Assessing the impact of ultrasound image standardization in deep learning-based segmentation of carotid plaque types.

BACKGROUND AND OBJECTIVE: Carotid B-mode ultrasound (CBUS) imaging is often used to detect and assess atherosclerotic plaques. Doctors often need to segment plaques in the CBUS images to further examine them. Multiple studies have proposed two-dimensional CBUS plaque segmentation deep learning (DL)-based solutions, achieving promising results. In most of these studies, image standardization is not reported, while not all plaque types are represented. However, prior multiple studies have highlighted the importance of data standardization in computerized CBUS plaque classification or segmentation solutions. In this study, we propose and separately evaluate three progressive preprocessing schemes, to discover the most optimal to standardize CBUS images for DL-based carotid plaque segmentation, while we also assess the effect of each preprocessing in the segmentation performance per echodensity-based plaque type (I, II, III, IV and V). METHODS: We included three CBUS image datasets (276 CBUS images, from three medical centres), with which we produced 3 data folds (with the best possible equal inclusion of images from all centers per fold), to perform 3-fold cross validation-based training and evaluation of the pre-released Channel-wise Feature Pyramid Network for Medicine (CFPNet-M) model, in carotid plaque type segmentation. We included the three data folds in their original version (O), generating also three preprocessed versions of them, namely, the resolution-normalized (R), the resolution- and intensity-normalized (RN), and the resolution- and intensity-normalized combined with despeckling (RND) versions. The samples were cropped to the plaque level, and the intersection over union (IoU) and the Dice Similarity Coefficient (DSC), along with other metrics, were used to measure the model's performance. In each training round, 12 % of the images in the 2 training folds was used for internal validation (last fold was used in evaluation). Two experienced ultrasonographers manually delineated plaques in the dataset, to provide us with ground truths, while the plaque types (I to V) were extracted according to the Gray-Weale and Geroulakos classification system. We measured the mean±standard deviation of DSC within and across the three evaluated folds, per preprocessing scheme and per plaque type. RESULTS: CFPNet-M segmented the plaques in the CBUS images in all the data preprocessing versions, yielding progressively improved performances (mean DSC at 81.9 ± 9.1 %, 83.6 ± 9.0 %, 84.1 ± 8.3 %, and 84.4 ± 8.1 % for the O, R, RN and RND 3-fold cross validation processes, respectively), irrespective of the plaque type. Interestingly, CFPNet_M yielded improved performances, for all plaque types (I, II, III, IV and V), when trained and tested with the RND data versus the O version, achieving an 80.6 ± 11 % versus 77.6 ± 17 % DSC for type I, an 84.3 ± 8 % versus 81.2 ± 9 % DSC for type II, an 84.9 ± 7 % versus 82.6 ± 7 % for type III, an 85.3 ± 8 % versus 83.9 ± 7 % for type IV, and a 84.8 ± 8 % versus 81.8 ± 2 % for type V. The best increase in DSC, from the O to the RND CBUS images, was found for the plaque type I (3.86 % increase), with types II and V, following. CONCLUSIONS: In this study, we investigated the impact of CBUS standardization in DL-based carotid plaque type segmentation and showed that indeed normalization of the image resolution and intensity, combined with speckle noise removal, prior to model training and testing, enhances the DL model's performance, across all plaque types. Based on the findings in this study, CBUS images should be standardized when destined for DL-based segmentation tasks, while all plaque types should be considered, as in a plethora of existing relevant studies, uniformly echolucent plaques or heavily calcified plaques with acoustic shadow are notably underrepresented.

Cardiovascular Disease Risk Stratification Using Hybrid Deep Learning Paradigm: First of Its Kind on Canadian Trial Data.

BACKGROUND: The risk of cardiovascular disease (CVD) has traditionally been predicted via the assessment of carotid plaques. In the proposed study, AtheroEdge™ 3.0HDL (AtheroPoint™, Roseville, CA, USA) was designed to demonstrate how well the features obtained from carotid plaques determine the risk of CVD. We hypothesize that hybrid deep learning (HDL) will outperform unidirectional deep learning, bidirectional deep learning, and machine learning (ML) paradigms. METHODOLOGY: 500 people who had undergone targeted carotid B-mode ultrasonography and coronary angiography were included in the proposed study. ML feature selection was carried out using three different methods, namely principal component analysis (PCA) pooling, the chi-square test (CST), and the random forest regression (RFR) test. The unidirectional and bidirectional deep learning models were trained, and then six types of novel HDL-based models were designed for CVD risk stratification. The AtheroEdge™ 3.0HDL was scientifically validated using seen and unseen datasets while the reliability and statistical tests were conducted using CST along with p-value significance. The performance of AtheroEdge™ 3.0HDL was evaluated by measuring the p-value and area-under-the-curve for both seen and unseen data. RESULTS: The HDL system showed an improvement of 30.20% (0.954 vs. 0.702) over the ML system using the seen datasets. The ML feature extraction analysis showed 70% of common features among all three methods. The generalization of AtheroEdge™ 3.0HDL showed less than 1% (p-value < 0.001) difference between seen and unseen data, complying with regulatory standards. CONCLUSIONS: The hypothesis for AtheroEdge™ 3.0HDL was scientifically validated, and the model was tested for reliability and stability and is further adaptable clinically.

UltraAIGenomics: Artificial Intelligence-Based Cardiovascular Disease Risk Assessment by Fusion of Ultrasound-Based Radiomics and Genomics Features for Preventive, Personalized and Precision Medicine: A Narrative Review.

Cardiovascular disease (CVD) diagnosis and treatment are challenging since symptoms appear late in the disease's progression. Despite clinical risk scores, cardiac event prediction is inadequate, and many at-risk patients are not adequately categorised by conventional risk factors alone. Integrating genomic-based biomarkers (GBBM), specifically those found in plasma and/or serum samples, along with novel non-invasive radiomic-based biomarkers (RBBM) such as plaque area and plaque burden can improve the overall specificity of CVD risk. This review proposes two hypotheses: (i) RBBM and GBBM biomarkers have a strong correlation and can be used to detect the severity of CVD and stroke precisely, and (ii) introduces a proposed artificial intelligence (AI)-based preventive, precision, and personalized ( aiP 3 ) CVD/Stroke risk model. The PRISMA search selected 246 studies for the CVD/Stroke risk. It showed that using the RBBM and GBBM biomarkers, deep learning (DL) modelscould be used for CVD/Stroke risk stratification in the aiP 3 framework. Furthermore, we present a concise overview of platelet function, complete blood count (CBC), and diagnostic methods. As part of the AI paradigm, we discuss explainability, pruning, bias, and benchmarking against previous studies and their potential impacts. The review proposes the integration of RBBM and GBBM, an innovative solution streamlined in the DL paradigm for predicting CVD/Stroke risk in the aiP 3 framework. The combination of RBBM and GBBM introduces a powerful CVD/Stroke risk assessment paradigm. aiP 3 model signifies a promising advancement in CVD/Stroke risk assessment.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Chronic venous disease: What if everything started with early care? A discussion with experts.

Chronic venous disease (CVD) is an umbrella term for a group of morphological and functional disorders of the venous system. Clinical signs of CVD may range from telangiectasia and reticular veins to active venous ulcers; therefore, earlier diagnosis and management of CVD may delay disease progression and reduce the burden of CVD on patients, caregivers, and healthcare systems. In this podcast discussion, Professor Andrew Nicolaides, Professor Stavros Kakkos, and Dr Gerardo Estrada-Guerrero share the key highlights from their symposium at the 2023 European Venous Forum. This symposium, titled "Chronic venous disease: what if everything started with early care?", discussed the clinical significance of "functional CVD," evidence and risk factors for CVD progression, and real-world strategies to facilitate earlier diagnosis and management of CVD. Together, these topics highlight the importance of early care to improve long-term outcomes for people with CVD.

Chronic venous disease (CVD) occurs when the blood vessels that carry blood back to the heart are damaged. In the early stages of CVD, people may have visible or swollen veins in their legs and feet, and may feel pain, heaviness, burning, itching, and cramping. Without treatment, people with CVD may develop open sores (ulcers) that are hard to heal and could get infected, so it is important that CVD is diagnosed and treated early. In this podcast, three doctors who specialize in CVD answer questions about a presentation they gave at a recent medical conference. In their presentation, the doctors talked about people who experience feelings of CVD but without any visible signs, and looked at programs that might help doctors diagnose CVD earlier. The doctors agree that it is important to diagnose and treat CVD early, so that people can avoid the long-term effects of this disease.

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

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

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

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

An international, multispecialty, expert-based Delphi Consensus document on controversial issues in the management of patients with asymptomatic and symptomatic carotid stenosis.

OBJECTIVE: Despite the publication of various national/international guidelines, several questions concerning the management of patients with asymptomatic (AsxCS) and symptomatic (SxCS) carotid stenosis remain unanswered. The aim of this international, multi-specialty, expert-based Delphi Consensus document was to address these issues to help clinicians make decisions when guidelines are unclear. METHODS: Fourteen controversial topics were identified. A three-round Delphi Consensus process was performed including 61 experts. The aim of Round 1 was to investigate the differing views and opinions regarding these unresolved topics. In Round 2, clarifications were asked from each participant. In Round 3, the questionnaire was resent to all participants for their final vote. Consensus was reached when ≥75% of experts agreed on a specific response. RESULTS: Most experts agreed that: (1) the current periprocedural/in-hospital stroke/death thresholds for performing a carotid intervention should be lowered from 6% to 4% in patients with SxCS and from 3% to 2% in patients with AsxCS; (2) the time threshold for a patient being considered "recently symptomatic" should be reduced from the current definition of "6 months" to 3 months or less; (3) 80% to 99% AsxCS carries a higher risk of stroke compared with 60% to 79% AsxCS; (4) factors beyond the grade of stenosis and symptoms should be added to the indications for revascularization in AsxCS patients (eg, plaque features of vulnerability and silent infarctions on brain computed tomography scans); and (5) shunting should be used selectively, rather than always or never. Consensus could not be reached on the remaining topics due to conflicting, inadequate, or controversial evidence. CONCLUSIONS: The present international, multi-specialty expert-based Delphi Consensus document attempted to provide responses to several unanswered/unresolved issues. However, consensus could not be achieved on some topics, highlighting areas requiring future research.

Recent advances and controversial issues in the optimal management of asymptomatic carotid stenosis.

OBJECTIVE: The optimal management of patients with asymptomatic carotid stenosis (AsxCS) is enduringly controversial. We updated our 2021 Expert Review and Position Statement, focusing on recent advances in the diagnosis and management of patients with AsxCS. METHODS: A systematic review of the literature was performed up to August 1, 2023, using PubMed/PubMed Central, EMBASE and Scopus. The following keywords were used in various combinations: "asymptomatic carotid stenosis," "carotid endarterectomy" (CEA), "carotid artery stenting" (CAS), and "transcarotid artery revascularization" (TCAR). Areas covered included (i) improvements in best medical treatment (BMT) for patients with AsxCS and declining stroke risk, (ii) technological advances in surgical/endovascular skills/techniques and outcomes, (iii) risk factors, clinical/imaging characteristics and risk prediction models for the identification of high-risk AsxCS patient subgroups, and (iv) the association between cognitive dysfunction and AsxCS. RESULTS: BMT is essential for all patients with AsxCS, regardless of whether they will eventually be offered CEA, CAS, or TCAR. Specific patient subgroups at high risk for stroke despite BMT should be considered for a carotid revascularization procedure. These patients include those with severe (≥80%) AsxCS, transcranial Doppler-detected microemboli, plaque echolucency on Duplex ultrasound examination, silent infarcts on brain computed tomography or magnetic resonance angiography scans, decreased cerebrovascular reserve, increased size of juxtaluminal hypoechoic area, AsxCS progression, carotid plaque ulceration, and intraplaque hemorrhage. Treatment of patients with AsxCS should be individualized, taking into consideration individual patient preferences and needs, clinical and imaging characteristics, and cultural, ethnic, and social factors. Solid evidence supporting or refuting an association between AsxCS and cognitive dysfunction is lacking. CONCLUSIONS: The optimal management of patients with AsxCS should include BMT for all individuals and a prophylactic carotid revascularization procedure (CEA, CAS, or TCAR) for some asymptomatic patient subgroups, additionally taking into consideration individual patient needs and preference, clinical and imaging characteristics, social and cultural factors, and the available stroke risk prediction models. Future studies should investigate the association between AsxCS with cognitive function and the role of carotid revascularization procedures in the progression or reversal of cognitive dysfunction.

Reduction of lower-limb edema in patients with chronic venous disease by micronized purified flavonoid fraction: a systematic literature review and meta-analysis.

INTRODUCTION: The prevalence of lower limb edema is high among patients with chronic venous disease (CVD). Several clinical studies with various designs have assessed the effect of micronized purified flavonoid fraction (MPFF) on edema. The aim of this work was to provide a comprehensive and accurate evaluation of the reduction in ankle and calf circumference as an indicator of lower limb edema reduction in patients with CVD treated with MPFF by combining studies that use different designs in a single group meta-analysis. EVIDENCE ACQUISITION: We conducted a systematic literature review in April 2022 based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) criteria to identify prospective studies investigating the effect of oral MPFF treatment 1000 mg/day on ankle and calf circumference in patients with CVD. Studies with population including at least one patient with an ulcer were excluded. All prospective studies irrespectively of design (i.e., interventional and non-interventional studies, randomized controlled trials (RCTs), non-randomized studies, studies without a control or reference treatment) were eligible. The Medline, Embase and Cochrane databases were searched. Endpoints were ankle and calf circumference measurements and their overall mean change from baseline estimated with random-effects meta-analysis methods. The evaluation criterion feeling of swelling was also analyzed as a standardized mean change (SMC) with 95% confidence intervals after combination of quantitative scales. EVIDENCE SYNTHESIS: Among 861 articles identified, eight studies (five RCTs including one placebo-controlled, three non-comparative studies) met the criteria. The overall population consisted of 1635 patients, predominantly female (89% ranging from 64% to 94%) with a mean age of 47 years ranging from 41 to 48 years. Mean reduction in ankle circumference was 6.0 mm (95%CI: 3.6 to 8.4; P<0.001) and 7.0 mm (95%CI: 0.9 to 13.1; P=0.024) after two and at least six months of treatment respectively. The results were similar when considering the study type RCTs and non-RCTs. Mean reduction in calf circumference was 5.7 mm (95%CI: 2.8 to 8.6; P<0.001) and 6.7 mm (95%CI: 5.2 to 8.1; P<0.001), at two months and at the last post-baseline evaluation respectively. Heterogeneity among studies was statistically significant (degree of consistency I2=93.5%; P<0.001 and I2=81.1%, P<0.01 for ankle and calf circumference, respectively). In the three studies reporting the effect on feeling of swelling a significant standardized mean change (SMC) reduction of 2.2 (95%CI: 0.2 to 4.2; P=0.028) on a quantitative scale was observed after two months of treatment with MPFF. CONCLUSIONS: MPFF appeared to be effective in reducing ankle and calf circumference as well as feeling of swelling irrespective of study design. The circumference reduction is present at short and long term, suggesting that benefit occurs early and is maintained overtime. Despite the observed heterogeneity among included studies, this meta-analysis supports the significant therapeutic efficacy of MPFF in reducing lower-limb edema in patients with CVD. The complete video presentation of the work is available online at www.minervamedica.it (Supplementary Digital Material 1: Supplementary Video 1, 5 min, 192 MB).

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

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

A Pharmaceutical Paradigm for Cardiovascular Composite Risk Assessment Using Novel Radiogenomics Risk Predictors in Precision Explainable Artificial Intelligence Framework: Clinical Trial Tool.

BACKGROUND: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. OBJECTIVE: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP3) framework benefiting the pharmaceutical paradigm. METHOD: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdgeTM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. CONCLUSIONS: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdgeTM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm.

Cardiovascular disease/stroke risk stratification in deep learning framework: a review.

The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.

Why do guidelines recommend screening for abdominal aortic aneurysms, but not for asymptomatic carotid stenosis? A plea for a randomized controlled trial.

BACKGROUND: 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.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Performance of SCORE2 and SCORE2-OP risk algorithms in a Cypriot cohort.

BACKGROUND: 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). METHODS: 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 revascularization, transient ischemic attack, claudication and critical limb ischemia). RESULTS: 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 high-risk regions was used. CONCLUSIONS: SCORE2 and SCORE2-OP moderate risk algorithms perform 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.