaxis group to 0.91% (31/3419) in the combined group (OR 0.46, 95% CI 0.30 to 0.71; 15 studies, 6737 participants, low‐certainty evidence). The incidence of DVT was 9.28% (288/3105) in the pharmacological prophylaxis group and 5.48% (167/3046) in the combined group (OR 0.38, 95% CI 0.21 to 0.70; 17 studies; 6151 participants, high‐certainty evidence). Increased bleeding side effects were not observed for IPC when it was added to anticoagulation (any bleeding: OR 0.87, 95% CI 0.56 to 1.35, 6 studies, 1314 participants, very low‐certainty evidence; major bleeding: OR 1.21, 95% CI 0.35 to 4.18, 5 studies, 908 participants, very low‐certainty evidence). No difference was detected between the orthopaedic and non‐orthopaedic surgery participants for PE incidence (test for subgroup difference P = 0.82) or for DVT incidence (test for subgroup difference P = 0.69). AUTHORS' CONCLUSIONS: Evidence suggests that combining IPC with pharmacological prophylaxis, compared to IPC alone reduces the incidence of both PE and DVT (low‐certainty evidence). Combining IPC with pharmacological prophylaxis, compared to pharmacological prophylaxis alone, reduces the incidence of both PE (low‐certainty evidence) and DVT (high‐certainty evidence). We downgraded due to risk of bias in study methodology and imprecision. Very low‐certainty evidence suggests that the addition of pharmacological prophylaxis to IPC increased the risk of bleeding compared to IPC alone, a side effect not observed when IPC is added to pharmacological prophylaxis (very low‐certainty evidence), as expected for a physical method of thromboprophylaxis. The certainty of the evidence for bleeding was downgraded to very low due to risk of bias in study methodology, imprecision and indirectness. The results of this update agree with current guideline recommendations, which support the use of combined modalities in hospitalised people (limited to those with trauma or undergoing surgery) at risk of developing VTE. More studies on the role of combined modalities in VTE prevention are needed to provide evidence for specific patient groups and to increase our certainty in the evidence.

SCORE2 and SCORE2-OP algorithms and associated online calculators provide a new and easy method of estimating the 10-year cardiovascular risk in apparently healthy Europeans. The aim of the study was to determine the performance of these algorithms in terms of discrimination and calibration in the cohort of the Cyprus Epidemiological Study on Atherosclerosis (CESA), not only for the 10-year risk for myocardial infarction (MI), stroke and cardiovascular death, but also for all types of atherosclerotic cardiovascular events (ASCVE).SCORE2 and SCORE2-OP for low-risk regions were calculated in a non-diabetic subset of CESA consisting of 908 people (mean age±SD: 57.8±10.5; range 40-89; 58.8% female) using baseline risk factors. Mean follow-up was 13.2±3.7 years (range 1-17) with 89 primary endpoints (MI, stroke and cardiovascular death) and 136 secondary endpoints (primary endpoints, angina, cardiac failure, coronary revascularisation, transient ischaemic attack, claudication and critical limb ischaemia).The C-statistic for the prediction of the primary endpoint for all ages was 0.76 (95% CI 0.70 to 0.81) and the observed 10-year event rate was similar to the predicted one. However, the observed 10-year rate for secondary events was similar to the estimated one only when the algorithm for highrisk regions was used.SCORE2 and SCORE2-OP moderate risk algorithms performs well in the Cypriot population for predicting the 10-year risk for MI, stroke and fatal cardiovascular disease. However, an estimate of the 10-year risk for all ASCVD events is best calculated from the high-risk algorithm.

Recent studies have suggested that textural characteristics of the intima-media complex (IMC) may be more useful than the intima-media thickness (IMT) in evaluating cardiovascular risk. The primary aim of our study was to investigate the association between texture features of the common carotid IMC and prevalent clinical cardiovascular disease (CVD). The secondary aim was to determine whether IMT and IMC texture features vary between the left and right carotid arteries. The study was performed on 2208 longitudinal-section ultrasound images of the left (L) and right (R) common carotid artery (CCA), acquired from 569 men and 535 women out of which 125 had clinical CVD. L and R sides of the IMC were intensity normalized and despeckled. The IMC was semiautomatically delineated for all images using a semiautomated segmentation system, and 61 different texture features were extracted. The corresponding IMT semiautomated measurements (mean±SD) of the L and R sides were 0.73±0.21 mm/0.69±0.19 mm for the normal population and 0.83±0.17 mm/0.79±0.18 mm for those with CVD. IMC texture features did not differ between the right- and left-hand sides. Several texture features were independent predictors of the presence of CVD. The multivariate logistic regression analysis combining age, IMT, and texture features produced a receiver operating characteristic curve with an area under the curve of 89%. A correct classification rate of 77% for separating the normal subject (NOR) versus CVD subjects was achieved using the support vector machine classifier with a combination of clinical features, IMT, and extracted texture features. Texture features provide additional information on the presence of clinical CVD, which is over and above that provided by conventional risk factors or IMT alone. The value of IMC texture features in the prediction of future cardiovascular events should be tested in prospective studies.

© 2022 Elsevier B.V. All rights reserved., ackground: Current guidelines do not recommend screening for asymptomatic carotid artery stenosis (AsxCS). The rationale behind this recommendation is that detection of AsxCS may lead to an unnecessary carotid intervention. In contrast, screening for abdominal aortic aneurysms is strongly recommended. Methods: A critical analysis of the literature was performed to evaluate the implications of detecting AsxCS. Results: Patients with AsxCS are at high risk for future stroke, myocardial infarction and vascular death. Population-wide screening for AsxCS should not be recommended. Additionally, screening of high-risk individuals for AsxCS with the purpose of identifying candidates for a carotid intervention is inappropriate. Instead, selective screening for AsxCS should be considered and should be viewed as an opportunity to identify individuals at high risk for atherosclerotic cardiovascular disease and future cardiovascular events for the timely initiation of intensive medical therapy and risk factor modification. Conclusions: Although mass screening should not be recommended, there are several arguments suggesting that selective screening for AsxCS should be considered. The rationale supporting such selective screening is to optimize risk factor control and to initiate intensive medical therapy for prevention of future cardiovascular events, rather than to identify candidates for an intervention.

International guidelines strongly recommend statins alone or in combination with other lipid-lowering agents to lower low-density lipoprotein cholesterol (LDL-C) levels for patients with asymptomatic/symptomatic carotid stenosis (AsxCS/SCS). Lowering LDL-C levels is associated with significant reductions in transient ischemic attack, stroke, cardiovascular (CV) event and death rates. The aim of this multi-disciplinary overview is to summarize the benefits and risks associated with lowering LDL-C with statins or non-statin medications for Asx/SCS patients. The cerebrovascular and CV beneficial effects associated with statins, proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors and other non-statin lipid-lowering agents (e.g. fibrates, ezetimibe) are reviewed. The use of statins and PCSK9 inhibitors is associated with several beneficial effects for Asx/SCS patients, including carotid plaque stabilization and reduction of stroke rates. Ezetimibe and fibrates are associated with smaller reductions in stroke rates. The side-effects resulting from statin and PCSK9 inhibitor use are also highlighted. The benefits associated with lowering LDL-C with statins or non-statin lipid lowering agents (e.g. PCSK9 inhibitors) outweigh the risks and potential side-effects. Irrespective of their LDL-C levels, all Asx/SCS patients should receive high-dose statin treatment±ezetimibe or PCSK9 inhibitors for reduction not only of LDL-C levels, but also of stroke, cardiovascular mortality and coronary event rates.

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.

European Society for Vascular Surgery (ESVS) 2021 Clinical Practice Guidelines on the Management of Venous Thrombosis

© The Author(s) 2022. Sage Publications, Despite the publication of several national/international guidelines, the optimal management of patients with asymptomatic carotid stenosis (AsxCS) remains controversial. This article compares 3 recently released guidelines (the 2020 German-Austrian, the 2021 European Stroke Organization [ESO], and the 2021 Society for Vascular Surgery [SVS] guidelines) vs the 2017 European Society for Vascular Surgery (ESVS) guidelines regarding the optimal management of AsxCS patients.The 2017 ESVS guidelines defined specific imaging/clinical parameters that may identify patient subgroups at high future stroke risk and recommended that carotid endarterectomy (CEA) should or carotid artery stenting (CAS) may be considered for these individuals. The 2020 German-Austrian guidelines provided similar recommendations with the 2017 ESVS Guidelines. The 2021 ESO Guidelines also recommended CEA for AsxCS patients at high risk for stroke on best medical treatment (BMT), but recommended against routine use of CAS in these patients. Finally, the SVS guidelines provided a strong recommendation for CEA+BMT vs BMT alone for low-surgical risk patients with >70% AsxCS. Thus, the ESVS, German-Austrian, and ESO guidelines concurred that all AsxCS patients should receive risk factor modification and BMT, but CEA should or CAS may also be considered for certain AsxCS patient subgroups at high risk for future ipsilateral ischemic stroke.

The death due to stroke is caused by embolism of the arteries which is due to the rupture of the atherosclerotic lesions in carotid arteries. The lesion formation is over time, and thus, early screening is recommended for asymptomatic and moderate-risk patients. The previous techniques adopted conventional methods or semi-automated and, more recently, machine learning solutions. A handful of studies have emerged based on solo deep learning (SDL) models such as UNet architecture.The proposed research is the first to adopt hybrid deep learning (HDL) artificial intelligence models such as SegNet-UNet. This model is benchmarked against UNet and advanced conventional models using scale-space such as AtheroEdge 2.0 (AtheroPoint, CA, USA). All our resultant statistics of the three systems were in the order of UNet, SegNet-UNet, and AtheroEdge 2.0.Using the database of 379 ultrasound scans from a Japanese cohort of 190 patients having moderate risk and implementing the cross-validation deep learning framework, our system performance using area-under-the-curve (AUC) for UNet, SegNet-UNet, and AtheroEdge 2.0 were 0.93, 0.94, and 0.95 (P0.001), respectively. The coefficient of correlation between the three systems and ground truth (GT) were: 0.82, 0.89, and 0.85 (P0.001 for all three), respectively. The mean absolute area error for the three systems against manual GT was 4.07±4.70 mmThe proposed study demonstrates a fast, accurate, and reliable solution for early detection and quantification of plaque lesions in common carotid artery ultrasound scans. The system runs on a test US image in1 second, proving overall performance to be clinically reliable.

After 3 decades of endovascular aneurysm repair (EVAR) for abdominal aortic aneurysms (AAA), the infrarenal neck is considered the most important determining factor for an uncomplicated and sustainable outcome. As the envelope has been pushed toward treating more challenging infrarenal necks with standard endografts, there are numerous publications regarding hostile aortic neck criteria.1–4 During preoperative planning and sizing, aortic neck length, diameter, suprarenal and infrarenal angulation, shape, and occurrence of calcium and thrombus are measured by most endovascular specialists, using dedicated software. Moreover, all endograft manufacturers have defined specific instructions for use (IFU) concerning infrarenal neck characteristics. Infrarenal neck length seems to be one of the most important criteria to consider, with a minimum of 10 or 15 mm, according to the IFU of most commercially available ndografts.3 According to most CoreLab definitions, the infrarenal neck ends when the aortic diameter increases >10% compared with baseline (ie, the diameter just below the lower margin of the lowest renal artery).5,6 Although the determination of the pre-EVAR neck characteristics gives the treating physicians a handle in the sizing and planning process, it does not always match with the actual circumferential seal of the endograft in the aortic neck after deployment. Oversizing the endograft’s main body often extends the seal compared with the predefined aortic neck length. However, especially in hostile necks, this does not always have the anticipated and desired effect.7 It seems reasonable to assume that the post-EVAR achieved circumferential apposition between the endograft and the aortic wall is a better indicator for outcome than the pre-EVAR determined aortic neck characteristics alone. The so-called sealing zone in the infrarenal aortic neck has received less attention in EVAR literature so far. A possible explanation for this might be that it is harder to define than the well-known aortic neck criteria. It also depends on the positioning of the endograft during the procedure. Moreover, the circumferential apposition between the endograft and the aortic wall has to be determined on the post- EVAR computed tomography (CT) scan, which is not a standard measurement so far.8 A Delphi method is often used to orchestrate expert opinions systematically when evidence is scarce or lacking, and research questions cannot simply be studied with experimental and epidemiological methods.9 In this study, the Delphi method is used to propose a consensus definition of the infrarenal sealing zone. Furthermore, it provides an algorithm to determine when and if adjunctive procedure(s) or reintervention should be considered in case of potential proximal sealing failure of the endograft.

The coronavirus disease 2019 (COVID-19) global pandemic has resulted in diversion of healthcare resources to the management of patients infected with SARS-CoV-2 virus. Elective interventions and surgical procedures in most countries have been postponed and operating room resources have been diverted to manage the pandemic. The Venous and Lymphatic Triage and Acuity Scale was developed to provide an international standard to rationalise and harmonise the management of patients with venous and lymphatic disorders or vascular anomalies. Triage urgency was determined based on clinical assessment of urgency with which a patient would require medical treatment or surgical intervention. Clinical conditions were classified into six categories of: (1) venous thromboembolism (VTE), (2) chronic venous disease, (3) vascular anomalies, (4) venous trauma, (5) venous compression and (6) lymphatic disease. Triage urgency was categorised into four groups and individual conditions were allocated to each class of triage. These included (1) medical emergencies (requiring immediate attendance), example massive pulmonary embolism; (2) urgent (to be seen as soon as possible), example deep vein thrombosis; (3) semi-urgent (to be attended to within 30-90 days), example highly symptomatic chronic venous disease, and (4) discretionary/non-urgent- (to be seen within 6-12 months), example chronic lymphoedema. Venous and Lymphatic Triage and Acuity Scale aims to standardise the triage of patients with venous and lymphatic disease or vascular anomalies by providing an international consensus-based classification of clinical categories and triage urgency. The scale may be used during pandemics such as the current COVID-19 crisis but may also be used as a general framework to classify urgency of the listed conditions.

Deep Learning-Based Segmentation of the Atherosclerotic Carotid Plaque in Ultrasonic Images, vol. 652 IFIP, pp. 187 - 198 Early stroke risk stratification in individuals with carotid atherosclerosis is of great importance, especially in high-risk asymptomatic (AS) cases. In this study, we present a new computer-aided diagnostic (CAD) system for the automated segmentation of the atherosclerotic plaque in carotid ultrasound (US) images and the extraction of a refined set of ultrasonic features to robustly characterize plaques in carotid US images and videos (AS vs symptomatic (SY)). So far, we trained a UNet model (16 to 256 neurons in the contracting path; the reverse, for the expanding path), starting from a dataset of 201 (AS = 109 and SY = 92) carotid US videos of atherosclerotic plaques, from which their first frames were extracted to prepare three subsets, a training, an internal validation, and final evaluation set, with 150, 30 and 15 images, respectively. The automated segmentations were evaluated based on manual segmentations, performed by a vascular surgeon. To assess our model’s capacity to segment plaques in previously unseen images, we calculated 4 evaluation metrics (mean ± std). The evaluation of the proposed model yielded a 0.736 ± 0.10 Dice similarity score (DSC), a 0.583 ± 0.12 intersection of union (IoU), a 0.728 ± 0.10 Cohen’s Kappa coefficient (KI) and a 0.65 ± 0.19 Hausdorff distance. The proposed segmentation workflow will be further optimized and evaluated, using a larger dataset and more neurons in each UNet layer, as in the original model architecture. Our results are close to others published in relevant studies.

Carotid distensibility is an indicator of carotid elasticity, which can be used as a cardiovascular disease risk factor. The objective of this study was to investigate how the degree of stenosis influences the strain characteristics of wall and plaque in the internal carotid artery (ICA). Ultrasound videos of the ICA from 83 subjects with atherosclerotic plaques at the origin of the ICA producing variable degrees of stenosis from 10 % to 85 % were investigated. We estimate for each video a carotid diameter during contraction (CDC), a carotid diameter during distension (CDD) in mm and a % of carotid plaque distension (%CPD). We have also estimated the radial strain at wall and plaque (%RSw, %RSp), the longitudinal strain at wall (%LSw) and the shear strain at wall (%SSw) and plaque (%SSp) respectively. The main findings were the following: 1) The %CPD is linearly related to the percentage of ICA stenosis decreasing with an increase in stenosis. 2) There is a statistical significant difference between the % of ICA plaque distensibility of the AS group vs the SY group. 3) There is a statistical significant difference for the RSw, and LSw and %SSw of ICA wall of the AS group vs the SY group. 4) There is a statistical significant difference for the %SSp and %RSp between the AS group vs the SY group. The degree of the ICA stenosis is associated with strain metrics of the carotid wall and plaque. Future work will investigate the proposed methodology on more subjects.

Objective To assess the clinical efficacy of sulodexide, including a comparison with venoactive drugs (VAD) (micronized purified flavonoid fraction, MPFF; hydroxy-ethyl-rutosides, HR; calcium dobesilate;Ruscus extract combined with hesperidin methyl chalcone and vitamin C, Ruscus+HMC+VitC; horse chestnut seed extract, HCSE) and pentoxifylline in patients with chronic venous disease. Methods We performed a literature search in MEDLINE, Embase, and Cochrane Library for randomized controlled trials (RCTs) and observational studies. Proportion of patients with complete venous ulcer healing was the primary outcome and lower leg volume, foot volume, ankle circumference and symptoms were the secondary outcomes. Bayesian network meta-analysis (NMA) was perfomed with random effects models using only RCTs. A meta-analysis of observational studies was performed for sulodexide because no RCT could be included in NMA for symptoms or signs. Results Forty-five RCTs and eighteen observational studies were identified. Sulodexide was included only in a single NMA for the proportion of patients with complete ulcer healing and it showed to have the highest probability of being the best treatment (48%) compared with pentoxifylline (37%) and MPFF (16%). MPFF was the most effective treatment in reducing lower leg volume, CIVIQ-20 score and pain VAS scale while calcium dobesilate and Ruscus+HMC+VitC were the most effective in reducing foot volume and ankle circumference respectively. Meta-analyses of observational studies for sulodexide showed that it improves significantly the scoring of pain, feeling of swelling, heaviness and parasthesiae measured by Likert scales. Conclusions Sulodexide is at least as effective as pentoxifylline and more effective than MPFF in improving the rate of ulcer healing in patients with CVD. VADs are effective in improving venous symptoms and signs, as was also shown by sulodexide in the meta-analysis of observational studies. The relative effectiveness of sulodexide and VADs needs to be evaluated by an RCT in order to better inform clinical practice.

The advent of Deep Learning (DL) is poised to dramatically change the delivery of healthcare in the near future. Not only has DL profoundly affected the healthcare industry it has also influenced global businesses. Within a span of very few years, advances such as self-driving cars, robots performing jobs that are hazardous to human, and chat bots talking with human operators have proved that DL has already made large impact on our lives. The open source nature of DL and decreasing prices of computer hardware will further propel such changes. In healthcare, the potential is immense due to the need to automate the processes and evolve error free paradigms. The sheer quantum of DL publications in healthcare has surpassed other domains growing at a very fast pace, particular in radiology. It is therefore imperative for the radiologists to learn about DL and how it differs from other approaches of Artificial Intelligence (AI). The next generation of radiology will see a significant role of DL and will likely serve as the base for augmented radiology (AR). Better clinical judgement by AR will help in improving the quality of life and help in life saving decisions, while lowering healthcare costs. A comprehensive review of DL as well as its implications upon the healthcare is presented in this review. We had analysed 150 articles of DL in healthcare domain from PubMed, Google Scholar, and IEEE EXPLORE focused in medical imagery only. We have further examined the ethic, moral and legal issues surrounding the use of DL in medical imaging.

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.

MOTIVATION This study presents a novel nonlinear model which can predict 10-year carotid ultrasound image-based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low-density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima-media thickness (IMT), wall variability, and total plaque area). METHODOLOGY Two-step process was adapted: First, five baseline carotid image-based phenotypes were automatically measured using AtheroEdge™ (AtheroPoint™ , CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10-year predictions of carotid phenotypes. RESULTS Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10-year carotid phenotypes. Mean correlation coefficient (CC) between 10-year carotid image-based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10-year measurements of five phenotypes IMTave10yr , IMTmax10yr , IMTmin10yr , IMTV10yr , and TPA10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter-operator variability between two operators showed significant CC (P

Currently, carotid intima-media thickness (cIMT) and geometric total plaque area (gTPA) are computed manually and thus are tedious and prone to interobserver and intraobserver variabilities. This study presents an intelligence-based automated deep learning (DL)–based technique for carotid wall interface detection, cIMT, and lumen diameter (LD) measurements, followed by a 3D cylindrical approach for TPA measurement. The observers were used for manual tracings of which were then used for the design of two DL-based systems. The DL boundaries for inner lumen wall and outer interadventitial borders were used for computing the cIMT and LD. Using cylindrical approach, we computed the gTPA. Furthermore, we compute the 10-year image-based cIMT and gTPA, using the progression rates. A total of 396 B-mode ultrasound right and left common carotid artery images were taken from 203 patients. The mean cIMT and gTPA using DL1 and DL2 is 0.91 mm, 20.52 mm2 and 0.88 mm, 19.44 mm2, respectively. The coefficient of correlation between gTPA and cIMT using DL1 and DL2 is 0.92 ( P < .001) and 0.94 ( P < .001), respectively. The area under the curve (AUC) for gTPA showed an improvement over cIMT by 14.36% and 12.57% for DL1 and DL2, respectively. The corresponding 10-year risk improvements were 9.09% and 6.26%. Our statistical significance tests successfully passed t test, Mann-Whitney, Wilcoxon, Kolmogorov-Smirnov, and Friedman. The study shows gTPA as an equally powerful carotid risk biomarker like cIMT. Given the cIMT and LD, cylindrical fitting is a fast method for gTPA measurements.

In recent years, it has become apparent that the severity of an asymptomatic carotid stenosis is not sufficient to assess the risk of stroke. Although the risk of stroke increases with increasing grades of stenosis, and as a result a stenosis of ≥80% is used by many surgeons as an indication for surgery, this subgroup does not contain the majority of strokes that will subsequently occur. In addition, because the severity of stenosis cannot identify subgroups with stroke risk higher than 2.5%, a very large number of operations (approximately 90) with an asymptomatic stenosis of ≥80% would need to undergo carotid endarterectomy to prevent one stroke for one year of follow-up.

Disclaimer Due to the evolving field of medicine, new research may, in due course, modify the recommendations presented in this document. At the time of publication, every attempt has been made to ensure that the information provided is up to date and accurate. It is the responsibility of the treating physician to determine the best treatment for the patient. The authors, committee members, editors, and publishers cannot be held responsible for any legal issues that may arise from the citation of this statement. Rules of evidence Management of patients with chronic venous disorders has been traditionally undertaken subjectively among physicians, often resulting in less than optimal strategies. In this document, a systematic approach has been developed with recommendations based upon cumulative evidence from the literature. Levels of evidence and grades of recommendation range from Level I and Grade A to Level III and Grade C. Level I evidence and Grade A recommendations derive from scientifically sound randomized clinical trials in which the results are clear-cut. Level II evidence and Grade B recommendations derive from clinical studies in which the results among trials often point to inconsistencies. Level III evidence and Grade C recommendations result from poorly designed trials or from small case series.1, 2 Meta-analysis Meta-analyses are included in the present document but there should be caution as to their possible abuse. Certain studies may be included in a meta-analysis carelessly without sufficiently understanding of substantive issues, ignoring relevant variables, using heterogenous findings or interpreting results with a bias.3 It has been demonstrated that the outcomes of 12 large randomized controlled trials were not predicted accurately 35% of the time by the meta-analyses published previously on the same topics.4

Background Patients with SLE have 5-10-fold increased risk of developing CVD compared to age and sex-matched controls. The average age of developing a first CVD event in patients with SLE is only 49. In this study we aimed to describe the rate and determinants of carotid plaque progression in a cohort of SLE patients who were asymptomatic of CVD at baseline. Methods We carried out vascular ultrasound studies of 100 patients with SLE asymptomatic of CVD at baseline. Sixty-nine patients were rescanned over a median of 5 years of follow up. Ninety-four percent who were re-scanned were women and the mean overall age was 46 years (SD 11). Clinical and CVD risk was assessed at baseline and follow up. The same protocol for assessment at baseline using B-mode Doppler ultrasound to measure intimal media thickness and plaque was used at follow up to assess progression. We also assessed total plaque area (TPA), a more sensitive measure of plaque, and echolucency expressed as gray scale median (GSM) which is linked to plaque lipid content. Results Of the 100 patients with a baseline scan, 69 patients had a second scan at a median of 5 years follow up. New plaque developed in 9% and 26% had an increase in plaque number. The mean overall IMT (0.111 vs 0.064, p < 0.01) and common carotid IMT (0.065 vs 0.055, p < 0.01) were significantly raised in plaque vs non-plaque patients. In a multi -variable analysis CIMT at follow-up was independently associated with age (beta 0.415, p < 0.001) and diastolic blood pressure (beta 0.285, p < 0.021). Independent predictors of plaque at follow-up scan on multi-variable analysis were age at scan>52 years (OR 10.41, CI 2.66-40.80) and systolic BP > 133 (OR 5.26, CI 1.396 - 19.862). In contrast, total cholesterol was negatively correlated with TPA (beta = -1.167, p = 0.002) and with GSM (beta = -0.513, p = 0.012). Conclusion Amongst these 69 patients, 26% had increased plaque and none had decreased plaque over a median of five years follow-up. Measurement of novel ultrasound variables such as TPA and echolucency may identify more modifiable risk factors that can be used to improve CVD outcomes in patients with SLE. Disclosures J. Bakshi: None. M. Griffin: None. S. Croca: None. F. Farhina: None. D. Isenberg: None. A. Nicolaides: None. A. Rahman: None.

Background Cardiovascular disease is a significant burden on SLE patients, and no satisfactory markers exist to predict atherosclerotic development in patients. This study utilised an orthogonal approach to develop a marker of cardiovascular progression with predictive value in SLE patients. Methods The earliest available sample from a cohort of 92 SLE patients was tested for anti-Domain I (aDI), anti Beta-2-Glycoprotein-I, and anti-Cardiolipin antibodies. Persistent positivity was not assessed. These patients then had vascular ultrasound scans (carotid and femoral arteries) (mean 10 years later) to assess subclinical atherosclerotic plaque. A range of demographic, clinical and serological markers were recorded at the time of the scan. Predictors of plaque presence were investigated using binary logistic regression. Results A total of 34 patients from the cohort of 92 had atherosclerotic plaque (37%). A total of 32 patients had aDI positivity, of which 20 (62.5%) also had Plaque, this is reflected by the significantly higher levels of aDI antibodies seen in Plaque patients (p < 0.01). Anti-DI positivity was predictive of the development of plaque in the future (Odds Ratio (OR) 5.476, p < 0.001). No association was seen for any other antibody tested. Multiple binary logistic regression showed aDI positivity had as much predictive value as triglyceride levels on the day of the scan (OR 3.5 vs 3.9, Table 1) for predicting plaque in patients. Age at scan was a third independent variable associated with atherogenic plaque. Conclusion Early aDI positivity may be a good marker of atherogenesis in SLE patients in the long term. Disclosures T.C.R. McDonnell: Other; I am an inventor on the patent for Domain I. F. Farinha: None. C. Pericleous: Other; This Author is an Inventor on the Patent for Domain I. M. Griffin: None. A. Nicolaides: None. S. Croca: None. I. Giles: Other; This Author is an Inventor on the Domain I Patent. A. Rahman: Other; This Author is an Inventor on the Domain I Patent..

Background SLE patients have 5–10-fold increased risk of developing CVD compared to controls.1,2 In this study we aimed to describe the rate and determinants of carotid plaque progression in a cohort of SLE patients who were asymptomatic of CVD at baseline. Methods Vascular ultrasound studies of 100 patients with SLE asymptomatic of CVD was carried out at baseline. Sixty-nine patients were rescanned (94% female, mean overall age 46 years (SD 11)) over a median of 5 years of follow up. Clinical and CVD risk was assessed at baseline and follow up. B-mode Doppler ultrasound was used to measure intimal media thickness and plaque to assess progression. Total plaque area (TPA), a more sensitive measure of plaque, and echolucency expressed as gray scale median (GSM), linked to plaque lipid content were assessed. Results Of the 100 patients with a baseline scan, 69 patients had a second scan at a median of 5 years follow up. New plaque developed in 9% and 26% had an increase in plaque number. The mean overall IMT (0.111 vs 0.064, p 52 years (OR 10.41, CI 2.66–40.80) and systolic BP>133 (OR 5.26, CI 1.396 – 19.862). In contrast, total cholesterol was negatively correlated with TPA (beta =-1.167, p=0.002) and with GSM (beta =-0.513, p=0.012). Conclusions Amongst these 69 patients, 26% had progression and none had decreased plaque over a median of five years follow-up. Measurement of novel ultrasound variables such as TPA and echolucency may identify more modifiable risk factors that can be used to improve CVD outcomes in patients with SLE. Acknowledgements Rosetrees Trust References Bruce IN. ‘Not only...but also’: factors that contribute to accelerated atherosclerosis and premature coronary heart disease in systemic lupus erythematosus. Rheumatology(Oxford, England) 2005;44(12);1492–502. Manzi S, Meilahn EN, Rairie JE, et al. Age-specific incidence rates of myocardial infarction and angina in women with systemic lupus erythematosus: comparison with the Framingham Study. Am J Epidemiol 1997;145(5);408–15.

The coronavirus disease 2019 (COVID-19) global pandemic has resulted in diversion of healthcare resources to the management of patients infected with SARS-CoV-2 virus. Elective interventions and surgical procedures in most countries have been postponed and operating room resources have been diverted to manage the pandemic. The Venous and Lymphatic Triage and Acuity Scale was developed to provide an international standard to rationalise and harmonise the management of patients with venous and lymphatic disorders or vascular anomalies. Triage urgency was determined based on clinical assessment of urgency with which a patient would require medical treatment or surgical intervention. Clinical conditions were classified into six categories of: (1) venous thromboembolism (VTE), (2) chronic venous disease, (3) vascular anomalies, (4) venous trauma, (5) venous compression and (6) lymphatic disease. Triage urgency was categorised into four groups and individual conditions were allocated to each class of triage. These included (1) medical emergencies (requiring immediate attendance), example massive pulmonary embolism; (2) urgent (to be seen as soon as possible), example deep vein thrombosis; (3) semi-urgent (to be attended to within 30–90 days), example highly symptomatic chronic venous disease, and (4) discretionary/non-urgent- (to be seen within 6–12 months), example chronic lymphoedema. Venous and Lymphatic Triage and Acuity Scale aims to standardise the triage of patients with venous and lymphatic disease or vascular anomalies by providing an international consensus-based classification of clinical categories and triage urgency. The scale may be used during pandemics such as the current COVID-19 crisis but may also be used as a general framework to classify urgency of the listed conditions.

Motivation The early screening of cardiovascular diseases (CVD) can lead to effective treatment. Thus, accurate and reliable atherosclerotic carotid wall detection and plaque measurements are crucial. Current measurement methods are time- consuming and do not utilize the power of knowledge-based paradigms such as artificial intelligence (AI). We present an AI-based methodology for the joint automated detection and measurement of wall thickness and carotid plaque (CP) in the form of carotid intima-media thickness (cIMT) and total plaque area (TPA), a class of AtheroEdge™ system (AtheroPoint™, CA, USA). Method The novel system consists of two stages, and each stage comprises an independent deep learning (DL) model. In Stage I, the first DL model segregates the common carotid artery (CCA) patches from ultrasound (US) images into the rectangular wall and non-wall patches. The characterized wall patches are integrated to form the region of interest (ROI), which is then fed into Stage II. In Stage II, the second DL model segments the far wall region. Lumen-intima (LI) and media- adventitial (MA) boundaries are then extracted from the wall region, which is then used for cIMT and PA measurement. Results Using the database of 250 carotid scans, the cIMT error using the AI model is mm, which is lower than those of all previous methods. The PA error is found to be mm2. The system's correlation coefficient (CC) between AI and ground truth (GT) values for cIMT is 0.99 (p < 0.0001), which is higher compared with the CC of 0.96 (p < 0.0001) shown by the earlier DL method. The CC for PA between AI and GT values is 0.89 (p < 0.0001). Conclusion A novel AI-based strategy was applied to carotid US images for the joint detection of carotid wall thickness (cWT) and plaque area (PA), followed by cIMT and PA measurement. This AI-based strategy shows improved performance using the patch technique compared with previous methods using full carotid scans.

Cerebral small vessel disease (cSVD) has a crucial role in lacunar stroke and brain hemorrhages and is a leading cause of cognitive decline and functional loss in elderly patients. Based on underlying pathophysiology, cSVD can be subdivided into amyloidal and non-amyloidal subtypes. Genetic factors of cSVD play a pivotal role in terms of unraveling molecular mechanism. An important pathophysiological mechanism of cSVD is blood-brain barrier leakage and endothelium dysfunction which gives a clue in identification of the disease through circulating biological markers. Detection of cSVD is routinely carried out by key neuroimaging markers including white matter hyperintensities, lacunes, small subcortical infarcts, perivascular spaces, cerebral microbleeds, and brain atrophy. Application of neural networking, machine learning and deep learning in image processing have increased significantly for correct severity of cSVD. A linkage between cSVD and other neurological disorder, such as Alzheimer’s and Parkinson’s disease and non-cerebral disease, has also been investigated recently. This review draws a broad picture of cSVD, aiming to inculcate new insights into its pathogenesis and biomarkers. It also focuses on the role of deep machine strategies and other dimensions of cSVD by linking it with several cerebral and non-cerebral diseases as well as recent advances in the field to achieve sensitive detection, effective prevention and disease management.

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.

Background: Stroke is in the top three leading causes of death worldwide. Non-invasive monitoring of stroke can be accomplished via stenosis measurements. The current conventional image-based methods for these measurements are not accurate and reliable. They do not incorporate shape and intelligent learning component in their design. Methods: In this study, we propose a deep learning (DL)-based methodology for accurate measurement of stenosis in common carotid artery (CCA) ultrasound (US) scans using a class of AtheroEdge system from AtheroPoint, USA. Three radiologists manually traced the lumen-intima (LI) for the near and the far walls, respectively, which served as a gold standard (GS) for training the DL-based model. Three DL-based systems were developed based on three types of GS. Results: IRB approved (Toho University, Japan) 407 US scans from 204 patients were collected. The risk was characterized into three classes: low, moderate, and high-risk. The area-under-curve (AUC) corresponding to three DL systems using receiver operating characteristic (ROC) analysis computed were: 0.90, 0.94 and 0.86, respectively. Conclusions: Novel DL-based strategy showed reliable, accurate and stable stenosis severity index (SSI) measurements.

Motivation The carotid intima-media thickness (cIMT) is an important biomarker for cardiovascular diseases and stroke monitoring. This study presents an intelligence-based, novel, robust, and clinically-strong strategy that uses a combination of deep-learning (DL) and machine-learning (ML) paradigms. Methodology A two-stage DL-based system (a class of AtheroEdge™ systems) was proposed for cIMT measurements. Stage I consisted of a convolution layer-based encoder for feature extraction and a fully convolutional network-based decoder for image segmentation. This stage generated the raw inner lumen borders and raw outer interadventitial borders. To smooth these borders, the DL system used a cascaded stage II that consisted of ML-based regression. The final outputs were the far wall lumen-intima (LI) and media-adventitia (MA) borders which were used for cIMT measurements. There were two sets of gold standards during the DL design, therefore two sets of DL systems (DL1 and DL2) were derived. Results A total of 396 B-mode ultrasound images of the right and left common carotid artery were used from 203 patients (Institutional Review Board approved, Toho University, Japan). For the test set, the cIMT error for the DL1 and DL2 systems with respect to the gold standard was 0.126 ± 0.134 and 0.124 ± 0.100 mm, respectively. The corresponding LI error for the DL1 and DL2 systems was 0.077 ± 0.057 and 0.077 ± 0.049 mm, respectively, while the corresponding MA error for DL1 and DL2 was 0.113 ± 0.105 and 0.109 ± 0.088 mm, respectively. The results showed up to 20% improvement in cIMT readings for the DL system compared to the sonographer's readings. Four statistical tests were conducted to evaluate reliability, stability, and statistical significance. Conclusion The results showed that the performance of the DL-based approach was superior to the nonintelligence-based conventional methods that use spatial intensities alone. The DL system can be used for stroke risk assessment during routine or clinical trial modes.

The objective of this paper was to investigate texture feature variability in ultrasound video of the carotid artery during the cardiac cycle in an attempt to define new discriminatory biomarkers of the vulnerable plaque. More specifically, in this paper, 120 longitudinal ultrasound videos, acquired from 40 normal (N) subjects from the common carotid artery and 40 asymptomatic (A) and 40 symptomatic (S) subjects from the proximal internal carotid artery were investigated. The videos were intensity normalized and despeckled, and the intima-media complex (IMC) (from the N subjects) and the atherosclerotic carotid plaques (from the A and S subjects) were segmented from each video, in order to extract the M-mode image, and the texture features associated with cardiac states of systole and diastole. The main results of this paper can be summarized as follows: 1) texture features varied significantly throughout the cardiac cycle with significant differences identified between the cardiac systolic and cardiac diastolic states; 2) gray scale median was significantly higher at cardiac systole versus diastole for the N, A, and S groups investigated; 3) plaque texture features extracted during the cardiac cycle at the systolic and diastolic states were statistically significantly different between A and S subjects (and can thus be used to discriminate between A and S subjects successfully). The combination of systolic and diastolic features yields better performance than those alone. It is anticipated that the proposed system may aid the physician in clinical practice in classifying between N, A, and S subjects using texture features extracted from ultrasound videos of IMC and carotid artery plaque. However, further evaluation has to be carried out with more videos and additional features., Illustration of the steps followed for segmenting the atherosclerotic plaque, computing the M-mode image and the state diagram from a video of an asymptomatic male subject at the age of 69 with 65% stenosis.

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

Aim: The study investigated the association of carotid ultrasound echolucent plaque-based biomarker with HbA1c, measured as age-adjusted grayscale median (AAGSM) as a function of chronological age, total plaque area, and conventional grayscale median (GSMconv). Methods: Two stages were developed: (a) automated measurement of carotid parameters such as total plaque area (TPA) ; (b) computing the AAGSM as a function of GSMconv, age, and TPA. Intra-operator (novice and experienced) analysis was conducted. Results: IRB approved, 204 patients' left/right CCA (408 images) ultrasound scans were collected: mean age: 69 ± 11 years ; mean HbA1c: 6.12 ± 1.47%. A moderate inverse correlation was observed between AAGSM and HbA1c (CC of -0.13, P = 0.01), compared to GSM (CC of -0.06, P = 0.24). The RCCA and LCCA showed CC of -0.18, P < 0.01 and -0.08 ; P < 0.24. Female and males showed CC of -0.29, P < 0.01 and -0.10, P = 0.09. Using the threshold for AAGSM and HbA1c as: low-risk (AAGSM > 100 ; HbA1c < 5.7%), moderate-risk (40 < AAGSM < 100 ; 5.7% < HbA1c < 6.5%) and high-risk (AAGSM < 40 ; HbA1c > 6.5%), the area under the curve showed a better performance of AAGSM over GSMconv. A paired t-test between operators and expert (P < 0.0001) ; inter- operator CC of 0.85 (P < 0.0001). Conclusions: Echolucent plaque in patients with diabetes can be more accurately characterized for risk stratification using AAGSM compared to GSMconv.

Cardiovascular disease (CVD) and stroke risk assessment have been largely based on the success of traditional statistically derived risk calculators such as Pooled Cohort Risk Score or Framingham Risk Score. However, over the last decade, automated computational paradigms such as machine learning (ML) and deep learning (DL) techniques have penetrated into a variety of medical domains including CVD/stroke risk assessment. This review is mainly focused on the changing trends in CVD/stroke risk assessment and its stratification from statistical-based models to ML-based paradigms using non-invasive carotid ultrasonography. In this review, ML-based strategies are categorized into two types: non-image (or conventional ML-based) and image-based (or integrated ML-based). The success of conventional (non-image-based) ML-based algorithms lies in the different data-driven patterns or features which are used to train the ML systems. Typically these features are the patients’ demographics, serum biomarkers, and multiple clinical parameters. The integrated (image-based) ML-based algorithms integrate the features derived from the ultrasound scans of the arterial walls (such as morphological measurements) with conventional risk factors in ML frameworks. Even though the review covers ML-based system designs for carotid and coronary ultrasonography, the main focus of the review is on CVD/stroke risk scores based on carotid ultrasound. There are two key conclusions from this review: (i) fusion of image-based features with conventional cardiovascular risk factors can lead to more accurate CVD/stroke risk stratification; (ii) the ability to handle multiple sources of information in big data framework using artificial intelligence-based paradigms (such as ML and DL) is likely to be the future in preventive CVD/stroke risk assessment.

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.

PURPOSE: Conventional cardiovascular risk factors (CCVRFs) and carotid ultrasound image-based phenotypes (CUSIP) are independently associated with long-term risk of cardiovascular (CV) disease. In this study, 26 cardiovascular risk (CVR) factors which consisted of a combination of CCVRFs and CUSIP together were ranked. Further, an optimal risk calculator using AtheroEdge composite risk score (AECRS1.0) was designed and benchmarked against seven conventional CV risk (CVR) calculators. METHODS: Two types of ranking were performed: (i) ranking of 26 CVR factors and (ii) ranking of eight types of 10-year risk calculators. In the first case, multivariate logistic regression was used to compute the odds ratio (OR) and in the second, receiver operating characteristic curves were used to evaluate the performance of eight types of CVR calculators using SPSS23.0 and MEDCALC12.0 with validation against STATA15.0. RESULTS: The left and right common carotid arteries (CCA) of 202 Japanese patients were examined to obtain 404 ultrasound scans. CUSIP ranked in the top 50% of the 26 covariates. Intima-media thickness variability (IMTV) and IMTV10yr were the most influential carotid phenotypes for left CCA (OR = 250, P

BackgroundWe characterised the phenotypic consequence of genetic variation at the PCSK9 locus and compared findings with recent trials of pharmacological inhibitors of PCSK9.MethodsPublished and individual participant level data (300,000+ participants) were combined to construct a weighted PCSK9 gene-centric score (GS). Fourteen randomized placebo controlled PCSK9 inhibitor trials were included, providing data on 79,578 participants. Results were scaled to a one mmol/L lower LDL-C concentrationResultsThe PCSK9 GS (comprising 4 SNPs) associations with plasma lipid and apolipoprotein levels were consistent in direction with treatment effects. The GS odds ratio (OR) for myocardial infarction (MI) was 0.53 (95%CI 0.42; 0.68), compared to a PCSK9 inhibitor effect of 0.90 (95%CI 0.86; 0.93). For ischemic stroke ORs were 0.84 (95%CI 0.57; 1.22) for the GS, compared to 0.85 (95%CI 0.78; 0.93) in the drug trials. ORs with type 2 diabetes mellitus (T2DM) were 1.29 (95% CI 1.11; 1.50) for the GS, as compared to 1.00 (95%CI 0.96; 1.04) for incident T2DM in PCSK9 inhibitor trials. No genetic associations were observed for cancer, heart failure, atrial fibrillation, chronic obstructive pulmonary disease, or Alzheimer’s disease – outcomes for which large-scale trial data were unavailable.ConclusionsGenetic variation at the PCSK9 locus recapitulates the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and MI. Apparent discordance between genetic associations and trial outcome for T2DM might be explained lack by a of statistical precision, or differences in the nature and duration of genetic versus pharmacological perturbation of PCSK9.FundingThis research was funded by the British Heart Foundation (SP/13/6/30554, RG/10/12/28456, FS/18/23/33512), UCL Hospitals NIHR Biomedical Research Centre, by the Rosetrees and Stoneygate Trusts.Condensed abstractEvidence on the long-term efficacy and safety of therapeutic inhibition of PCSK9 is lacking. To explore potential long-term effects of PCSK9 inhibition, we characterised the phenotypic consequence of LDL-cholesterol lowering variants at the PCSK9 locus. A PCSK9 gene score comprising 4 SNPs recapitulated the effects of therapeutic inhibition of PCSK9 on major blood lipid fractions and risk of myocardial infarction, and was associated with an increased risk of type 2 diabetes. No associations with safety outcomes such as cancer, COPD, Alzheimer’s disease or atrial fibrillation were identified. Our findings suggest PCSK9 inhibition may be safe and effective during prolonged use.

Deep learning (DL) is affecting each and every sphere of public and private lives and becoming a tool for daily use. The power of DL lies in the fact that it tries to imitate the activities of neurons in the neocortex of human brain where the thought process takes place. Therefore, like the brain, it tries to learn and recognize patterns in the form of digital images. This power is built on the depth of many layers of computing neurons backed by high power processors and graphics processing units (GPUs) easily available today. In the current scenario, we have provided detailed survey of various types of DL systems available today, and specifically, we have concentrated our efforts on current applications of DL in medical imaging. We have also focused our efforts on explaining the readers the rapid transition of technology from machine learning to DL and have tried our best in reasoning this paradigm shift. Further, a detailed analysis of complexities involved in this shift and possible benefits accrued by the users and developers.

Motivation AtheroEdge Composite Risk Score (AECRS1.010yr) is an integrated stroke/cardiovascular risk calculator that was recently developed and computes the 10-year risk of carotid image phenotypes by integrating conventional cardiovascular risk factors (CCVRFs). It is therefore important to understand how closely AECRS1.010yr is associated with the ten other currently available conventional cardiovascular risk calculators (CCVRCs). Methods The Institutional Review Board of Toho University approved the examination of the left/right common carotid arteries of 202 Japanese patients. Step 1 consists of measurement of AECRS1.010yr, given current image phenotypes and CCVRFs. Step 2 consists of computing the risk score using ten different CCVRCs given CCVR factors: QRISK3, Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study (UKPDS) 56, UKPDS60, Reynolds Risk Score (RRS), Pooled cohort Risk Score (PCRS or ASCVD), Systematic Coronary Risk Evaluation (SCORE), Prospective Cardiovascular Munster Study (PROCAM) calculator, NIPPON, and World Health Organization (WHO) risk. Step 3 consists of computing the closeness factor between AECRS1.010yr and ten CCVRCs using cumulative ranking index derived using eight different statistically derived metrics. Results AECRS1.010yr reported the highest area-under-the-curve (0.927;P Conclusion AECRS1.010yr produced the largest AUC due to the integration of image-based phenotypes with CCVR factors, and ranked at first place with the highest AUC. Cumulative ranking of ten CCVRCs demonstrated that QRISK3 was the closest calculator to AECRS1.010yr, which is also consistent with the industry trend.