Kitas, George D.

Purpose: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. Methods: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. Summary: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

(1) Background: COVID-19 computed tomography (CT) lung segmentation is critical for COVID lung severity diagnosis. Earlier proposed approaches during 2020–2021 were semiautomated or automated but not accurate, user-friendly, and industry-standard benchmarked. The proposed study compared the COVID Lung Image Analysis System, COVLIAS 1.0 (GBTI, Inc., and AtheroPointTM, Roseville, CA, USA, referred to as COVLIAS), against MedSeg, a web-based Artificial Intelligence (AI) segmentation tool, where COVLIAS uses hybrid deep learning (HDL) models for CT lung segmentation. (2) Materials and Methods: The proposed study used 5000 ITALIAN COVID-19 positive CT lung images collected from 72 patients (experimental data) that confirmed the reverse transcription-polymerase chain reaction (RT-PCR) test. Two hybrid AI models from the COVLIAS system, namely, VGG-SegNet (HDL 1) and ResNet-SegNet (HDL 2), were used to segment the CT lungs. As part of the results, we compared both COVLIAS and MedSeg against two manual delineations (MD 1 and MD 2) using (i) Bland–Altman plots, (ii) Correlation coefficient (CC) plots, (iii) Receiver operating characteristic curve, and (iv) Figure of Merit and (v) visual overlays. A cohort of 500 CROATIA COVID-19 positive CT lung images (validation data) was used. A previously trained COVLIAS model was directly applied to the validation data (as part of Unseen-AI) to segment the CT lungs and compare them against MedSeg. (3) Result: For the experimental data, the four CCs between COVLIAS (HDL 1) vs. MD 1, COVLIAS (HDL 1) vs. MD 2, COVLIAS (HDL 2) vs. MD 1, and COVLIAS (HDL 2) vs. MD 2 were 0.96, 0.96, 0.96, and 0.96, respectively. The mean value of the COVLIAS system for the above four readings was 0.96. CC between MedSeg vs. MD 1 and MedSeg vs. MD 2 was 0.98 and 0.98, respectively. Both had a mean value of 0.98. On the validation data, the CC between COVLIAS (HDL 1) vs. MedSeg and COVLIAS (HDL 2) vs. MedSeg was 0.98 and 0.99, respectively. For the experimental data, the difference between the mean values for COVLIAS and MedSeg showed a difference of <2.5%, meeting the standard of equivalence. The average running times for COVLIAS and MedSeg on a single lung CT slice were ~4 s and ~10 s, respectively. (4) Conclusions: The performances of COVLIAS and MedSeg were similar. However, COVLIAS showed improved computing time over MedSeg.

Background: Atherosclerosis is the primary cause of the cardiovascular disease (CVD). Several risk factors lead to atherosclerosis, and altered nutrition is one among those. Nutrition has been ignored quite often in the process of CVD risk assessment. Altered nutrition along with carotid ultrasound imaging-driven atherosclerotic plaque features can help in understanding and banishing the problems associated with the late diagnosis of CVD. Artificial intelligence (AI) is another promisingly adopted technology for CVD risk assessment and management. Therefore, we hypothesize that the risk of atherosclerotic CVD can be accurately monitored using carotid ultrasound imaging, predicted using AI-based algorithms, and reduced with the help of proper nutrition. Layout: The review presents a pathophysiological link between nutrition and atherosclerosis by gaining a deep insight into the processes involved at each stage of plaque development. After targeting the causes and finding out results by low-cost, user-friendly, ultrasound-based arterial imaging, it is important to (i) stratify the risks and (ii) monitor them by measuring plaque burden and computing risk score as part of the preventive framework. Artificial intelligence (AI)-based strategies are used to provide efficient CVD risk assessments. Finally, the review presents the role of AI for CVD risk assessment during COVID-19. Conclusions: By studying the mechanism of low-density lipoprotein formation, saturated and trans fat, and other dietary components that lead to plaque formation, we demonstrate the use of CVD risk assessment due to nutrition and atherosclerosis disease formation during normal and COVID times. Further, nutrition if included, as a part of the associated risk factors can benefit from atherosclerotic disease progression and its management using AI-based CVD risk assessment.

Background: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are evaluated. We propose a robust and stable inter-variability analysis of CT lung segmentation in COVID-19 to avoid the effect of bias. Methodology: The proposed inter-variability study consists of two GT tracers for lung segmentation on chest CT. Three AI models, PSP Net, VGG-SegNet, and ResNet-SegNet, were trained using GT annotations. We hypothesized that if AI models are trained on the GT tracings from multiple experience levels, and if the AI performance on the test data between these AI models is within the 5% range, one can consider such an AI model robust and unbiased. The K5 protocol (training to testing: 80%:20%) was adapted. Ten kinds of metrics were used for performance evaluation. Results: The database consisted of 5000 CT chest images from 72 COVID-19-infected patients. By computing the coefficient of correlations (CC) between the output of the two AI models trained corresponding to the two GT tracers, computing their differences in their CC, and repeating the process for all three AI-models, we show the differences as 0%, 0.51%, and 2.04% (all < 5%), thereby validating the hypothesis. The performance was comparable; however, it had the following order: ResNet-SegNet > PSP Net > VGG-SegNet. Conclusions: The AI models were clinically robust and stable during the inter-variability analysis on the CT lung segmentation on COVID-19 patients.

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 < 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 > VGG-SegNet > NIH > SegNet. The HDL runs in <1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Aim The objective was to examine the prevalence of atherosclerotic cardiovascular disease (ASCVD) and its risk factors among patients with RA with diabetes mellitus (RA-DM) and patients with RA without diabetes mellitus (RAwoDM), and to evaluate lipid and blood pressure (BP) goal attainment in RA-DM and RAwoDM in primary and secondary prevention. Methods The cohort was derived from the Survey of Cardiovascular Disease Risk Factors in Patients with Rheumatoid Arthritis from 53 centres/19 countries/3 continents during 2014–2019. We evaluated the prevalence of cardiovascular disease (CVD) among RA-DM and RAwoDM. The study population was divided into those with and without ASCVD, and within these groups we compared risk factors and CVD preventive treatment between RA-DM and RAwoDM. Results The study population comprised of 10 543 patients with RA, of whom 1381 (13%) had DM. ASCVD was present in 26.7% in RA-DM compared with 11.6% RAwoDM (p<0.001). The proportion of patients with a diagnosis of hypertension, hyperlipidaemia and use of lipid-lowering or antihypertensive agents was higher among RA-DM than RAwoDM (p<0.001 for all). The majority of patients with ASCVD did not reach the lipid goal of low-density lipoprotein cholesterol <1.8 mmol/L. The lipid goal attainment was statistically and clinically significantly higher in RA-DM compared with RAwoDM both for patients with and without ASCVD. The systolic BP target of <140 mm Hg was reached by the majority of patients, and there were no statistically nor clinically significant differences in attainment of BP targets between RA-DM and RAwoDM. Conclusion CVD preventive medication use and prevalence of ASCVD were higher in RA-DM than in RAwoDM, and lipid goals were also more frequently obtained in RA-DM. Lessons may be learnt from CVD prevention programmes in DM to clinically benefit patients with RA .

Abstract Aims To assess differences in estimated cardiovascular disease (CVD) risk among rheumatoid arthritis (RA) patients from different world regions and to evaluate the management and goal attainment of lipids and blood pressure (BP). Methods and results The survey of CVD risk factors in patients with RA was conducted in 14 503 patients from 19 countries during 2014–19. The treatment goal for BP was &lt;140/90 mmHg. CVD risk prediction and lipid goals were according to the 2016 European guidelines. Overall, 21% had a very high estimated risk of CVD, ranging from 5% in Mexico, 15% in Asia, 19% in Northern Europe, to 31% in Central and Eastern Europe and 30% in North America. Of the 52% with indication for lipid-lowering treatment (LLT), 44% were using LLT. The lipid goal attainment was 45% and 18% in the high and very high risk groups, respectively. Use of statins in monotherapy was 24%, while 1% used statins in combination with other LLT. Sixty-two per cent had hypertension and approximately half of these patients were at BP goal. The majority of the patients used antihypertensive treatment in monotherapy (24%), while 10% and 5% as a two- or three-drug combination. Conclusion We revealed considerable geographical differences in estimated CVD risk and preventive treatment. Low goal attainment for LLT was observed, and only half the patients obtained BP goal. Despite a high focus on the increased CVD risk in RA patients over the last decade, there is still substantial potential for improvement in CVD preventive measures.

Abstract Background/Introduction The realisation that subjects with rheumatoid arthritis (RA) are at increased risk of cardiovascular disease (CVD) has led to a growing interest in risk factor control in such people, but whether this has influenced the management of dyslipidaemia and hypertension (HT) is uncertain. Purpose To describe differences in lipid and blood pressure (BP) levels among patients with RA from five world regions. Furthermore, to evaluate attainment of guideline recommended targets for lipid lowering and antihypertensive treatment. Methods The SUrvey of CVD Risk Factors in patients with RA (SURF-RA) was conducted at 53 centres in 19 countries from 2014 to 2019. Data including demographics, RA disease characteristics, CVD comorbidity, risk factors and use of preventive treatment was collected. HT was defined as self-reported HT, and/or measured BP &gt;140/90 mmHg, and/or use of anti HT medication (a-HT). The treatment goal of a-HT was BP &lt;140/90 mmHg. The 10-year risk of a fatal CVD event was calculated by the European CVD risk calculator, the Systematic COronary Risk Evaluation (SCORE), and was thereafter multiplied with 1.5 as recommended by the European League Against Rheumatism. Patients were classified in a high or very high CVD risk group according to the 2012 European Society of Cardiology guidelines, with low density lipoprotein cholesterol (LDL-c) goal at &lt;2.6 and &lt;1.8 mmol/L, respectively. Results In total, 14503 RA patients were included. The mean age was 59.8±13.6 years, most of whom (74%) were female. Nearly 2/3 of the patients were hypertensive. Use of a-HT in the total population differed substantially between the cohorts with limited use in West Europe and Latin America (17.4% and 24.8%), in contrast to North America and East Europe (46.8% and 57.0%). On average, half of those with HT were at the recommended BP goal. The lowest BP goal attainment was seen in Asia, West and East Europe (40.8–43.1%), and the highest in North America (63.5%). Overall 51.5% had an indication for lipid lowering therapy (LLT), and of these only 43.5% were taking LLT. Only 34.0% of patients with an indication for LLT were at recommended LDL-c goals. The proportion of RA patients on target for LDL-c varied greatly between regions, from 23.1% in East Europe to 51.0% in North America. The LDL-c goal attainment was higher in RA patients at high risk (45.1%) compared to those at very high risk of CVD (18.0%). Conclusion(s) This large international survey on RA patients revealed considerable geographical differences in CVD preventive treatment. Only one half of subjects were at blood pressure goals, and achievement of lipid goals was even poorer at one third of those eligible for treatment, which is lower than what is reported for subjects with coronary heart disease. We conclude that there is a substantial need for improvement in CVD preventive measures in RA patients. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Unrestricted research collaboration with Lilly

Abstract Background The cardiovascular disease (CVD) risk in patients with rheumatoid arthritis (RA) is comparable to that of patients with diabetes mellitus (DM). Although several studies have indicated high prevalence's of DM in RA patients, little is known about how this affects their CVD risk. Objectives To examine indications for, and use of antihypertensive treatment (a-HT) and lipid-lowering therapy (LLT) in RA patients with DM (RA-DM) and RA patients without DM (RAwoDM). Further, to compare the prevalence of various types of CVD across RA-DM and RAwoDM. Methods The cohort was derived from the SUrvey of cardiovascular disease Risk Factor in patients with Rheumatoid Arthritis (SURF-RA), which was performed in 53 centres/17 countries in 5 world regions (West and East Europe; North and Latin America; and Asia) from 2014 - 2019. Indication for a-HT was defined as: 1) systolic/diastolic blood pressure (BP) ≥140/90 mm Hg, 2) self-reported hypertension, and/or 3) current use of a-HT. Indication for LLT was defined according to ESC guidelines. CVD risk estimates (by SCORE) were multiplied by 1.5 according to EULAR recommendations. Target treatment targets for BP and lipids were defined according to ESC guidelines applicable at the time data were recorded. Results Presence of comorbid DM was available in 10 602 (73.1%) of the 14 503 RA patients included in SURF-RA, of whom 75 and 1262 patients reported DM type 1 and type 2, respectively (total 1337 patients, 12.6%). Although less often current smokers, RA-DM patients were more often previous smokers, male sex and had higher body mass index compared to RAwoDM (p&lt;0.0001 for all). a-HT (84.7% vs 62.3%) and LLT (100% vs 47.2%) were more frequently indicated in RA-DM than in RAwoDM patients (p&lt;0.0001 for both). RA-DM were more likely than RAwoDM to receive a-HT on indication (60.4% vs 57.6%, p&lt;0.0001), while the difference in LLT use on indication was not significantly different (45.7% vs 42.5%, p=0.06). Moreover, RA-DM compared to RAwoDM patients had more often reached treatment goals when on a-HT (60.7% vs 54.1%, p&lt;0.0001) and LLT (62.8% vs 48.9%, p&lt;0.0001). Finally, the risk of all recorded established CVD (coronary heart disease, stroke, peripheral artery disease and atrial fibrillation) was increased by a factor of 2 to 3 in RA-DM compared to RAwoDM (Figure). Conclusion The effect of RA and comorbid DM on CVD risk appears to be additive. While CVD preventive medications are more often indicated in RA-DM than in RAwoDM patients, they are also more likely to receive such therapy and to reach CVD preventive treatment goals. The latter finding may be due to more developed CVD preventive care in DM compared to RA patients. Improved CVD preventive systems for patients with RA are warranted. CVD in RA patients with and without DM Funding Acknowledgement Type of funding source: Other. Main funding source(s): Lilly