# Automated Classification of Left Ventricular Hypertrophy on Cardiac MRI
## 4 Discussion and Conclusions  
Cardiovascular diseases are the leading causes of death around the world [1; 2; 43]. LVH is a well-recognized independent risk factor for several cardiovascular complications [5]. The diagnosis of LVH can be challenging. For this, there are some methods used in clinical practice such as electrocardiography, echocardiography, and CMR. CMR is a non-invasive tool for diagnosing myocardial pathologies. CMR-based hypertrophy detection can be more efficient and reliable and may improve the diagnostic method in order to recognize LVH in an earlier stage. We developed a deep-learning-based algorithm for identifying left ventricular hypertrophy during a CMR examination (on-site) and for helping the diagnostic process following the examination (off-site). The on-site application can save time, if the algorithm indicates the presence of LVH right after the long-axis measurements; therefore, some additional, necessary images could be acquired and contrast administration should be applied. With the use of on-site application, the CMR protocol can be changed during the scanning, in order to avoid the need to call back the patient for an additional CMR examination to provide the correct diagnosis. Nevertheless, if the algorithm is used during post-process evaluation, it can warn the reader that LVH is present, so the diagnostic accuracy can be improved. This is important because the identification of the incipient or milder form of LVH is difficult for less-experienced readers, and early detection of LVH and subsequent therapy are key factors in reducing cardiovascular morbidity and mortality [38,44]. Our algorithm achieved a performance close to the medical experts' (readers) scores. Our comparison was based on the F1-score, precision, and recall. The model we implemented was an ensemble model. Each view had a separate extractor, and the features extracted from the acquired images were concatenated. Then, an ensemble classifier takes the concatenated features as the input and calculates the probability of having LVH. The dataset was collected from the Heart and Vascular Center of Semmelweis University, and it contains the raw image scans with all available views (long-axis and short-axis cine images) and the corresponding pathologies. Our algorithm had a recall rate of 90% when the combination of long-axis views was used as the input. In the case of the combination of long-axis views and short-axis views, we had a 96% rate. The corresponding F1-scores were 89% and 91%, respectively. High recall is beneficial, because fewer LVH cases will be left undiagnosed. False positives (predicted as LVH, yet normal) can be discarded by the experts supervising the examination. In order to judge the applicability of our method, we established a baseline by measuring the scores of medical experts. The measurement involved six readers with varying levels of experience. The measurement was designed to simulate a realistic clinical scenario where the reader has no clear reference to the real case, but has access to the images of full CMR scans. To make it more realistic, we included several other diseases in addition to LVH. We included diseases that appear frequently in clinical practice, and the readers were blinded to the purpose of the study. There are three main outcomes of the human experiment: (1) the differences among the scores (F1-score, recall, etc.) of the readers were surprisingly small; (2) recall was the highest value indicating that the readers had a bias toward having a cardiac disease; (3) we obtained the baseline values for the scores (F1-score--95%, recall--98%); see Table 2. High recall was also achieved by our algorithm in the case of the combined long-axis and short-axis model. Figures 5 and 6 indicate that our algorithm can already be advantageous in clinical practice even though there is still room for improvement. We claim that by using a larger dataset, the gap can be bridged and that this method can be a good candidate to become part of the daily clinical routine during CMR examinations.