# Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI
## 1 Introduction
A huge body of work is available on automatic processing of MRI images, such as the segmentation of the ventricles [16, 17, 18], left ventricular quantification [19], pediatric cardiomyopathy classification [20], left ventricle wall motion classification [21], and cardiovascular event prediction for dilated cardiomyopathy [22]. These research endeavors also resulted in new architectures, developed for this field specifically, e.g., U-net [23] for myocardium segmentation, \(\
u\)-net for cardiac vascular segmentation [24], and \(\Omega\)-net for multiview CMR detection, orientation, and segmentation [25]. Besides the algorithmic improvement, the increasing availability of public datasets fuels the breakthroughs in the healthcare domain [26, 27]. For LVH, the lack of publicly available benchmarks is reflected in the lower number of papers on the subject [28]. However, for echocardiography, there are promising works out there [29, 30, 31]. For instance, in paper [32], the echocardiography-based hypertrophy detection calculates the wall thickness, then the decision is made. This method automates the wall thickness measurements with thresholding, then the wall thickness is calculated from the adjusted contours. The accuracy of echocardiography-based diagnosis tends to be lower than that of CMR-based examinations [33, 34]. This further motivates the application of CMR for hypertrophy detection. In the work of [35], the disease classification was based on a multi-stage process. First, it segments the heart, then it calculates the volumetric data. The volumetric data are used as features in a random forest to obtain the classification. The classes are different from ours, but there are HCM and normal heart cases, while the input images are CMR images. In paper [36], the authors developed an automatic wall thickness measurement on CMR images. The measurement was based on endocardial and epicardial segmentations. Another method relies on the clinical assessment of normal ranges for different morphological characteristics [37]. To the best of our knowledge, this is the first paper discussing an automatic method for left ventricular hypertrophy classification from CMR images. If the algorithm can detect suspicious features of hypertrophy during a regular CMR examination, this indicates that the applied CMR protocol should be changed by adding necessary measurements and sequences on-site for a more detailed evaluation without the need for additional examinations. During the post-process evaluation, it could improve the diagnostic accuracy by recognizing a milder, incipient form of LVH, which can be challenging for the less-experienced readers. The early detection of LVH and appropriate therapy will decrease cardiovascular morbidity and mortality [38]. In this paper, steps toward this ambition were made by developing an algorithm that considers more views of the heart and classifies the patient's hearts as normal or exhibiting hypertrophy. The algorithm we developed achieved results comparable to the human readers. Its high recall and sufficient precision allow for its use in an on-site setting, potentially causing the operators to change the CMR protocol (e.g., to administer the contrast agent, acquire late enhancement images, etc.) if hypertrophy is suspected. During the CMR examination, usually, the long-axis cine images are acquired first, then the short-axis cine images, then the late enhancement images if needed. We found that if the algorithm is restricted to only use long-axis cine images, it is still sufficient to alert the operator in order to select an appropriate CMR protocol, but might be limited in some selected cases. The rest of the paper is structured the following way: In Section 2, we introduce the dataset we utilized during our research, then we describe how our method works. In Section 3, we report the experimental results on a hold-out dataset and we make a comparison to the human-level performance. Section 4 describes our concluding thoughts.