Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most

Risk factors for cardiovascular disease in patients with metabolic-associated fatty liver disease:a machine learning approach

BACKGROUND: Nonalcoholic fatty liver disease is associated with an increased cardiovascular disease (CVD) risk, although the exact mechanism(s) are less clear. Moreover, the relationship between newly redefined metabolic-associated fatty liver disease (MAFLD) and CVD risk has been poorly investigated. Data-driven machine learning (ML) techniques may be beneficial in discovering the most important risk factors for CVD in patients with MAFLD. METHODS: In this observational study, the patients with MAFLD underwent subclinical atherosclerosis assessment and blood biochemical analysis. Patients were split into two groups based on the presence of CVD (defined as at least one of the following: coronary artery disease; myocardial infarction; coronary bypass grafting; stroke; carotid stenosis; lower extremities artery stenosis). The ML techniques were utilized to construct a model which could identify individuals with the highest risk of CVD. We exploited the multiple logistic regression classifier operating on the most discriminative patient’s parameters selected by univariate feature ranking or extracted using principal component analysis (PCA). Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for the investigated classifiers, and the optimal cut-point values were extracted from the ROC curves using the Youden index, the closest to (0, 1) criteria and the Index of Union methods. RESULTS: In 191 patients with MAFLD (mean age: 58, SD: 12 years; 46% female), there were 47 (25%) patients who had the history of CVD. The most important clinical variables included hypercholesterolemia, the plaque scores, and duration of diabetes. The five, ten and fifteen most discriminative parameters extracted using univariate feature ranking and utilized to fit the ML models resulted in AUC of 0.84 (95% confidence interval [CI]: 0.77–0.90, p < 0.0001), 0.86 (95% CI 0.80–0.91, p < 0.0001) and 0.87 (95% CI 0.82–0.92, p < 0.0001), whereas the classifier fitted over 10 principal components extracted using PCA followed by the parallel analysis obtained AUC of 0.86 (95% CI 0.81–0.91, p < 0.0001). The best model operating on 5 most discriminative features correctly identified 114/144 (79.17%) low-risk and 40/47 (85.11%) high-risk patients. CONCLUSION: A ML approach demonstrated high performance in identifying MAFLD patients with prevalent CVD based on the easy-to-obtain patient parameters

Radiomics and Machine Learning in the Prediction of Cardiovascular Disease

Carotid atherosclerosis is a major risk factor for ischaemic stroke which is a leading cause of death worldwide. For stroke survivors, 1 in 4 will have another stroke within five years. Carotid CT angiography (CTA) is commonly performed following an ischaemic stroke or transient ischemic attack to help guide patient management in the secondary prevention of stroke. For example, carotid endarterectomy surgery plus medical therapy or medical therapy alone. The degree of carotid stenosis is the mainstay in making this decision and uses only one aspect of anatomical information that can be obtained from a carotid CTA scan. Radiomics, sometimes called ‘texture analysis’, is the extraction of quantitative data from medical images that may not be apparent to the naked eye and has already demonstrated clinical utility in oncology for applications ranging from lesion characterisation to tumour grading and prognostication. Machine learning refers to the process of learning from experience (in this case data), rather than following pre-programmed rules. This thesis presents the findings of a proof-of-principle study to assess the value of radiomics in identifying the ‘vulnerable plaque’ and the ‘vulnerable patient’ within the context of cerebrovascular events. To evaluate the potential of radiomic features as imaging biomarkers, their reproducibility and robustness to morphological perturbations were assessed, as well as their biological associations with both PET and immunohistochemistry data. The ability of radiomic features to classify different carotid artery types, namely, culprit, non-culprit and asymptomatic carotid arteries was assessed using several machine learning classifiers. This was subsequently compared with a deep learning approach, which has greater capacity for data mining than feature-based machine learning approaches. Overall, radiomics could extract further useful information from carotid CTA scans. Culprit versus non-culprit carotid arteries in symptomatic patients and asymptomatic carotid arteries from asymptomatic patients had different radiomic profiles that could be leveraged using machine learning for better classification performance than carotid calcification or carotid PET imaging alone. Reliable and robust CT-based carotid radiomic features were identified that were associated with the degree of inflammation underlying the carotid artery. If validated with future prospective studies, this has the potential to improve personalised patient care in stroke management and advance clinical decision-making.Cambridge School of Clinical Medicine, the Medical Research Council's Doctoral Training Partnership and the Frank Edward Elmore Fun

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Diabetic retinopathy as an independent predictor of subclinical cardiovascular disease : Baseline results of the PRECISED study

Funding This work was supported by an Integrative Excellence Project by the Spanish Institute of Health, Instituto de Salud Carlos III, grant PIE 2013/27, CIBER CV, CIBERDEM, and the European Regional Development Fund (ERDF-FEDER). The Neurovascular Research Laboratory is part of the Spanish Stroke Research Network INVICTUS+ (RD16/0019/0021).Objective Detection of subclinical cardiovascular disease (CVD) has significant impact on the management of type 2 diabetes. We examined whether the assessment of diabetic retinopathy (DR) is useful for identifying patients at a higher risk of having silent CVD. Research design and methods Prospective case-control study comprising 200 type 2 diabetic subjects without history of clinical CVD and 60 age-matched non-diabetic subjects. The presence of subclinical CVD was examined using two parameters: (1) calcium coronary score (CACs); (2) composite of CACs >400 UA, carotid plaque ≥3 mm, carotid intima-media thickness ratio >1, or the presence of ECG changes suggestive of previous asymptomatic myocardial infarction. In addition, coronary angio-CT was performed. DR was assessed by slit-lamp biomicroscopy and retinography. Results Type 2 diabetic subjects presented higher CACs than non-diabetic control subjects (p400 (area under the receiver operating characteristic curve (AUROC) 0.76). In addition, an inverse relationship was observed between the degree of DR and CACs <10 AU. The variables independently associated with the composite measurement of subclinical CVD were age, diabetes duration, the glomerular filtration rate, microalbuminuria, and the presence of DR (AUROC 0.71). In addition, a relationship (p<0.01) was observed between the presence and degree of DR and coronary stenosis. Conclusions The presence and degree of DR is independently associated with subclinical CVD in type 2 diabetic patients. Our results lead us to propose a rationalized screening for coronary artery disease in type 2 diabetes based on prioritizing patients with DR, particularly those with moderate-severe degree

Circle of Willis variants and cerebrovascular health: Representations, prevalences, functions and related consequences. Incomplete anatomy and changes to flow appear to induce more unfavourable health outcomes

Background: The Circle of Willis (CoW) is a circular structure of arteries in which most of the blood flowing to our brains pass through. The structure has primarily been regarded as important for its ability to redistribute blood flow in case of acute arterial occlusion, but may also have a role in dampening the pressure gradient in cerebral blood flow. The CoW anatomy also varies considerably, where its segments can be missing or thinner than normal, and therefore appears as a risk factor for cerebrovascular health. Objectives: To describe and report (I) the observed CoW variants and anatomy, and also examine the incomplete CoW variants’ associations to (II) white matter hyperintensities (WMH) and (III) saccular intracranial aneurysms (IA) compared to the complete CoW variant. Methods: Participants were invited from The Seventh Tromsø Study of which 1878 underwent magnetic resonance imaging. From the scans, CoW variants were semiautomatically classified. Likewise, WMH was automatically segmented and IAs were manually ascertained by radiologists. Results: The complete CoW is not very prevalent in participants older than 40 years old, and our findings suggest that the CoW becomes more incomplete with older age. Furthermore, incomplete CoW variants were not associated with increased WMH volume compared to the complete CoW variant. Incomplete CoW variants were associated increased odds of IA presence compared to the complete CoW variant. Conclusion: The results indicate that a complete CoW variant is not common in adults and elderly, which may have unfortunate consequences when incomplete CoW variants are associated with increased prevalence of IAs. Fortunately, not all results imply unfavourable outcomes, but further study of the CoW changes and possible effects of the variants over time are required.Bakgrunn: Willis Sirkel (CoW) er en sirkulær struktur av arterier i bunnen av hjernen som det meste av blodet går igjennom på tur til hjernen. Strukturen har vært antatt viktig for dens evner til å omdisponere blod i tilfellet arterier går tett, men i nyere tid har det også blitt foreslått at strukturen kan være viktig for å dempe pulstrykket i hjernen fra hjertet. Anatomien til CoW varierer mye, der segmenter mangler eller er tynnere enn normalt, og framstår dermed som et mulig risikomoment for hjernehelsen. Mål: Å beskrive (I) CoW varianter og anatomi. Analysere ufullstendige CoW varianters assosiasjoner til (II) vevsskader i hjernens indre som kalles hvit materie hyperintensiteter (WMH) og (III) sakkulære intrakranielle aneurismer (IA) sammenliknet med den fullstendige CoW varianten. Metoder: Deltakere ble invitert fra den Syvende Tromsøundersøkelsen hvorav 1878 ble tatt hjernebilder av med magnetresonans. Fra disse bildene ble CoW anatomi klassifisert. Likeså ble WMH automatisk segmentert og IA påvist av radiologer. Resultater: Den fullstendige CoW var ikke vanlig blant deltakerne eldre enn 40 år, og vi observerte også at CoW anatomien ble mer og mer ufullstendig hos eldre. Videre var ufullstendige CoW varianter ikke assosiert med høyere forekomst av WMH sammenliknet med den fullstendige CoW. Videre var ufullstendige CoW varianter assosiert med forhøyet odds for å ha IA sammenliknet med den fullstendige CoW. Konklusjon: Resultatene antyder at en fullstendig CoW ikke er spesielt vanlig hos voksne og eldre, noe som kan få uheldige følger når ufullstendige CoW er assosiert med økt forekomst av IA. Heldigvis antyder ikke alle resultatene negative følger, men mer forskning på CoW endringer og mulige effekter av anatomien over tid behøves for å stadfeste resultatene

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

Background: Cardiovascular disease (CVD) is challenging to diagnose and treat since symptoms appear late during the progression of atherosclerosis. Conventional risk factors alone are not always sufficient to properly categorize at-risk patients, and clinical risk scores are inadequate in predicting cardiac events. Integrating genomic-based biomarkers (GBBM) found in plasma/serum samples with novel non-invasive radiomics-based biomarkers (RBBM) such as plaque area, plaque burden, and maximum plaque height can improve composite CVD risk prediction in the pharmaceutical paradigm. These biomarkers consider several pathways involved in the pathophysiology of atherosclerosis disease leading to CVD. Objective: This review proposes two hypotheses: (i) The composite biomarkers are strongly correlated and can be used to detect the severity of CVD/Stroke precisely, and (ii) an explainable artificial intelligence (XAI)-based composite risk CVD/Stroke model with survival analysis using deep learning (DL) can predict in preventive, precision, and personalized (aiP 3 ) framework benefiting the pharmaceutical paradigm. Method: The PRISMA search technique resulted in 214 studies assessing composite biomarkers using radiogenomics for CVD/Stroke. The study presents a XAI model using AtheroEdge TM 4.0 to determine the risk of CVD/Stroke in the pharmaceutical framework using the radiogenomics biomarkers. Conclusions: Our observations suggest that the composite CVD risk biomarkers using radiogenomics provide a new dimension to CVD/Stroke risk assessment. The proposed review suggests a unique, unbiased, and XAI model based on AtheroEdge TM 4.0 that can predict the composite risk of CVD/Stroke using radiogenomics in the pharmaceutical paradigm

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

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