A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework

A deep learning algorithm to identify carotid plaques and assess their stability

Background

Carotid plaques are major risk factors for stroke. Carotid ultrasound can help to assess the risk and incidence rate of stroke. However, large-scale carotid artery screening is time-consuming and laborious, the diagnostic results inevitably involve the subjectivity of the diagnostician to a certain extent. Deep learning demonstrates the ability to solve the aforementioned challenges. Thus, we attempted to develop an automated algorithm to provide a more consistent and objective diagnostic method and to identify the presence and stability of carotid plaques using deep learning.

Methods

A total of 3,860 ultrasound images from 1,339 participants who underwent carotid plaque assessment between January 2021 and March 2023 at the Shanghai Eighth People's Hospital were divided into a 4:1 ratio for training and internal testing. The external test included 1,564 ultrasound images from 674 participants who underwent carotid plaque assessment between January 2022 and May 2023 at Xinhua Hospital affiliated with Dalian University. Deep learning algorithms, based on the fusion of a bilinear convolutional neural network with a residual neural network (BCNN-ResNet), were used for modeling to detect carotid plaques and assess plaque stability. We chose AUC as the main evaluation index, along with accuracy, sensitivity, and specificity as auxiliary evaluation indices.

Results

Modeling for detecting carotid plaques involved training and internal testing on 1,291 ultrasound images, with 617 images showing plaques and 674 without plaques. The external test comprised 470 ultrasound images, including 321 images with plaques and 149 without. Modeling for assessing plaque stability involved training and internal testing on 764 ultrasound images, consisting of 494 images with unstable plaques and 270 with stable plaques. The external test was composed of 279 ultrasound images, including 197 images with unstable plaques and 82 with stable plaques. For the task of identifying the presence of carotid plaques, our model achieved an AUC of 0.989 (95% CI: 0.840, 0.998) with a sensitivity of 93.2% and a specificity of 99.21% on the internal test. On the external test, the AUC was 0.951 (95% CI: 0.962, 0.939) with a sensitivity of 95.3% and a specificity of 82.24%. For the task of identifying the stability of carotid plaques, our model achieved an AUC of 0.896 (95% CI: 0.865, 0.922) on the internal test with a sensitivity of 81.63% and a specificity of 87.27%. On the external test, the AUC was 0.854 (95% CI: 0.889, 0.830) with a sensitivity of 68.52% and a specificity of 89.49%.

Conclusion

Deep learning using BCNN-ResNet algorithms based on routine ultrasound images could be useful for detecting carotid plaques and assessing plaque instability.

Explore the value of carotid ultrasound radiomics nomogram in predicting ischemic stroke risk in patients with type 2 diabetes mellitus

Background and objective

Type 2 Diabetes Mellitus (T2DM) with insulin resistance (IR) is prone to damage the vascular endothelial, leading to the formation of vulnerable carotid plaques and increasing ischemic stroke (IS) risk. The purpose of this study is to develop a nomogram model based on carotid ultrasound radiomics for predicting IS risk in T2DM patients.

Methods

198 T2DM patients were enrolled and separated into study and control groups based on IS history. After manually delineating carotid plaque region of interest (ROI) from images, radiomics features were identified and selected using the least absolute shrinkage and selection operator (LASSO) regression to calculate the radiomics score (RS). A combinatorial logistic machine learning model and nomograms were created using RS and clinical features like the triglyceride-glucose index. The three models were assessed using area under curve (AUC) and decision curve analysis (DCA).

Results

Patients were divided into the training set and the testing set by the ratio of 0.7. 4 radiomics features were selected. RS and clinical variables were all statically significant in the training set and were used to create a combination model and a prediction nomogram. The combination model (radiomics + clinical nomogram) had the largest AUC in both the training set and the testing set (0.898 and 0.857), and DCA analysis showed that it had a higher overall net benefit compared to the other models.

Conclusions

This study created a carotid ultrasound radiomics machine-learning-based IS risk nomogram for T2DM patients with carotid plaques. Its diagnostic performance and clinical prediction capabilities enable accurate, convenient, and customized medical care.

Artificial intelligence applied in cardiovascular disease: a bibliometric and visual analysis

Background

With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including cardiovascular disease. Facts have proved that AI has broad application prospects in rapid and accurate diagnosis.

Objective

This study mainly summarizes the research on the application of AI in the field of cardiovascular disease through bibliometric analysis and explores possible future research hotpots.

Methods

The articles and reviews regarding application of AI in cardiovascular disease between 2000 and 2023 were selected from Web of Science Core Collection on 30 December 2023. Microsoft Excel 2019 was applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 6.2.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.

Results

A total of 4,611 articles were selected in this study. AI-related research on cardiovascular disease increased exponentially in recent years, of which the USA was the most productive country with 1,360 publications, and had close cooperation with many countries. The most productive institutions and researchers were the Cedar sinai medical center and Acharya, Ur. However, the cooperation among most institutions or researchers was not close even if the high research outputs. Circulation is the journal with the largest number of publications in this field. The most important keywords are "classification", "diagnosis", and "risk". Meanwhile, the current research hotpots were "late gadolinium enhancement" and "carotid ultrasound".

Conclusions

AI has broad application prospects in cardiovascular disease, and a growing number of scholars are devoted to AI-related research on cardiovascular disease. Cardiovascular imaging techniques and the selection of appropriate algorithms represent the most extensively studied areas, and a considerable boost in these areas is predicted in the coming years.

Clinical significance of carotid intima-media thickness and plasma homocysteine in acute ST-segment elevation myocardial infarction

Background

Patients with acute ST-segment elevation myocardial infarction (STEMI) often have fewer identifiable traditional risk factors compared to other types of acute coronary syndrome. Therefore, it is necessary to explore more sensitive predictive models different from traditional cardiovascular scoring systems to identify high-risk populations. The retrospective case-control study aimed to investigate the predictive value of carotid intima-media thickness (CIMT) and homocysteine (Hcy) on the occurrence of STEMI.

Methods

A total of 198 patients with first STEMI were continuously selected into the observation group, who received emergency coronary angiography in Hefei Hospital Affiliated to Anhui Medical University from January 2020 to January 2022, and a total of 129 patients with chest pain and chest tightness who received coronary angiography to exclude significant coronary artery disease were selected as the control group in the above hospitals during the same period. Hcy was biochemical index determined by fasting blood sampling within 48 h after admission, while CIMT and carotid plaque was measured using ultrasound. Univariate and multivariate logistic regression analysis was used to screen out independent risk factors including Hcy, CIMT and carotid plaque of STEMI. On the basis of traditional risk factors, Hcy, CIMT and carotid plaque were introduced in order to form different combined diagnosis models. The receiver operating characteristic (ROC) curve of single indicator and multi-indicator combined diagnosis were plotted to evaluate the clinical usefulness of the study factors or diagnostic models. Based on those, a Nomogram was constructed to predict STEMI.

Results

Hcy (OR =1.161, 95% CI: 1.084-1.244, P<0.001), CIMT (OR =206.968, 95% CI: 22.375-1,914.468, P<0.001), carotid plaque (OR =2.499, 95% CI: 1.214-5.142, P=0.013) were independent risk factors for STEMI (P<0.01). ROC results suggested that the area under the curve (AUC) of Hcy was 0.729, the optimal cut-off value was 13.525 µmol/L. The AUC of CIMT is 0.763, and the optimal cut-off value is 0.875mm. Combined with the independent predictors including smoking, diabetes, high density lipoprotein cholesterol, low density lipoprotein cholesterol, Hcy, CIMT, carotid plaque, the AUC of the diagnosis model was 0.892 (95% CI: 0.856-0.928, P<0.001). Based on the above results, a Nomogram for predicting STEMI was constructed with a C-index of 0.892. The results of the H-L fitting test show that χ2=1.5049, df=2, P=0.4712; the calibration curve of the Nomogram is close to the ideal curve, and the internal validation C-index was 0.880. The clinical decision curve analysis (DCA) shows that the "nomogram line" of the model is far from the "All line" and the "None line".

Conclusions

Hcy, CIMT, and carotid artery plaque could be independent risk factors of STEMI. The inclusion of these factors in addition to traditional risk factors can more fully and accurately predict the risk of STEMI. The Nomogram based on the results of this study is feasible and can bring clinical net benefit.

The effects of curcumin-piperine supplementation on inflammatory, oxidative stress and metabolic indices in patients with ischemic stroke in the rehabilitation phase: a randomized controlled trial

BACKGROUND

Stroke is a leading cause of death worldwide, which is associated with a heavy economic and social burden. The purpose of this study was to investigate the effects of supplementation with curcumin-piperine combination in patients with ischemic stroke in the rehabilitation stage.

METHODS

In this randomized controlled trial, 66 patients with stroke were randomized into two groups receiving curcumin-piperine tablets (500 mg curcumin + 5 mg piperine) and matched placebo tablets for 12 weeks. High-sensitivity C-reactive protein (hs-CRP), carotid intima-media thickness (CIMT), thrombosis, total antioxidant capacity (TAC), lipid profile, anthropometric indices, blood pressure, and quality of life were assessed before and after the intervention. Statistical data analysis was done using SPSS22 software.

RESULTS

A total of 56 patients with a mean age of 59.80 ± 4.25 years completed the trial. Based on ANCOVA test, adjusted for baseline values, curcumin-piperine supplementation for 12 weeks resulted in significant reductions in serum levels of hs-CRP (p = 0.026), total cholesterol (TC) (p = 0.009), triglycerides (TG) (p = 0.001), CIMT (p = 0.002), weight (P = 0.001), waist circumference (p = 0.024), and systolic and diastolic blood pressure (p < 0.001), and a significant increase in TAC (p < 0.001) in comparison to the placebo. Pain score significantly increased in both groups; however, its increase was significantly higher in the placebo group compared with the intervention group (p = 0.007). No significant changes were observed between the two groups in terms of serum fibrinogen, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and quality of life indices.

CONCLUSION

Curcumin-piperine supplementation had beneficial effects on CIMT, serum hs-CRP, TC, TG, TAC, and systolic and diastolic blood pressure in patients with ischemic stroke in the rehabilitation stage.

Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look

BACKGROUND AND MOTIVATION

Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias.

METHODS

Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework.

FINDINGS AND CONCLUSIONS

UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.

Association between soft drink consumption and carotid atherosclerosis in a large-scale adult population: The TCLSIH cohort study

BACKGROUND AND AIMS

Carotid atherosclerosis indicates an increased risk for cardiac-cerebral vascular disease. Given the pattern of consumption in China, sugar-sweetened beverage is the main type of soft drink consumed. As soft drinks contain a high amount of fructose, they may be a risk factor of carotid atherosclerosis. A prospective cohort study was conducted to investigate the association between soft drink consumption and the incidence of carotid atherosclerosis in a Chinese adult population.

METHODS AND RESULTS

A total of 3828 participants (men: 2007 and women: 1821) were included. Carotid atherosclerosis was measured by using ultrasonography and was defined by increased carotid intima-media thickness and/or carotid plaques. Soft drink consumption was assessed using a validated food frequency questionnaire. Cox proportional hazards regression analysis was used to assess the association of soft drink consumption categories with the incidence of carotid atherosclerosis. During a mean follow-up of 3.20 years, 1009 individuals of the 3828 eligible participants developed carotid atherosclerosis. After adjusting for potential confounding factors, we compared the higher levels to the lowest level of soft drink consumption in women, and we estimated the multivariable hazard ratios and 95% confidence intervals of incident carotid atherosclerosis to be 1.09 (0.80, 1.50), and 1.56 (1.14, 2.13) (P for trend <0.05). However, there was no significant association between soft drink consumption and the incidence of carotid atherosclerosis in men or total population.

CONCLUSION

The result indicated that soft drink consumption was associated with a higher incidence of carotid atherosclerosis in women.

TRIAL REGISTERED

UMIN Clinical Trials Registry.

TRIAL REGISTRATION NUMBER

UMIN000027174. TRIAL REGISTRATION WEBSITE: https://upload.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000031137.

Comparison of the intima-media thickness of the common carotid artery in patients with rheumatoid arthritis: A single-center cross-sectional case-control study, and a brief review of the literature

Background and Aim

Rheumatoid arthritis (RA) is an autoimmune chronic inflammatory disease affecting 0.5%-1% of adults worldwide. The carotid intima-media thickness (CIMT) is a simple, reliable, noninvasive marker for subclinical atherosclerosis. The aim of this study was to compare the intima-media thickness of the common carotid artery in patients with RA with that of healthy patients.

Methods

In this case-control study, subjects were recruited from the patients who presented to a private rheumatology clinic. RA was documented by a rheumatologist. All subjects underwent an ultrasound examination of the carotid artery to assess CIMT. Subjects with RA filled out the disease activity score (DAS28) questionnaire.

Results

Sixty-two subjects (31 subjects with RA and 31 healthy subjects) took part in the study. The mean age of the subjects in the RA and the control groups was 42.39 ± 12.98 and 44.48 ± 13.56 years, respectively. Values of CIMT were significantly greater in RA subjects compared with their healthy counterparts (p < 0.001). The CIMT increased significantly with increased disease severity (r = 0.73). Subjects were divided into two age groups (≤40 and >40 years). A comparison of CIMT in the mentioned subgroups revealed a remarkable difference in CIMT values between those of the RA patients and those of their control counterparts in both age groups (p = 0.002 and p < 0.001 for those below and above 40 years, respectively).

Conclusion

CIMT could be used as an efficient clinical index for identifying the early stages of atherosclerosis and predicting cardiovascular events following atherosclerosis in RA patients.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

Comparison of ultrasonic shear wave elastography, AngioPLUS planewave ultrasensitive imaging, and optimized high-resolution magnetic resonance imaging in evaluating carotid plaque stability

Objective

This study aimed to compare the efficiency of evaluating carotid plaque stability using ultrasonic shear wave elastography (SWE), AngioPLUS planewave ultrasensitive imaging (AP), and optimized high-resolution magnetic resonance imaging (MRI).

Methods

A total of 100 patients who underwent carotid endarterectomy at our hospital from October 2019 to August 2022 were enrolled. Based on the final clinical diagnosis, these patients were divided into vulnerable (n = 62) and stable (n = 38) plaque groups. All patients were examined using ultrasound SWE, AP, and optimized high-resolution MRI before surgery. The clinical data and ultrasound characteristics of patients of the two groups were compared. Considering the final clinical diagnosis as the gold standard, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of SWE, AP, high-resolution MRI, and the final clinical diagnosis of vulnerable plaque were calculated. Pearson's correlation test was used to analyze the correlations of AP, SWE, and MRI results with the grading results of carotid artery stenosis.

Results

Statistically significant differences were noticed in terms of the history of smoking and coronary heart disease, plaque thickness, surface rules, calcified nodules, low echo area, and the degree of carotid artery stenosis between the two groups (P < 0.05). Considering the final clinical diagnosis as the gold standard, the sensitivity, specificity, PPV, and NPV of SWE-based detection of carotid artery vulnerability were 87.10% (54/62), 76.32% (29/38), 85.71% (54/63) and 78.38% (29/37), respectively, showing a general consistency with the final clinical results (Kappa = 0.637, P < 0.05). Considering the final clinical diagnosis as the gold standard, the sensitivity, specificity, PPV and NPV of AP-based detection of carotid artery vulnerability were 93.55% (58/62), 84.21% (32/38), 90.63% (58/64), and 88.89% (32/36), respectively, which agreed with the final clinical detection results (Kappa = 0.786, P < 0.05). Considering the final clinical diagnosis as the gold standard, the sensitivity, specificity, PPV and NPV of high-resolution MRI-based detection of carotid artery vulnerability were 88.71% (55/62), 78.95% (30/38), 87.30% (55/63), and 81.08% (30/37), respectively, showing consistency with the final clinical results (Kappa = 0.680, P < 0.05). AP, SWE, and MRI results were positively correlated with the results of carotid artery stenosis grading (P < 0.05).

Conclusion

AP technology is a non-invasive, inexpensive, and highly sensitive method to evaluate the stability of carotid artery plaques. This method can dynamically display the flow of blood in new vessels of plaque in real time and provide a reference for clinical diagnosis and treatment.

Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence

Artificial intelligence (AI)-based technologies have garnered interest across a range of disciplines in the past several years, with an even more recent interest in various health care fields, including Vascular Surgery. AI offers a unique ability to analyze health data more quickly and efficiently than could be done by humans alone and can be used for clinical applications such as diagnosis, risk stratification, and follow-up, as well as patient-used applications to improve both patient and provider experiences, mitigate health care disparities, and individualize treatment. As with all novel technologies, AI is not without its risks and carries with it unique ethical considerations that will need to be addressed before its broad integration into health care systems. AI has the potential to revolutionize the way care is provided to patients, including those requiring vascular care.

Ultrasound-Based Image Analysis for Predicting Carotid Artery Stenosis Risk: A Comprehensive Review of the Problem, Techniques, Datasets, and Future Directions

The carotid artery is a major blood vessel that supplies blood to the brain. Plaque buildup in the arteries can lead to cardiovascular diseases such as atherosclerosis, stroke, ruptured arteries, and even death. Both invasive and non-invasive methods are used to detect plaque buildup in the arteries, with ultrasound imaging being the first line of diagnosis. This paper presents a comprehensive review of the existing literature on ultrasound image analysis methods for detecting and characterizing plaque buildup in the carotid artery. The review includes an in-depth analysis of datasets; image segmentation techniques for the carotid artery plaque area, lumen area, and intima-media thickness (IMT); and plaque measurement, characterization, classification, and stenosis grading using deep learning and machine learning. Additionally, the paper provides an overview of the performance of these methods, including challenges in analysis, and future directions for research.

BSMNet: Boundary-salience multi-branch network for intima-media identification in carotid ultrasound images

Carotid artery intima-media thickness (CIMT) is an essential factor in signaling the risk of cardiovascular diseases, which is commonly evaluated using ultrasound imaging. However, automatic intima-media segmentation and thickness measurement are still challenging due to the boundary ambiguity of intima-media and inherent speckle noises in ultrasound images. In this work, we propose an end-to-end boundary-salience multi-branch network, BSMNet, to tackle the carotid intima-media identification from ultrasound images, where the prior shape knowledge and anatomical dependence are exploited using a parallel linear structure learning modules followed by a boundary refinement module. Moreover, we design a strip attention model to boost the thin strip region segmentation with shape priors, in which an anisotropic kernel shape captures long-range global relations and scrutinizes meaningful local salient contexts simultaneously. Extensive experimental results on an in-house carotid ultrasound (US) dataset demonstrate the promising performance of our method, which achieves about 0.02 improvement in Dice and HD95 than other state-of-the-art methods. Our method is promising in advancing the analysis of systemic arterial disease with ultrasound imaging.

Contrast U-Net driven by sufficient texture extraction for carotid plaque detection

Ischemic heart disease or stroke caused by the rupture or dislodgement of a carotid plaque poses a huge risk to human health. To obtain accurate information on the carotid plaque characteristics of patients and to assist clinicians in the determination and identification of atherosclerotic areas, which is one significant foundation work. Existing work in this field has not deliberately extracted texture information of carotid from the ultrasound images. However, texture information is a very important part of carotid ultrasound images. To make full use of the texture information in carotid ultrasound images, a novel network based on U-Net called Contrast U-Net is designed in this paper. First, the proposed network mainly relies on a contrast block to extract accurate texture information. Moreover, to make the network better learn the texture information of each channel, the squeeze-and-excitation block is introduced to assist in the jump connection from encoding to decoding. Experimental results from intravascular ultrasound image datasets show that the proposed network can achieve superior performance compared with other popular models in carotid plaque detection.

NAG-Net: Nested attention-guided learning for segmentation of carotid lumen-intima interface and media-adventitia interface

Cardiovascular diseases (CVD), as the leading cause of death in the world, poses a serious threat to human health. The segmentation of carotid Lumen-intima interface (LII) and Media-adventitia interface (MAI) is a prerequisite for measuring intima-media thickness (IMT), which is of great significance for early screening and prevention of CVD. Despite recent advances, existing methods still fail to incorporate task-related clinical domain knowledge and require complex post-processing steps to obtain fine contours of LII and MAI. In this paper, a nested attention-guided deep learning model (named NAG-Net) is proposed for accurate segmentation of LII and MAI. The NAG-Net consists of two nested sub-networks, the Intima-Media Region Segmentation Network (IMRSN) and the LII and MAI Segmentation Network (LII-MAISN). It innovatively incorporates task-related clinical domain knowledge through the visual attention map generated by IMRSN, enabling LII-MAISN to focus more on the clinician's visual focus region under the same task during segmentation. Moreover, the segmentation results can directly obtain fine contours of LII and MAI through simple refinement without complicated post-processing steps. To further improve the feature extraction ability of the model and reduce the impact of data scarcity, the strategy of transfer learning is also adopted to apply the pretrained weights of VGG-16. In addition, a channel attention-based encoder feature fusion block (EFFB-ATT) is specially designed to achieve efficient representation of useful features extracted by two parallel encoders in LII-MAISN. Extensive experimental results have demonstrated that our proposed NAG-Net outperformed other state-of-the-art methods and achieved the highest performance on all evaluation metrics.

Artificial intelligence applied in pulmonary hypertension: a bibliometric analysis

Introduction

Advances in Artificial Intelligence (AI) offer new Information Technology (IT) opportunities in various applications and fields (industry, health, etc.). The medical informatics scientific community expends tremendous effort on the management of diseases affecting vital organs making it a complex disease (lungs, heart, brain, kidneys, pancreas, and liver). Scientific research becomes more complex when several organs are simultaneously affected, as is the case with Pulmonary Hypertension (PH), which affects both the lungs and the heart. Therefore, early detection and diagnosis of PH are essential to monitor the disease's progression and prevent associated mortality.

Method

The issue addressed relates to knowledge of recent developments in AI approaches applied to PH. The aim is to provide a systematic review through a quantitative analysis of the scientific production concerning PH and the analysis of the networks of this production. This bibliometric approach is based on various statistical, data mining, and data visualization methods to assess research performance using scientific publications and various indicators (e.g., direct indicators of scientific production and scientific impact).

Results

The main sources used to obtain citation data are the Web of Science Core Collection and Google Scholar. The results indicate a diversity of journals (e.g., IEEE Access, Computers in Biology and Medicine, Biology Signal Processing and Control, Frontiers in Cardiovascular Medicine, Sensors) at the top of publications. The most relevant affiliations are universities from United States of America (Boston Univ, Harvard Med Sch, Univ Oxford, Stanford Univ) and United Kingdom (Imperial Coll London). The most cited keywords are "Classification", "Diagnosis", "Disease", "Prediction", and "Risk".

Conclusion

This bibliometric study is a crucial part of the review of the scientific literature on PH. It can be viewed as a guideline or tool that helps researchers and practitioners to understand the main scientific issues and challenges of AI modeling applied to PH. On the one hand, it makes it possible to increase the visibility of the progress made or the limits observed. Consequently, it promotes their wide dissemination. Furthermore, it offers valuable assistance in understanding the evolution of scientific AI activities applied to managing the diagnosis, treatment, and prognosis of PH. Finally, ethical considerations are described in each activity of data collection, treatment, and exploitation to preserve patients' legitimate rights.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

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.

Effectiveness of exercise in reducing cerebral stroke risk factors: A systematic review and meta-analysis

OBJECTIVES

This study aimed to explore the relationship between exercise and cerebral stroke and provide evidence for the prevention of cerebral stroke.

MATERIALS/METHODS

All clinical trials of exercise intervention for atherosclerosis were systematically reviewed. Five major databases were searched to retrieve relevant studies from their inception to May 2022. According to the magnitude of heterogeneity, the random and fixed-effect models were used to test reasonably.

RESULTS

According to the inclusion and exclusion criteria, 1341 articles were screened and 13 articles involving 825 patients were identified. The result showed that in the randomized controlled trials carotid intima-media thickness index was lower in the exercise group (-0.04 mm, 95% confidence interval: -0.07 to -0.01). All were statistically significant (P < .005) and subgroup analysis showed that the intervention period and paper quality are sources of heterogeneity.

CONCLUSIONS

The results of this systematic review suggest that exercise is associated with a slow increase in carotid intima-media thickness, which may provide evidence that exercise helps reduce cerebral stroke.

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.

An end-to-end framework for intima media measurement and atherosclerotic plaque detection in the carotid artery

BACKGROUND AND OBJECTIVES

The detection and delineation of atherosclerotic plaque are usually manually performed by medical experts on the carotid artery. Evidence suggests that this manual process is subject to errors and has a large variability between experts, equipment, and datasets. This paper proposes a robust end-to-end framework for automatic atherosclerotic plaque detection.

METHODS

The proposed framework is composed of: (1) a semantic segmentation model based on U-Net, with EfficientNet as the backbone, that obtains a segmentation mask with the carotid intima-media region; and (2) a convolutional neural network designed using Bayesian optimization that simultaneously performs a regression to get the average and maximum carotid intima media thickness, and a classification to determine the presence of plaque.

RESULTS

Our approach improves the state-of-the-art in both co and bulb territories in the REGICOR database, with more than 8000 images, while providing predictions in real-time. The correlation coefficient was 0.89 in the common carotid artery and 0.74 for bulb region, and the F1 score for atherosclerotic plaque detecting was 0.60 and 0.59, respectively. The experimentation carried out includes a comparison with other fully automatic methods for carotid intima media thickness estimation found in the literature. Additionally, we present an extensive experimental study to evaluate the robustness of our proposal, as well as its suitability and efficiency compared to different versions of the framework.

CONCLUSIONS

The proposed end-to-end framework significantly improves the automatic characterization of atherosclerotic plaque. The generation of the segmented mask can be helpful for practitioners since it allows them to evaluate and interpret the model's results by visual inspection. Furthermore, the proposed framework overcomes the limitations of previous research based on ad-hoc post-processing, which could lead to overestimations in the case of oblique forms of the carotid artery.

Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients

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.

Automated deep learning-based paradigm for high-risk plaque detection in B-mode common carotid ultrasound scans: an asymptomatic Japanese cohort study

BACKGROUND

The death due to stroke is caused by embolism of the arteries which is due to the rupture of the atherosclerotic lesions in carotid arteries. The lesion formation is over time, and thus, early screening is recommended for asymptomatic and moderate-risk patients. The previous techniques adopted conventional methods or semi-automated and, more recently, machine learning solutions. A handful of studies have emerged based on solo deep learning (SDL) models such as UNet architecture.

METHODS

The proposed research is the first to adopt hybrid deep learning (HDL) artificial intelligence models such as SegNet-UNet. This model is benchmarked against UNet and advanced conventional models using scale-space such as AtheroEdge 2.0 (AtheroPoint, CA, USA). All our resultant statistics of the three systems were in the order of UNet, SegNet-UNet, and AtheroEdge 2.0.

RESULTS

Using the database of 379 ultrasound scans from a Japanese cohort of 190 patients having moderate risk and implementing the cross-validation deep learning framework, our system performance using area-under-the-curve (AUC) for UNet, SegNet-UNet, and AtheroEdge 2.0 were 0.93, 0.94, and 0.95 (p<0.001), respectively. The coefficient of correlation between the three systems and ground truth (GT) were: 0.82, 0.89, and 0.85 (p<0.001 for all three), respectively. The mean absolute area error for the three systems against manual GT was 4.07±4.70 mm2, 3.11±3.92 mm2, 3.72±4.76 mm2, respectively, proving the superior performance SegNet-UNet against UNet and AtheroEdge 2.0, respectively. Statistical tests were also conducted for their reliability and stability.

CONCLUSIONS

The proposed study demonstrates a fast, accurate, and reliable solution for early detection and quantification of plaque lesions in common carotid artery ultrasound scans. The system runs on a test US image in < 1 second, proving overall performance to be clinically reliable.

Vitamin D Deficiency and Gender Alter Vasoconstrictor and Vasodilator Reactivity in Rat Carotid Artery

The vitamin-D-sensitivity of the cardiovascular system may show gender differences. The prevalence of vitamin D (VD) deficiency (VDD) is high, and it alters cardiovascular function and increases the risk of stroke. Our aim was to investigate the vascular reactivity and histological changes of isolated carotid artery of female and male rats in response to different VD supplies. A total of 48 male and female Wistar rats were divided into four groups: female VD supplemented, female VDD, male VD supplemented, male VDD. The vascular function of isolated carotid artery segments was examined by wire myography. Both vitamin D deficiency and male gender resulted in increased phenylephrine-induced contraction. Acetylcholine-induced relaxation decreased in male rats independently from VD status. Inhibition of prostanoid signaling by indomethacin reduced contraction in females, but increased relaxation ability in male rats. Functional changes were accompanied by VDD and gender-specific histological alterations. Elastic fiber density was significantly decreased by VDD in female rats, but not in males. Smooth muscle actin and endothelial nitric oxide synthase levels were significantly lowered, but the thromboxane receptor was elevated in VDD males. Decreased nitrative stress was detected in both male groups independently from VD supply. The observed interactions between vitamin D deficiency and sex may play a role in the gender difference of cardiovascular risk.

Effect of Despeckling Filters on the Segmentation of Ultrasound Common Carotid Artery Images

Background

Carotid intima-media thickness (IMT) measured in B-mode ultrasound image is an important indicator of Atherosclerosis disease. Speckle noise inherently present in ultrasounds’ thereby degrades the visual evaluation and limits the automated segmentation performance. The objective of this study is to investigate the effects of three despeckle filters on the segmentation of carotid IMT in ultrasound image.

Methods

Automated segmentation of IMT is achieved by utilizing fast fuzzy c-mean clustering and distance-regularized level set without re-initialization techniques. Manual segmentation has been done by an experienced radiologist. The performances of median, hybrid median and improved adaptive complex diffusion (IACDF) filters are examined and a quantitative and qualitative comparison among these filters has been reported on 151 DICOM images. Bland–Altman plots were used to compare IMT results of these filters.

Furthermore, performances of above three filters are evaluated under different noise levels by individually adding speckle and salt and pepper noise in ten randomly selected images from 151 DICOM dataset. Plots between noise and quality evaluation metric parameters are used to compare de-noising performance of these filters.

Results

The average processing time per image of proposed IMT measurement technique without-filter and with filter is approx 15.39 s max.

Conclusion

It is shown that the median filter (window 5 × 5) measures better than hybrid median and IACDF filters. Finally, concluded that de-noising of ultrasound image before segmentation procedure certainly improves segmentation accuracy. Furthermore, it is observed that these filters do not impose serious computational burden and entail moderate processing time.