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Frontiers in Cardiovascular Medicine

Cardiovascular conditions remain the leading cause of mortality and morbidity worldwide (1), with genetic factors playing a significant role in conferring risk for disease (2).

High-throughput DNA sequencing and genotyping technologies, such as whole-genome sequencing and high-resolution array genotyping, have developed at an extraordinary pace since the first draft of the human genome was published in 2001 at a cost of $0.5-1 billion (3).

At the same time, technological advances in physics, engineering, and computing have enabled a step-change improvement in cardiovascular imaging, facilitating the shift from one dimensional, low-fidelity descriptors of the cardiovascular system to high-resolution multi-parametric phenotyping.

An unprecedented volume of clinical data is also becoming available, from smartphone-linked wearable sensors (5) to the numerous variables included in the electronic health records of entire populations (6).

designs underpinned candidate gene and linkage studies that established causal relationships between rare genetic variants and rare conditions, such as those that first identified the role of myosin heavy-chain beta in hypertrophic cardiomyopathy (HCM) (9) and of titin in dilated cardiomyopathy (DCM) (10).

For example, a study into the genetic determinants of hypertension in over 1 million subjects, identified 901 loci that were associated with systolic blood pressure (SBP) and these explained 5.7% of the variance observed (12).

The rapid development of complementary high-throughput technologies, able to characterize the transcriptome, epigenome, proteome, and metabolome now enables us to search for molecular evidence of gene causality and to understand the mechanisms and pathways involved in health and disease (13).

Phenotyping was characterized by imprecise quantification, sparsity of measurements, high intra- and inter- observer variability, low signal to noise ratios, reliance on geometric assumptions, and adequate body habitus, poor standardization of measurement techniques and the tendency to discretize continuous phenotypes (15).

The imaging community responded to calls for more accurate and precise, high-dimensional phenotyping (19, 20) with the roll out of developments in echocardiography (e.g., tissue doppler, speckle-tracking, and 3D imaging), CMR (e.g., tissue characterization, 4D flow, 3D imaging, diffusion tensor imaging, spectroscopy, and real-time scanning), CT (e.g., improved spatial and temporal resolution, radiation dose reduction techniques, functional assessment of coronary artery flow using FFR-CT, and coronary plaque characterization), and nuclear cardiology (e.g., improvements in radiopharmaceuticals and hardware resulting in increased accuracy and reduced radiation exposure).

As a subset of AI, machine learning refers to the family of algorithms that share a capacity to perform tasks like classification, regression, or clustering based on patterns or rules iteratively learnt directly from the data without using explicit instructions.

Indeed, while traditional ML is carried out using central processing units (CPUs), DL was only made possible thanks to the development of graphics processing units (GPUs), which have a massively parallel architecture consisting of thousands of cores and were designed to handle vast numbers of tasks simultaneously.

For example, an AI model trained on a healthy cohort may not generalize well to a general population that includes extreme disease phenotypes, and a system trained on images from a specific CMR scanner might not perform well when labeling images acquired under different technical conditions.

A recent systematic review and meta-analysis of 82 studies applying DL methods to medical imaging found that although the diagnostic performance of DL methods was often reported as equivalent to human experts, few studies tested human vs.

Traditional supervised ML methods have been applied successfully to classification tasks in extremely diverse input data, ranging from discrimination between sequences underlying Cis-regulatory elements from random genome sequences (39), separation of human induced pluripotent stem cell-derived cardiomyocytes of distinct genetic cardiac diseases (CPVT, LQT, HCM) (40) to numerous applications in medical imaging analysis.

Examples of this include automated quality control during CMR acquisition (41), high-resolution CMR study of cardiac remodeling in hypertension (42) and aortic stenosis (43), and echocardiographic differentiation of restrictive cardiomyopathy from constrictive pericarditis (44).

Unsupervised ML analysis have provided new unbiased insights into cardiovascular pathologies such as by establishing subsets of patients likely to benefit from cardiac resynchronization therapy (45) and by agnostic identification of echocardiography derived patterns in patients with heart failure with preserved ejection fraction and controls (46).

Applications include the analysis of CMRs (50), echocardiograms (51), and electrocardiograms (52), identification of the manufacturer of a pacemaker from a chest radiograph (53), aortic pressure waveform analysis during coronary angiography (54);

Such approaches have been used to predict sequence specificities of DNA- and RNA-binding proteins (31, 63), transcriptional enhancers (64) and splicing patterns (65) and to identify the functional effects of non-coding variants (66, 67).

However, several studies have already demonstrated the usefulness of AI tools in the analysis of large biological, imaging, and environmental data, in such tasks as dimensionality reduction and feature selection, speech recognition, clustering, image segmentation, natural language processing, variable classification, and outcome prediction (Figure 1).

To predict which dilated cardiomyopathy patients responded to immunoglobulin G substitution (IA/IgG) therapy, as assessed by echocardiography, two supervised ML approaches, a random forest analysis and a support vector machine algorithm, were used independently on gene expression data derived from 48 endomyocardial biopsies (72).

A support vector machine classifier, also proved to be extremely helpful in identifying specific proteomic signatures that accurately discriminated between patients with heart failure with reduced ejection fraction (HFrEF) and controls in the absence (73) or presence of chronic kidney disease (74).

The combination of three different supervised machine learning algorithms (support-vector machine, random forest, and neural network) trained on this sparser data was then shown to be better at distinguishing the two types of remodeling (ML model sensitivity = 87%;

One such study, using a hypothesis-free unsupervised clustering approach, revealed four distinct proteomic signatures with differing clinical risk and survival in patients with pulmonary arterial hypertension (76).

ML has similarly been able to identify new sub-phenotypes in heart failure with preserved ejection fraction, classifying subjects into three subgroups associated with distinct clinical, biomarker, hemodynamic, and structural groups with markedly different outcomes (77).

used a naïve Bayes classifier in a longitudinal imaging-genetics study of 1,027 young adults to identify a predictive relationship between genotypic variation and early signs of atherosclerosis, as assessed by carotid artery intima-media thickness, which could not be explained by conventional cardiovascular risk factors (78).

Interestingly, the model trained on SNP data only was highly predictive (AUC = 0.85), and better than models trained on clinical data (AUC = 0.61) and on a combination of genomic and clinical data (AUC = 0.83).

investigated the performance of 15 different supervised machine learning algorithms in predicting positive cardiac remodeling in patients that underwent cardiac resynchronization therapy (CRT) from clinical and genomic data (87).

Several of the approaches demonstrated clear overfitting (accuracy ~100%), while the algorithm that was identified as the most useful had a fair performance (accuracy = 83%) in addition to high transparency (predictive features easily identified).

This identified multiple genetic loci and several candidate genes associated with LV remodeling, and enabled the computing of a polygenic risk score (PRS) that was predictive of heart failure in a validation sample of nearly 230,000 subjects (odds ratio 1.41, 95% CI 1.26 –

To date, no methodological approaches have been able to include whole-genome and high-resolution whole-heart phenotypes, without requiring extensive dimensionality reduction, filtering and/or feature selection, possibly introducing errors or biases to the input data.

Issues related to the lack of interpretability (“black box”) of some ML algorithms are less of an issue in imaging analysis, where accuracy of analysis can be visually verified, but very relevant to integrated imaging-genetics analysis or risk prediction, where identifying and explaining the features driving the algorithm's output can be virtually impossible.

These problems are likely to be exacerbated if new test datasets include subjects with differing genetic or physiological backgrounds, data were acquired using different technical conditions (e.g., different scanners or different genotyping batches) or if the quality of data acquired in the research setting significantly differs from real world data sets.

Finally, issues regarding privacy, ownership, and consent over vast amounts of genetic and imaging data and legal and ethical considerations for clinicians using integrated imaging-genetics algorithms will become an ever more relevant topic of debate.

The increasing variety and capabilities of ML tools at the disposal of researchers provide a powerful platform to agnostically revisit classical definitions of disease, to more accurately predict outcomes and to vastly improve our understanding of the genetic and environmental underpinnings of cardiovascular health and pathology.

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