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The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.

Hypertension remains a major public health issue with inadequate control worldwide. The May Measurement Month (MMM) initiative by the International Society of Hypertension was implemented in Greece in 2019 aiming to raise hypertension awareness and control. Adult volunteers (≥18 years) were recruited through opportunistic screening in five urban areas. Information on medical history and triplicate sitting blood pressure (BP) measurements were obtained using validated automated upper-arm devices. Hypertension was defined as systolic BP ≥140 mmHg and/or diastolic ≥90 mmHg, and/or self-reported use of drugs for hypertension. A total of 5727 were analysed [mean age 52.7 (SD 16.6) years, men 46.5%, 88.3% had BP measurement in the last 18 months]. The prevalence of hypertension was (41.6%) and was higher in men and in older individuals. Among individuals with hypertension, 78.7% were diagnosed, 73.1% treated, and 48.3% controlled. Awareness, treatment, and control of hypertension were higher in women and in older individuals. Hypertensives had a higher body mass index (BMI) and were more likely to have diabetes, myocardial infarction and stroke, and less likely to smoke than normotensives (all P

Wilson's disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a "conventional deep convolution neural network" (cDCNN) and an "improved DCNN" (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring "differentiable at zero." Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning-based "Inception V3" paradigm by 11.92% and (b) four types of "conventional machine learning-based systems": k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. Graphical Abstract.

Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visually, primarily due to subtle differences in WMH. This Letter presents a computer-aided design-based automated classification strategy that uses optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii) the Visual Geometric Group-19 (VGG-19). Further, the authors benchmark TL systems against a machine learning (ML) paradigm. Using four-fold augmentation, VGG-19 is superior to MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70%, 0.932 (p < 0.0001) and 86.87 ± 2.23%, 0.871 (p < 0.0001), respectively. Further, MobileNet and VGG-19 showed an improvement of 3.4 and 13.5%, respectively, when benchmarked against the ML-based soft classifier – Random Forest.

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

Background Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA. Methods The system follows a four-step process: (I) VA using cIMT based using linear- regression (LR) model and its coefficients ; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)-based model with gender adjustment ; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA ; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area- under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin. Results The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1, 287 ultrasound scans. The 10- year FRS using IVA reported higher AUC (AUC =0.78) compared with 10- year FRS using CA (AUC =0.66) by ~18%. Conclusions IVA is an efficient biomarker for risk stratifications for patients in routine practice.

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

Diabetes and atherosclerosis are the predominant causes of stroke and cardiovascular disease (CVD) both in low- and high-income countries. This is due to the lack of appropriate medical care or high medical costs. Low-cost 10-year preventive screening can be used for deciding an effective therapy to reduce the effects of atherosclerosis in diabetes patients. American College of Cardiology (ACC)/American Heart Association (AHA) recommended the use of 10-year risk calculators, before advising therapy. Conventional risk calculators are suboptimal in certain groups of patients because their stratification depends on (a) current blood biomarkers and (b) clinical phenotypes, such as age, hypertension, ethnicity, and sex. The focus of this review is on risk assessment using innovative composite risk scores that use conventional blood biomarkers combined with vascular image-based phenotypes. AtheroEdge™ tool is beneficial for low-moderate to high- moderate and low-risk to high-risk patients for the current and 10-year risk assessment that outperforms conventional risk calculators. The preventive screening tool that combines the image- based phenotypes with conventional risk factors can improve the 10-year cardiovascular/stroke risk assessment.

The aim of this study was to calculate the average operational cost per sub-type of stroke patient and to investigate cost drivers (e.g. ALoS, NIHSS score, age) correlated to cost.Direct medical costs (diagnostic imaging and clinical laboratory exams, overheads/bed cost, pharmaceuticals, ringers and other non-durables and inpatient rehabilitation) per patient were calculated from the providers' (hospitals') perspective. Resource use data derived from the "SUN4P" web-based registry and unit costs were retrieved from publically available sources and were assigned to resource use.The sample comprised 6,282 inpatient days of 750 patients (mean age: 75.5±13.3 years) admitted from July 2019 to July 2021, in nine public hospitals. Mean length of stay was 8.4±7.6 days and mean total operational cost was calculated to €1,239.4 (from which 45% and 35% related to diagnostic exams and overheads/bed cost respectively). Mean cost related to hemorrhagic stroke patients that were discharged alive was calculated significantly higher compared to mean cost related to ischemic stroke patients who didn't undertake thrombolysis and were also discharged alive from the hospital (€2,155.2 vs. €945.2, p0.001). Linear regression analysis revealed that length of stay was significantly correlated with cost (coefficient beta=232, 95% CI confidence interval = 220-243, p0.001).These findings are in accordance with current evidence and should be thoroughly assessed to rationalize inpatient reimbursement rates in order to achieve improved value of care.

The aim of this study was to present the descriptive characteristics of the Stroke Units Necessity for Patients (SUN4P) registry.The study population derived from the web-based SUN4P registry included 823 patients with first-ever acute stroke. Descriptive statistics were used to present patients' characteristics.The vast majority of patients (80.4%) had an ischemic stroke, whereas 15.4% had a hemorrhagic stroke. Hypertension was the leading risk factor in both patients. The patients with ischemic stroke had higher prevalence of traditional cardiovascular risk factors such as diabetes mellitus, dyslipidemia and smoking and most commonly cryptogenic stroke (39%). National Institutes of Health Stroke Scale (NIHSS) was higher among patients with hemorrhagic in comparison to those with ischemic stroke (10.5 vs 6 respectively). Moreover, all patients had similar rate of disability prior to stroke, as shown by Modified Rankin Scale (mRS=0).These data are in accordance with current evidence and should be thoroughly assessed in order to ensure optimal therapeutic management of stroke patients.

To assess stroke patient-reported experiences and hospital staff experiences, during hospital stay.Stroke patient-reported experiences (n=387) were recorded using the translated and culturally adapted NHS-Stroke Questionnaire into Greek and staff experiences (n=236) were investigated using the Compassion Satisfaction and Burnout subscales of the ProQOL questionnaire.Staff's mean compassion satisfaction score was 39.2 (SD=6.3) and mean burnout score was 24.3 (SD=5.6). Only 38.5% of the staff stated that there is smooth cooperation with healthcare professionals of other specialties/disciplines. Personnel working in an NHS Hospital was more satisfied and less burned-out when compared to personnel working at a University Hospital (p=0.02 and p0.001, respectively). Mean total patient-reported experiences score was 81.9 (SD=9.5). Bivariate analysis revealed statistically significant differences for total patient-reported experiences among the eight study hospitals (p0.001).Health policy planners and decision-makers must take into consideration the results of such self-reported measures to establish innovative techniques to accomplish goals such as staff-specialization, continuous training and applying formal frameworks for efficient cooperation amongst different disciplines.

To assess stroke patient-reported experiences and hospital staff experiences, during hospital stay. Methods: Stroke patient-reported experiences (n=387) were recorded using the translated and culturally adapted NHS-Stroke Questionnaire into Greek and staff experiences (n=236) were investigated using the Compassion Satisfaction and Burnout subscales of the ProQOL questionnaire. Results: Staff’s mean compassion satisfaction score was 39.2 (SD=6.3) and mean burnout score was 24.3 (SD=5.6). Only 38.5% of the staff stated that there is smooth cooperation with healthcare professionals of other specialties/disciplines. Personnel working in an NHS Hospital was more satisfied and less burned-out when compared to personnel working at a University Hospital (p=0.02 and p0.001). Conclusions: Health policy planners and decision-makers must take into consideration the results of such self-reported measures to establish innovative techniques to accomplish goals such as staff-specialization, continuous training and applying formal frameworks for efficient cooperation amongst different disciplines.

The aim of this study was to present the descriptive characteristics of the Stroke Units Necessity for Patients (SUN4P) registry. Methods: The study population derived from the web-based SUN4P registry included 823 patients with first-ever acute stroke. Descriptive statistics were used to present patients’ characteristics. Results: The vast majority of patients (80.4%) had an ischemic stroke, whereas 15.4% had a hemorrhagic stroke. Hypertension was the leading risk factor in both patients. The patients with ischemic stroke had higher prevalence of traditional cardiovascular risk factors such as diabetes mellitus, dyslipidemia and smoking and most commonly cryptogenic stroke (39%). National Institutes of Health Stroke Scale (NIHSS) was higher among patients with hemorrhagic in comparison to those with ischemic stroke (10.5 vs 6 respectively). Moreover, all patients had similar rate of disability prior to stroke, as shown by Modified Rankin Scale (mRS=0). Conclusions: These data are in accordance with current evidence and should be thoroughly assessed in order to ensure optimal therapeutic management of stroke patients.

The aim of this study was to calculate the average operational cost per sub-type of stroke patient and to investigate cost drivers (e.g. ALoS, NIHSS score, age) correlated to cost. Methods: Direct medical costs (diagnostic imaging and clinical laboratory exams, overheads/bed cost, pharmaceuticals, ringers and other non-durables and inpatient rehabilitation) per patient were calculated from the providers’ (hospitals’) perspective. Resource use data derived from the “SUN4P” web-based registry and unit costs were retrieved from publically available sources and were assigned to resource use. Results: The sample comprised 6,282 inpatient days of 750 patients (mean age: 75.5±13.3 years) admitted from July 2019 to July 2021, in nine public hospitals. Mean length of stay was 8.4±7.6 days and mean total operational cost was calculated to €1,239.4 (from which 45% and 35% related to diagnostic exams and overheads/bed cost respectively). Mean cost related to hemorrhagic stroke patients that were discharged alive was calculated significantly higher compared to mean cost related to ischemic stroke patients who didn’t undertake thrombolysis and were also discharged alive from the hospital (€2,155.2 vs. €945.2, p

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value &lt, 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet &gt, VGG-SegNet &gt, NIH &gt, SegNet. The HDL runs in &lt, 1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

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).

Aortic stiffness assessed by carotid-femoral pulse wave velocity (PWV) is an important predictor to gauge the overall risk of hypertensive patients; nonetheless, it is underutilized in everyday practice. We propose a simple scoring system based on clinical variables that can identify patients with a priority for measurement of PWV, that is, those with elevated PWV (≥10 m/s) and, consequently, at higher risk for events.Patient data from three outpatient clinics (n = 3943) were used to form a derivation, internal and external validation cohort. For derivation, independent predictors of high PWV from a binary logistic regression model were split in subcategories and implemented in a simple clinical prediction scoring system with the acronym SAGE (office systolic blood pressure, age, glycemia and eGFR categories).Its performance was validated at the internal and external validation cohorts with c-statistics being 0.83 (95% CI 0.81-0.86) and 0.77 (95% CI 0.73-0.80), respectively. A cut-off of eight points to identify patients with high PWV in the internal/external validation cohorts yielded a positive-predictive value, negative-predictive value, sensitivity and specificity of 52/36%, 88/81%, 56/70% and 88/65%, respectively.The SAGE score that takes into account easily measured clinical variables (office SBP, age, fasting glucose and eGFR categories) can be used to accurately predict elevated levels of PWV and prioritize its measurement in specific hypertensive patients. Its use will result in greater acknowledgement of the role of aortic stiffness and aid physicians in implementing it in clinical practice.

The beneficial effect of multifactorial treatment of cardiovascular (CV) risk factors (RFs) in type 2 diabetes (T2D) is well established from randomized clinical trials. We prospectively evaluated the impact of such treatment in a real-world setting, on the development of subclinical arterial damage (SAD), as determined by structural/functional noninvasive biomarkers of vascular pathology (atheromatosis, carotid hypertrophy, arteriosclerosis). We prospectively studied 116 persons with T2D, treated with a multifactorial approach for CV RFs at a tertiary medical center, and 324 individuals without diabetes, for 3.2 years. The primary outcome was changes in vascular biomarkers related to SAD. At baseline, participants in the diabetes group had higher prevalence of SAD. At study end, the changes in clinical, biochemical, and lifestyle characteristics, as well as antihypertensive and lipid-lowering treatments, were comparable between the 2 groups. During follow-up, classical CV RFs (smoking, blood pressure, low-density lipoprotein-cholesterol, triglycerides) and behavioral features were significantly improved in both groups. Multivariate analysis, after adjusting for all classic CV RFs and use of antihypertensive/lipid-lowering therapies, demonstrated that all evaluated SAD biomarkers were similarly changed in the 2 groups. In conclusion, implementation of a multimodality approach of T2D treatment is feasible and efficacious in decelerating progression of SAD in routine clinical practice.

Chronic kidney disease (CKD) and cardiovascular disease (CVD) together result in an enormous burden on global healthcare. The estimated glomerular filtration rate (eGFR) is a well-established biomarker of CKD and is associated with adverse cardiac events. This review highlights the link between eGFR reduction and that of atherosclerosis progression, which increases the risk of adverse cardiovascular events. In general, CVD risk assessments are performed using conventional risk prediction models. However, since these conventional models were developed for a specific cohort with a unique risk profile and further these models do not consider atherosclerotic plaque-based phenotypes, therefore, such models can either underestimate or overestimate the risk of CVD events. This review examined the approaches used for CVD risk assessments in CKD patients using the concept of integrated risk factors. An integrated risk factor approach is one that combines the effect of conventional risk predictors and non-invasive carotid ultrasound image-based phenotypes. Furthermore, this review provided insights into novel artificial intelligence methods, such as machine learning and deep learning algorithms, to carry out accurate and automated CVD risk assessments and survival analyses in patients with CKD.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings., Highlights • The review presents the PRISMA model for ARDS-based studies and eight stages of pathophysiology in ARDS due to COVID-19. • Selection of comorbidity studies, and their statistical distribution in ARDS framework. • Classification of the AI models as per the by school of thought (SoTs), based on segregation of techniques and their characteristics, and their salient features distinguishing pre-and post- COVID-19 lungs. • Critical Recommendations for Artificial Intelligent systems for safe and clinically effective design.

BACKGROUND: Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRC(ML)) and compares against statistically derived CVRC (CVRC(Stat)) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors). METHODS: The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRC(Stat) (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRC(Stat), the patients were again risk-stratified using CVRC(ML) based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events. RESULTS: An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRC(Stat) calculators were: AECRS2.0 (AUC 0.83, P

Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1st, 2020. Since the outbreak began, almost 28,000 articles about COVID-19 have been published (https://pubmed.ncbi.nlm.nih.gov); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients—specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis., Highlights • This paper presents the four pathways that can lead to heart and brain injuries following COVID-19 infection. • This study gives several insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. • This study also discusses the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient.

This study attempted to investigate the behavior of 24‐hour central ambulatory blood pressure (ABP) in adolescents and young adults. Adolescents and young adults (age 10‐25 years) referred for elevated blood pressure (BP) and healthy volunteers had simultaneous 24‐hour peripheral (brachial) and central (aortic) ABP monitoring using the same automated upper‐arm cuff device (Mobil‐O‐Graph 24h PWA). Central BP was calculated by the device using two different calibration methods (C1SBP using peripheral systolic (pSBP)/diastolic BP and C2SBP using mean arterial/diastolic BP). A total of 136 participants (age 17.9 ± 4.7 years, 54% adolescents, 77% males, 25% volunteers, 34% with elevated peripheral ABP) were analyzed. Twenty‐four‐hour pSBP was higher than C1SBP, with this difference being more pronounced during daytime than nighttime (16.3 ± 4.5 and 10.5 ± 3.2 mm Hg, respectively, P

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi- automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from “ground truth” images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

Recent findings Cardiovascular disease (CVD) is the leading cause of mortality and poses challenges for healthcare providers globally. Risk-based approaches for the management of CVD are becoming popular for recommending treatment plans for asymptomatic individuals. Several conventional predictive CVD risk models based do not provide an accurate CVD risk assessment for patients with different baseline risk profiles. Artificial intelligence (AI) algorithms have changed the landscape of CVD risk assessment and demonstrated a better performance when compared against conventional models, mainly due to its ability to handle the input nonlinear variations. Further, it has the flexibility to add risk factors derived from medical imaging modalities that image the morphology of the plaque. The integration of noninvasive carotid ultrasound image-based phenotypes with conventional risk factors in the AI framework has further provided stronger power for CVD risk prediction, so-called “integrated predictive CVD risk models.” Purpose of the review: The objective of this review is (i) to understand several aspects in the development of predictive CVD risk models, (ii) to explore current conventional predictive risk models and their successes and challenges, and (iii) to refine the search for predictive CVD risk models using noninvasive carotid ultrasound as an exemplar in the artificial intelligence-based framework. Conclusion Conventional predictive CVD risk models are suboptimal and could be improved. This review examines the potential to include more noninvasive image-based phenotypes in the CVD risk assessment using powerful AI-based strategies.

Background: Recently, a 10-year image-based integrated calculator (called AtheroEdge Composite Risk Score-AECRS1.0) was developed which combines conventional cardiovascular risk factors (CCVRF) with image phenotypes derived from carotid ultrasound (CUS). Such calculators did not include chronic kidney disease (CKD)-based biomarker called estimated glomerular filtration rate (eGFR). The novelty of this study is to design and develop an advanced integrated version called- AECRS2.0 that combines eGFR with image phenotypes to compute the composite risk score. Furthermore, AECRS2.0 was benchmarked against QRISK3 which considers eGFR for risk assessment. Methods: The method consists of three major steps: 1) five, current CUS image phenotypes (CUSIP) measurements using AtheroEdge system (AtheroPoint, CA, USA) consisting of: average carotid intima- media thickness (cIMTave), maximum cIMT (cIMTmax), minimum cIMT (cIMTmin), variability in cIMT (cIMTV), and total plaque area (TPA) ; 2) five, 10-year CUSIP measurements by combining these current five CUSIP with 11 CCVRF (age, ethnicity, gender, body mass index, systolic blood pressure, smoking, carotid artery type, hemoglobin, low- density lipoprotein cholesterol, total cholesterol, and eGFR) ; 3) AECRS2.0 risk score computation and its comparison to QRISK3 using area-under-the-curve (AUC). Results: South Asian- Indian 339 patients were retrospectively analyzed by acquiring their left/right common carotid arteries (678 CUS, mean age: 54.25±9.84 years ; 75.22% males ; 93.51% diabetic with HbA1c ≥6.5% ; and mean eGFR 73.84±20.91 mL/min/1.73m2). The proposed AECRS2.0 reported higher AUC (AUC=0.89, P