# Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI
## 1 Introduction  
Cardiovascular diseases are the leading cause of death in developed countries [1, 2]. Cardiovascular magnetic resonance (CMR) provides functional and morphological information of the heart for the evaluation, management, and diagnosis of patients with suspected or established cardiovascular disease. CMR is a multi-parametric, non-invasive imaging modality, which is considered the gold standard for the assessment of global and regional function and is able to evaluate myocardial perfusion and viability, tissue characterization, and coronary artery anatomy [3]. Left ventricular hypertrophy (LVH) is present in 15% to 20% of the population. It is more common in Afro-Americans and in patients with hypertension and obesity [4]. LVH is an independent predictor of future cardiovascular events, including coronary heart disease, heart failure, and stroke, regardless of its etiology [5, 6]. The definition of LVH is an increase in left ventricular mass either due to an increase in wall thickness, an increase in cavity size, or both. In clinical practice, LVH is a common condition, which can be caused by diverse physiological and pathological mechanisms such as athlete's heart, hypertension, aortic stenosis, hypertrophic cardiomyopathy, infiltrative heart muscle disease, storage, and metabolic disorders (amyloidosis, Anderson-Fabry disease, etc.). LVH can develop silently over several years without symptoms, and it can be difficult to diagnose. The electrocardiogram (ECG) is a useful, but less sensitive tool for detecting LVH. The utility of the ECG lies in its relative inexpensiveness and wide availability. Its limitations stem from its moderate sensitivity or specificity, dependingon which of the various diagnostic criteria are applied [7, 8]. In the Multi-Ethnic Study of Atherosclerosis patients who underwent MRI and ECG, it was found that various ECG criteria had low sensitivity for the detection of LVH [9]. As a result of these limitations of ECG, LVH is most reliably identified on imaging with echocardiography or CMR. Prior studies primarily used ECG [10] or M-Mode and two-dimensional (2D) echocardiography to identify LVH. Conventional 2D echocardiography is the first-line imaging modality, which is used to evaluate the patterns, extent, and distribution of LVH and other anatomic and functional parameters and ventricular function. Nonetheless, echocardiography is limited by intra-observer and inter-observer variability, acoustic windows, and the lack of tissue characterization. Echocardiography-based LVH evaluation varies among the different definitions by ultrasound technicians and laboratories around the world, leading to inconsistency among epidemiological studies, and therefore, this could limit its clinical application [11]. CMR provides a comprehensive evaluation of myocardial hypertrophy regarding the extent and distribution of LVH and tissue characterization. Accurate measurements of wall thickness, the phenotype of hypertrophy, chamber size, and ventricular function can be obtained without any limiting factors such as imaging windows and body habitus. Importantly, CMR has a myocardial tissue characterization property that allows phenotypic determination of the LVH and careful evaluation of the precise etiology of LVH, which is a challenging clinical problem [12]. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, which often leads to sudden death in young people with an estimated prevalence of about 1:500 [13]. LVH is characterized by sudden cardiac death, stroke, and heart failure, but also decreased life expectancy [14, 15]. The different LVH morphologic pattern can be precisely assessed by CMR and able to identify segmental hypertrophy, which can be difficult for echocardiography (i.e., apical HCM). The application of machine learning methods on CMR images has boomed in the past 5-6 years.