A machine learning cardiac magnetic resonance approach to extract disease features and automate pulmonary arterial hypertension diagnosis

Resistin predicts disease severity and survival in patients with pulmonary arterial hypertension

BACKGROUND

Abnormal remodeling of distal pulmonary arteries in patients with pulmonary arterial hypertension (PAH) leads to progressively increased pulmonary vascular resistance, followed by right ventricular hypertrophy and failure. Despite considerable advancements in PAH treatment prognosis remains poor. We aim to evaluate the potential for using the cytokine resistin as a genetic and biological marker for disease severity and survival in a large cohort of patients with PAH.

METHODS

Biospecimens, clinical, and genetic data for 1121 adults with PAH, including 808 with idiopathic PAH (IPAH) and 313 with scleroderma-associated PAH (SSc-PAH), were obtained from a national repository. Serum resistin levels were measured by ELISA, and associations between resistin levels, clinical variables, and single nucleotide polymorphism genotypes were examined with multivariable regression models. Machine-learning (ML) algorithms were applied to develop and compare risk models for mortality prediction.

RESULTS

Resistin levels were significantly higher in all PAH samples and PAH subtype (IPAH and SSc-PAH) samples than in controls (P < .0001) and had significant discriminative abilities (AUCs of 0.84, 0.82, and 0.91, respectively; P < .001). High resistin levels (above 4.54 ng/mL) in PAH patients were associated with older age (P = .001), shorter 6-min walk distance (P = .001), and reduced cardiac performance (cardiac index, P = .016). Interestingly, mutant carriers of either rs3219175 or rs3745367 had higher resistin levels (adjusted P = .0001). High resistin levels in PAH patients were also associated with increased risk of death (hazard ratio: 2.6; 95% CI: 1.27-5.33; P < .0087). Comparisons of ML-derived survival models confirmed satisfactory prognostic value of the random forest model (AUC = 0.70, 95% CI: 0.62-0.79) for PAH.

CONCLUSIONS

This work establishes the importance of resistin in the pathobiology of human PAH. In line with its function in rodent models, serum resistin represents a novel biomarker for PAH prognostication and may indicate a new therapeutic avenue. ML-derived survival models highlighted the importance of including resistin levels to improve performance. Future studies are needed to develop multi-marker assays that improve noninvasive risk stratification.

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and plays a crucial role in diagnosing cardiovascular disease (CVD). However, its widespread application has been limited by the heavy resource burden of CMR interpretation. Here, to address this challenge, we developed and validated computerized CMR interpretation for screening and diagnosis of 11 types of CVD in 9,719 patients. We propose a two-stage paradigm consisting of noninvasive cine-based CVD screening followed by cine and late gadolinium enhancement-based diagnosis. The screening and diagnostic models achieved high performance (area under the curve of 0.988 ± 0.3% and 0.991 ± 0.0%, respectively) in both internal and external datasets. Furthermore, the diagnostic model outperformed cardiologists in diagnosing pulmonary arterial hypertension, demonstrating the ability of artificial intelligence-enabled CMR to detect previously unidentified CMR features. This proof-of-concept study holds the potential to substantially advance the efficiency and scalability of CMR interpretation, thereby improving CVD screening and diagnosis.

State-of-the Art Cardiac Magnetic Resonance in Pulmonary Hypertension - An Update on Diagnosis, Risk Stratification and Treatment

Pulmonary hypertension (PH) is a globally under-recognized but life-shortening disease with a poor prognosis if untreated, delayed or inappropriately treated. One of the most important issues for PH is to improve patient quality of life and survival through timely and accurate diagnosis, precise risk stratification and prognosis prediction. Cardiac magnetic resonance (CMR), a non-radioactive, non-invasive image-based examination with excellent tissue characterization, provides a comprehensive assessment of not only the disease severity but also secondary changes in cardiac structure, function and tissue characteristics. The purpose of this review is to illustrate an updated status of CMR for PH assessment, focusing on the application of both conventional and emerging technologies as well as the latest clinical trials.

Shapely additive values can effectively visualize pertinent covariates in machine learning when predicting hypertension

Machine learning methods are widely used within the medical field to enhance prediction. However, little is known about the reliability and efficacy of these models to predict long-term medical outcomes such as blood pressure using lifestyle factors, such as diet. The authors assessed whether machine-learning techniques could accurately predict hypertension risk using nutritional information. A cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) between January 2017 and March 2020. XGBoost was used as the machine-learning model of choice in this study due to its increased performance relative to other common methods within medical studies. Model prediction metrics (e.g., AUROC, Balanced Accuracy) were used to measure overall model efficacy, covariate Gain statistics (percentage each covariate contributes to the overall prediction) and SHapely Additive exPlanations (SHAP, method to visualize each covariate) were used to provide explanations to machine-learning output and increase the transparency of this otherwise cryptic method. Of a total of 9650 eligible patients, the mean age was 41.02 (SD = 22.16), 4792 (50%) males, 4858 (50%) female, 3407 (35%) White patients, 2567 (27%) Black patients, 2108 (22%) Hispanic patients, and 981 (10%) Asian patients. From evaluation of model gain statistics, age was found to be the single strongest predictor of hypertension, with a gain of 53.1%. Additionally, demographic factors such as poverty and Black race were also strong predictors of hypertension, with gain of 4.33% and 4.18%, respectively. Nutritional Covariates contributed 37% to the overall prediction: Sodium, Caffeine, Potassium, and Alcohol intake being significantly represented within the model. Machine Learning can be used to predict hypertension.

Exploring the EVolution in PrognOstic CapabiLity of MUltisequence Cardiac MagneTIc ResOnance in PatieNts Affected by Takotsubo Cardiomyopathy Based on Machine Learning Analysis: Design and Rationale of the EVOLUTION Study

PURPOSE

Takotsubo cardiomyopathy (TTC) is a transient but severe acute myocardial dysfunction with a wide range of outcomes from favorable to life-threatening. The current risk stratification scores of TTC patients do not include cardiac magnetic resonance (CMR) parameters. To date, it is still unknown whether and how clinical, trans-thoracic echocardiography (TTE), and CMR data can be integrated to improve risk stratification.

METHODS

EVOLUTION (Exploring the eVolution in prognOstic capabiLity of mUlti-sequence cardiac magneTIc resOnance in patieNts affected by Takotsubo cardiomyopathy) is a multicenter, international registry of TTC patients who will undergo a clinical, TTE, and CMR evaluation. Clinical data including demographics, risk factors, comorbidities, laboratory values, ECG, and results from TTE and CMR analysis will be collected, and each patient will be followed-up for in-hospital and long-term outcomes. Clinical outcome measures during hospitalization will include cardiovascular death, pulmonary edema, arrhythmias, stroke, or transient ischemic attack.Clinical long-term outcome measures will include cardiovascular death, pulmonary edema, heart failure, arrhythmias, sudden cardiac death, and major adverse cardiac and cerebrovascular events defined as a composite endpoint of death from any cause, myocardial infarction, recurrence of TTC, transient ischemic attack, and stroke. We will develop a comprehensive clinical and imaging score that predicts TTC outcomes and test the value of machine learning models, incorporating clinical and imaging parameters to predict prognosis.

CONCLUSIONS

The main goal of the study is to develop a comprehensive clinical and imaging score, that includes TTE and CMR data, in a large cohort of TTC patients for risk stratification and outcome prediction as a basis for possible changes in patient management.

Role of artificial intelligence and machine learning in interventional cardiology

Directed by two decades of technological processes and remodeling, the dynamic quality of healthcare data combined with the progress of computational power has allowed for rapid progress in artificial intelligence (AI). In interventional cardiology, AI has shown potential in providing data interpretation and automated analysis from electrocardiogram (ECG), echocardiography, computed tomography angiography (CTA), magnetic resonance imaging (MRI), and electronic patient data. Clinical decision support has the potential to assist in improving patient safety and making prognostic and diagnostic conjectures in interventional cardiology procedures. Robot-assisted percutaneous coronary intervention (R-PCI), along with functional and quantitative assessment of coronary artery ischemia and plaque burden on intravascular ultrasound (IVUS), are the major applications of AI. Machine learning (ML) algorithms are used in these applications, and they have the potential to bring a paradigm shift in intervention. Recently, an efficient branch of ML has emerged as a deep learning algorithm for numerous cardiovascular (CV) applications. However, the impact DL on the future of cardiology practice is not clear. Predictive models based on DL have several limitations including low generalizability and decision processing in cardiac anatomy.

Artificial Intelligence as a Diagnostic Tool in Non-Invasive Imaging in the Assessment of Coronary Artery Disease

Coronary artery disease (CAD) remains a leading cause of mortality and morbidity worldwide, and it is associated with considerable economic burden. In an ageing, multimorbid population, it has become increasingly important to develop reliable, consistent, low-risk, non-invasive means of diagnosing CAD. The evolution of multiple cardiac modalities in this field has addressed this dilemma to a large extent, not only in providing information regarding anatomical disease, as is the case with coronary computed tomography angiography (CCTA), but also in contributing critical details about functional assessment, for instance, using stress cardiac magnetic resonance (S-CMR). The field of artificial intelligence (AI) is developing at an astounding pace, especially in healthcare. In healthcare, key milestones have been achieved using AI and machine learning (ML) in various clinical settings, from smartwatches detecting arrhythmias to retinal image analysis and skin cancer prediction. In recent times, we have seen an emerging interest in developing AI-based technology in the field of cardiovascular imaging, as it is felt that ML methods have potential to overcome some limitations of current risk models by applying computer algorithms to large databases with multidimensional variables, thus enabling the inclusion of complex relationships to predict outcomes. In this paper, we review the current literature on the various applications of AI in the assessment of CAD, with a focus on multimodality imaging, followed by a discussion on future perspectives and critical challenges that this field is likely to encounter as it continues to evolve in cardiology.

The year 2021 in the European Heart Journal—Cardiovascular Imaging: Part I

The European Heart Journal—Cardiovascular Imaging was introduced in 2012 and has during these 10 years become one of the leading multimodality cardiovascular imaging journals. The journal is currently ranked as Number 19 among all cardiovascular journals. It has an impressive impact factor of 9.130 and our journal is well established as one of the top cardiovascular journals. The most important studies published in our Journal in 2021 will be highlighted in two reports. Part I of the review will focus on studies about myocardial function and risk prediction, myocardial ischaemia, and emerging techniques in cardiovascular imaging, while Part II will focus on valvular heart disease, heart failure, cardiomyopathies, and congenital heart disease.

Artificial intelligence in cardiology: Hope for the future and power for the present

Cardiovascular disease (CVD) is the principal cause of mortality and morbidity globally. With the pressures for improved care and translation of the latest medical advances and knowledge to an actionable plan, clinical decision-making for cardiologists is challenging. Artificial Intelligence (AI) is a field in computer science that studies the design of intelligent agents which take the best feasible action in a situation. It incorporates the use of computational algorithms which simulate and perform tasks that traditionally require human intelligence such as problem solving and learning. Whilst medicine is arguably the last to apply AI in its everyday routine, cardiology is at the forefront of AI revolution in the medical field. The development of AI methods for accurate prediction of CVD outcomes, non-invasive diagnosis of coronary artery disease (CAD), detection of malignant arrythmias through wearables, and diagnosis, treatment strategies and prediction of outcomes for heart failure (HF) patients, demonstrates the potential of AI in future cardiology. With the advancements of AI, Internet of Things (IoT) and the promotion of precision medicine, the future of cardiology will be heavily based on these innovative digital technologies. Despite this, ethical dilemmas regarding the implementation of AI technologies in real-world are still unaddressed.

The Role of Artificial Intelligence in Predicting Outcomes by Cardiovascular Magnetic Resonance: A Comprehensive Systematic Review

Background and Objectives: Interest in artificial intelligence (AI) for outcome prediction has grown substantially in recent years. However, the prognostic role of AI using advanced cardiac magnetic resonance imaging (CMR) remains unclear. This systematic review assesses the existing literature on AI in CMR to predict outcomes in patients with cardiovascular disease. Materials and Methods: Medline and Embase were searched for studies published up to November 2021. Any study assessing outcome prediction using AI in CMR in patients with cardiovascular disease was eligible for inclusion. All studies were assessed for compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Results: A total of 5 studies were included, with a total of 3679 patients, with 225 deaths and 265 major adverse cardiovascular events. Three methods demonstrated high prognostic accuracy: (1) three-dimensional motion assessment model in pulmonary hypertension (hazard ratio (HR) 2.74, 95%CI 1.73−4.34, p < 0.001), (2) automated perfusion quantification in patients with coronary artery disease (HR 2.14, 95%CI 1.58−2.90, p < 0.001), and (3) automated volumetric, functional, and area assessment in patients with myocardial infarction (HR 0.94, 95%CI 0.92−0.96, p < 0.001). Conclusion: There is emerging evidence of the prognostic role of AI in predicting outcomes for three-dimensional motion assessment in pulmonary hypertension, ischaemia assessment by automated perfusion quantification, and automated functional assessment in myocardial infarction.

Machine learning cardiac-MRI features predict mortality in newly diagnosed pulmonary arterial hypertension

Aims

Pulmonary arterial hypertension (PAH) is a rare but serious disease associated with high mortality if left untreated. This study aims to assess the prognostic cardiac magnetic resonance (CMR) features in PAH using machine learning.

Methods and results

Seven hundred and twenty-three consecutive treatment-naive PAH patients were identified from the ASPIRE registry; 516 were included in the training, and 207 in the validation cohort. A multilinear principal component analysis (MPCA)-based machine learning approach was used to extract mortality and survival features throughout the cardiac cycle. The features were overlaid on the original imaging using thresholding and clustering of high- and low-risk of mortality prediction values. The 1-year mortality rate in the validation cohort was 10%. Univariable Cox regression analysis of the combined short-axis and four-chamber MPCA-based predictions was statistically significant (hazard ratios: 2.1, 95% CI: 1.3, 3.4, c-index = 0.70, P = 0.002). The MPCA features improved the 1-year mortality prediction of REVEAL from c-index = 0.71 to 0.76 (P ≤ 0.001). Abnormalities in the end-systolic interventricular septum and end-diastolic left ventricle indicated the highest risk of mortality.

Conclusion

The MPCA-based machine learning is an explainable time-resolved approach that allows visualization of prognostic cardiac features throughout the cardiac cycle at the population level, making this approach transparent and clinically interpretable. In addition, the added prognostic value over the REVEAL risk score and CMR volumetric measurements allows for a more accurate prediction of 1-year mortality risk in PAH.

Harnessing Big Data to Advance Treatment and Understanding of Pulmonary Hypertension

Pulmonary hypertension is a complex disease with multiple causes, corresponding to phenotypic heterogeneity and variable therapeutic responses. Advancing understanding of pulmonary hypertension pathogenesis is likely to hinge on integrated methods that leverage data from health records, imaging, novel molecular -omics profiling, and other modalities. In this review, we summarize key data sets generated thus far in the field and describe analytical methods that hold promise for deciphering the molecular mechanisms that underpin pulmonary vascular remodeling, including machine learning, network medicine, and functional genetics. We also detail how genetic and subphenotyping approaches enable earlier diagnosis, refined prognostication, and optimized treatment prediction. We propose strategies that identify functionally important molecular pathways, bolstered by findings across multi-omics platforms, which are well-positioned to individualize drug therapy selection and advance precision medicine in this highly morbid disease.

An evolutionary machine learning for pulmonary hypertension animal model from arterial blood gas analysis

Pulmonary hypertension (PH) is a rare and fatal condition that leads to right heart failure and death. The pathophysiology of PH and potential therapeutic approaches are yet unknown. PH animal models' development and proper evaluation are critical to PH research. This work presents an effective analysis technology for PH from arterial blood gas analysis utilizing an evolutionary kernel extreme learning machine with multiple strategies integrated slime mould algorithm (MSSMA). In MSSMA, two efficient bee-foraging learning operators are added to the original slime mould algorithm, ensuring a suitable trade-off between intensity and diversity. The proposed MSSMA is evaluated on thirty IEEE benchmarks and the statistical results show that the search performance of the MSSMA is significantly improved. The MSSMA is utilised to develop a kernel extreme learning machine (MSSMA-KELM) on PH from arterial blood gas analysis. Comprehensively, the proposed MSSMA-KELM can be used as an effective analysis technology for PH from arterial Blood gas analysis with an accuracy of 93.31%, Matthews coefficient of 90.13%, Sensitivity of 91.12%, and Specificity of 90.73%. MSSMA-KELM can be treated as an effective approach for evaluating mouse PH models.

Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning

Introduction

Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA.

Methods

A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was conducted in 1,333 patients with mixed pulmonary vascular disease. Segmentation was achieved using deep learning via a convolutional neural network. Volumetric imaging biomarkers were correlated with invasive haemodynamics in the test cohort.

Results

Dice similarity coefficients (DSC) for segmented structures were in the range 0.58-0.93 for both the internal and external test cohorts. The left and right ventricle myocardium segmentations had lower DSC of 0.83 and 0.58 respectively while all other structures had DSC >0.89 in the internal test cohort and >0.87 in the external test cohort. Interobserver comparison found that the left and right ventricle myocardium segmentations showed the most variation between observers: mean DSC (range) of 0.795 (0.785-0.801) and 0.520 (0.482-0.542) respectively. Right ventricle myocardial volume had strong correlation with mean pulmonary artery pressure (Spearman's correlation coefficient = 0.7). The volume of segmented cardiac structures by deep learning had higher or equivalent correlation with invasive haemodynamics than by manual segmentations. The model demonstrated good generalisability to different vendors and hospitals with similar performance in the external test cohort. The failure rates in mixed pulmonary vascular disease were low (<3.9%) indicating good generalisability of the model to different diseases.

Conclusion

Fully automated segmentation of the four cardiac chambers and great vessels has been achieved in CTPA with high accuracy and low rates of failure. DL volumetric biomarkers can potentially improve CTPA cardiac assessment and invasive haemodynamic prediction.

Ventricular mass discriminates pulmonary arterial hypertension as redefined at the Sixth World Symposium on Pulmonary Hypertension

Cardiac magnetic resonance (CMR) measures of right ventricular (RV) mass, volumes, and function have diagnostic and prognostic value in pulmonary arterial hypertension (PAH). We hypothesized that RV mass-based metrics would discriminate incident PAH as redefined by the lower mean pulmonary arterial pressure (mPAP) threshold of >20 mmHg at the Sixth World Symposium on Pulmonary Hypertension (6th WSPH). Eighty-nine subjects with suspected PAH underwent CMR imaging, including 64 subjects with systemic sclerosis (SSc). CMR metrics, including RV and left ventricular (LV) mass, were measured. All subjects underwent right heart catheterization (RHC) for assessment of hemodynamics within 48 h of CMR. Using generalized linear models, associations between CMR metrics and PAH were assessed, the best subset of CMR variables for predicting PAH were identified, and relationships between mass-based metrics, hemodynamics, and other predictive CMR metrics were examined. Fifty-nine subjects met 6th WSPH criteria for PAH. RV mass metrics, including ventricular mass index (VMI), demonstrated the greatest magnitude difference between subjects with versus without PAH. Overall and in SSc, VMI and RV mass measured by CMR were among the most predictive variables discriminating PAH at RHC, with areas under the receiver operating characteristic curve 0.86 and 0.83. respectively. VMI increased linearly with pulmonary vascular resistance and with mPAP in PAH, including in lower ranges of mPAP associated with mild PAH. VMI ≥ 0.37 yielded a positive predictive value of 90% for discriminating PAH. RV mass metrics measured by CMR, including VMI, discriminate incident, treatment-naïve PAH as defined by 6th WSPH criteria.

Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: a systematic literature review

OBJECTIVES

To undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for the diagnosis of acquired pulmonary arterial hypertension (PAH).

METHODS

Searches of Medline, Embase and Web of Science were undertaken on 1 July 2020. Original publications studying AI applied to cross-sectional imaging for the diagnosis of acquired PAH in adults were identified through two-staged double-blinded review. Study quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies and Checklist for Artificial Intelligence in Medicine frameworks. Narrative synthesis was undertaken following Synthesis Without Meta-Analysis guidelines. This review received no funding and was registered in the International Prospective Register of Systematic Reviews (ID:CRD42020196295).

RESULTS

Searches returned 476 citations. Three retrospective observational studies, published between 2016 and 2020, were selected for data-extraction. Two methods applied to cardiac-MRI demonstrated high diagnostic accuracy, with the best model achieving AUC=0.90 (95% CI: 0.85-0.93), 89% sensitivity and 81% specificity. Stronger results were achieved using cardiac-MRI for classification of idiopathic PAH, achieving AUC=0.97 (95% CI: 0.89-1.0), 96% sensitivity and 87% specificity. One study reporting CT-based AI demonstrated lower accuracy, with 64.6% sensitivity and 97.0% specificity.

CONCLUSIONS

Automated methods for identifying PAH on cardiac-MRI are emerging with high diagnostic accuracy. AI applied to cross-sectional imaging may provide non-invasive support to reduce diagnostic delay in PAH. This would be helped by stronger solutions in other modalities.

ADVANCES IN KNOWLEDGE

There is a significant shortage of research in this important area. Early detection of PAH would be supported by further research advances on the promising emerging technologies identified.

What's new in pulmonary hypertension clinical research: lessons from the best abstracts at the 2020 American Thoracic Society International Conference

In this conference paper, we review the 2020 American Thoracic Society International Conference session titled, "What's New in Pulmonary Hypertension Clinical Research: Lessons from the Best Abstracts". This virtual mini-symposium took place on 21 October 2020, in lieu of the annual in-person ATS International Conference which was cancelled due to the COVID-19 pandemic. Seven clinical research abstracts were selected for presentation in the session, which encompassed five major themes: (1) standardizing diagnosis and management of pulmonary hypertension, (2) improving risk assessment in pulmonary arterial hypertension, (3) evaluating biomarkers of disease activity, (4) understanding metabolic dysregulation across the spectrum of pulmonary hypertension, and (5) advancing knowledge in chronic thromboembolic pulmonary hypertension. Focusing on these five thematic contexts, we review the current state of knowledge, summarize presented research abstracts, appraise their significance and limitations, and then discuss relevant future directions in pulmonary hypertension clinical research.

Pulmonary arterial hypertension and flavonoids: A role in treatment

Pulmonary arterial hypertension (PAH) is a high mortality progressive pulmonary vascular disease that can lead to right heart failure. The use of clinical drugs for the treatment of PAH is limited to a great extent because of its single target and high price. Flavonoids are widely distributed in nature, and have been found in fruits, vegetables, and traditional Chinese medicine. They have diverse biological activities and various pharmacological effects such as antitumor, antioxidation, and anti-inflammatory. This review summarizes the progress in pharmacodynamics and mechanism of flavonoids in the treatment of PAH in recent years, in order to provide some theoretical references for relevant researchers.

Metalloproteinases and their inhibitors are associated with pulmonary arterial stiffness and ventricular function in pediatric pulmonary hypertension

Disturbed balance between matrix metalloproteinases (MMPs) and their respective tissue inhibitors (TIMPs) is a well-recognized pathophysiological component of pulmonary arterial hypertension (PAH). Both classes of proteinases have been associated with clinical outcomes as well as with specific pathological features of ventricular dysfunction and pulmonary arterial remodeling. The purpose of this study was to evaluate the circulating levels of MMPs and TIMPs in children with PAH undergoing the same-day cardiac magnetic resonance imaging (MRI) and right heart catheterization. Children with PAH (n = 21) underwent a same-day catheterization, comprehensive cardiac MRI evaluation, and blood sample collection for proteomic analysis. Correlative analysis was performed between protein levels and 1) standard PAH indices from catheterization, 2) cardiac MRI hemodynamics, and 3) pulmonary arterial stiffness. MMP-8 was significantly associated with the right ventricular end-diastolic volume (R = 0.45, P = 0.04). MMP-9 levels were significantly associated with stroke volume (R = -0.49, P = 0.03) and pulmonary vascular resistance (R = 0.49, P = 0.03). MMP-9 was further associated with main pulmonary arterial stiffness evaluated by relative area change (R = -0.79, P < 0.01).TIMP-2 and TIMP-4 levels were further associated with the right pulmonary artery pulse wave velocity (R = 0.51, P = 0.03) and backward compression wave (R = 0.52, P = 0.02), respectively. MMPs and TIMPs warrant further clinically prognostic evaluation in conjunction with the conventional cardiac MRI hemodynamic indices.NEW & NOTEWORTHY Metalloproteinases have been associated with clinical outcomes in pulmonary hypertension and with specific pathological features of ventricular dysfunction and pulmonary arterial remodeling. In this study, we demonstrated that plasma circulating levels of metalloproteinases and their inhibitors are associated with standard cardiac MRI hemodynamic indices and with the markers of proximal pulmonary arterial stiffness. Particularly, MMP-9 and TIMP-2 were associated with several different markers of pulmonary arterial stiffness. These findings suggest the interplay between the extracellular matrix (ECM) remodeling and overall hemodynamic status in children with PAH might be assessed using the peripheral circulating MMP and TIMP levels.

Potential Role of Artificial Intelligence in Cardiac Magnetic Resonance Imaging: Can It Help Clinicians in Making a Diagnosis?

In the era of modern medicine, artificial intelligence (AI) is a growing field of interest which is experiencing a steady development. Several applications of AI have been applied to various aspects of cardiac magnetic resonance to assist clinicians and engineers in reducing the costs of exams and, at the same time, to improve image acquisition and reconstruction, thus simplifying their analysis, interpretation, and decision-making process as well. In fact, the role of AI and machine learning in cardiovascular imaging relies on evaluating images more quickly, improving their quality, nulling intraobserver and interobserver variability in their interpretation, upgrading the understanding of the stage of the disease, and providing with a personalized approach to cardiovascular care. In addition, AI algorithm could be directed toward workflow management. This article presents an overview of the existing AI literature in cardiac magnetic resonance, with its strengths and limitations, recent applications, and promising developments. We conclude that AI is very likely be used in all the various process of diagnosis routine mode for cardiac care of patients.

The Role of Artificial Intelligence in Cardiovascular Imaging: State of the Art Review

In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.

From Early Morphometrics to Machine Learning-What Future for Cardiovascular Imaging of the Pulmonary Circulation?

Imaging plays a cardinal role in the diagnosis and management of diseases of the pulmonary circulation. Behind the picture itself, every digital image contains a wealth of quantitative data, which are hardly analysed in current routine clinical practice and this is now being transformed by radiomics. Mathematical analyses of these data using novel techniques, such as vascular morphometry (including vascular tortuosity and vascular volumes), blood flow imaging (including quantitative lung perfusion and computational flow dynamics), and artificial intelligence, are opening a window on the complex pathophysiology and structure-function relationships of pulmonary vascular diseases. They have the potential to make dramatic alterations to how clinicians investigate the pulmonary circulation, with the consequences of more rapid diagnosis and a reduction in the need for invasive procedures in the future. Applied to multimodality imaging, they can provide new information to improve disease characterization and increase diagnostic accuracy. These new technologies may be used as sophisticated biomarkers for risk prediction modelling of prognosis and for optimising the long-term management of pulmonary circulatory diseases. These innovative techniques will require evaluation in clinical trials and may in themselves serve as successful surrogate end points in trials in the years to come.

Role of biomarkers in evaluation, treatment and clinical studies of pulmonary arterial hypertension

Pulmonary arterial hypertension is a complex disease resulting from the interplay of myriad biological and environmental processes that lead to remodeling of the pulmonary vasculature with consequent pulmonary hypertension. Despite currently available therapies, there remains significant morbidity and mortality in this disease. There is great interest in identifying and applying biomarkers to help diagnose patients with pulmonary arterial hypertension, inform prognosis, guide therapy, and serve as surrogate endpoints. An extensive literature on potential biomarker candidates is available, but barriers to the implementation of biomarkers for clinical use in pulmonary arterial hypertension are substantial. Various omic strategies have been undertaken to identify key pathways regulated in pulmonary arterial hypertension that could serve as biomarkers including genomic, transcriptomic, proteomic, and metabolomic approaches. Other biologically relevant components such as circulating cells, microRNAs, exosomes, and cell-free DNA have recently been gaining attention. Because of the size of the datasets generated by these omic approaches and their complexity, artificial intelligence methods are being increasingly applied to decipher their meaning. There is growing interest in imaging the lung with various modalities to understand and visualize processes in the lung that lead to pulmonary vascular remodeling including high resolution computed tomography, Xenon magnetic resonance imaging, and positron emission tomography. Such imaging modalities have the potential to demonstrate disease modification resulting from therapeutic interventions. Because right ventricular function is a major determinant of prognosis, imaging of the right ventricle with echocardiography or cardiac magnetic resonance imaging plays an important role in the evaluation of patients and may also be useful in clinical studies of pulmonary arterial hypertension.

The Value of Hemodynamic Measurements or Cardiac MRI in the Follow-up of Patients With Idiopathic Pulmonary Arterial Hypertension

Background

Treatment of patients with pulmonary arterial hypertension (PAH) is conventionally based on functional plus invasive measurements obtained during right heart catheterization (RHC). Whether risk assessment during repeated measurements could also be performed on the basis of imaging parameters is unclear, as a direct comparison of strategies is lacking.

Research Question

How does the predictive value of noninvasive parameters compare with that of invasive hemodynamic measurements 1 year after the diagnosis of idiopathic PAH?

Study Design and Methods

One hundred and eighteen patients with idiopathic PAH who underwent RHC and cardiac MRI (CMR) were included in this study (median time between baseline evaluation and first parameter measures, 1.0 [0.8-1.2] years). Forty-four patients died or underwent lung transplantation. Forward Cox regression analyses were done to determine the best predictive functional, hemodynamic, and/or imaging model. Patients were classified as high risk if the event occurred < 5 years after diagnosis (n = 24), whereas patients without event were classified as low risk.

Results

A prognostic model based on age, sex, and absolute values at follow-up of functional parameters (6-min walk distance) performed well (Akaike information criterion [AIC], 279; concordance, 0.67). Predictive models with only hemodynamic (right atrial pressure, mixed venous oxygen saturation; AIC, 322; concordance, 0.66) or imaging parameters (right ventricular ejection fraction; AIC, 331; concordance, 0.63) at 1 year of follow-up performed similarly. The predictive value improved when functional data were combined with either hemodynamic data (AIC, 268; concordance, 0.69) or imaging data (AIC, 273; concordance, 0.70). A model composed of functional, hemodynamic, and imaging data performed only marginally better (AIC, 266; concordance, 0.69). Finally, changes between baseline and 1-year follow-up were observed for multiple hemodynamic and CMR parameters; only a change in CMR parameters was of prognostic predictive value.

Interpretation

At 1 year of follow-up, risk assessment based on CMR is at least equal to risk assessment based on RHC. In this study, only changes in CMR, but not hemodynamic parameters, were of prognostic predictive value during the first year of follow-up.

Neural-Network-Based Diagnosis Using 3-Dimensional Myocardial Architecture and Deformation: Demonstration for the Differentiation of Hypertrophic Cardiomyopathy

The diagnosis of cardiomyopathy states may benefit from machine-learning (ML) based approaches, particularly to distinguish those states with similar phenotypic characteristics. Three-dimensional myocardial deformation analysis (3D-MDA) has been validated to provide standardized descriptors of myocardial architecture and deformation, and may therefore offer appropriate features for the training of ML-based diagnostic tools. We aimed to assess the feasibility of automated disease diagnosis using a neural network trained using 3D-MDA to discriminate hypertrophic cardiomyopathy (HCM) from its mimic states: cardiac amyloidosis (CA), Anderson–Fabry disease (AFD), and hypertensive cardiomyopathy (HTNcm). 3D-MDA data from 163 patients (mean age 53.1 ± 14.8 years; 68 females) with left ventricular hypertrophy (LVH) of known etiology was provided. Source imaging data was from cardiac magnetic resonance (CMR). Clinical diagnoses were as follows: 85 HCM, 30 HTNcm, 30 AFD, and 18 CA. A fully-connected-layer feed-forward neural was trained to distinguish HCM vs. other mimic states. Diagnostic performance was compared to threshold-based assessments of volumetric and strain-based CMR markers, in addition to baseline clinical patient characteristics. Threshold-based measures provided modest performance, the greatest area under the curve (AUC) being 0.70. Global strain parameters exhibited reduced performance, with AUC under 0.64. A neural network trained exclusively from 3D-MDA data achieved an AUC of 0.94 (sensitivity 0.92, specificity 0.90) when performing the same task. This study demonstrates that ML-based diagnosis of cardiomyopathy states performed exclusively from 3D-MDA is feasible and can distinguish HCM from mimic disease states. These findings suggest strong potential for computer-assisted diagnosis in clinical practice.

Cardiac Magnetic Resonance in Pulmonary Hypertension-an Update

PURPOSE OF REVIEW

This article reviews advances over the past 3 years in cardiac magnetic resonance (CMR) imaging in pulmonary hypertension (PH). We aim to bring the reader up-to-date with CMR applications in diagnosis, prognosis, 4D flow, strain analysis, T1 mapping, machine learning and ongoing research.

RECENT FINDINGS

CMR volumetric and functional metrics are now established as valuable prognostic markers in PH. This imaging modality is increasingly used to assess treatment response and improves risk stratification when incorporated into PH risk scores. Emerging techniques such as myocardial T1 mapping may play a role in the follow-up of selected patients. Myocardial strain may be used as an early marker for right and left ventricular dysfunction and a predictor for mortality. Machine learning has offered a glimpse into future possibilities. Ongoing research of new PH therapies is increasingly using CMR as a clinical endpoint.

SUMMARY

The last 3 years have seen several large studies establishing CMR as a valuable diagnostic and prognostic tool in patients with PH, with CMR increasingly considered as an endpoint in clinical trials of PH therapies. Machine learning approaches to improve automation and accuracy of CMR metrics and identify imaging features of PH is an area of active research interest with promising clinical utility.

Clinical applications of artificial intelligence in cardiology on the verge of the decade

Artificial intelligence (AI) has been hailed as the fourth industrial revolution and its influence on people's lives is increasing. The research on AI applications in medicine is progressing rapidly. This revolution shows promise for more precise diagnoses, streamlined workflows, increased accessibility to healthcare services and new insights into ever-growing population-wide datasets. While some applications have already found their way into contemporary patient care, we are still in the early days of the AI-era in medicine. Despite the popularity of these new technologies, many practitioners lack an understanding of AI methods, their benefits, and pitfalls. This review aims to provide information about the general concepts of machine learning (ML) with special focus on the applications of such techniques in cardiovascular medicine. It also sets out the current trends in research related to medical applications of AI. Along with new possibilities, new threats arise - acknowledging and understanding them is as important as understanding the ML methodology itself. Therefore, attention is also paid to the current opinions and guidelines regarding the validation and safety of AI-powered tools.