Model for classification of heart failure severity in patients with hypertrophic cardiomyopathy using a deep neural network algorithm with a 12-lead electrocardiogram.

OBJECTIVES: In hypertrophic cardiomyopathy (HCM), specific ECG abnormalities are observed. Therefore, ECG is a valuable screening tool. Although several studies have reported on estimating the risk of developing fatal arrhythmias from ECG findings, the use of ECG to identify the severity of heart failure (HF) by applying deep learning (DL) methods has not been established. METHODS: We assessed whether data-driven machine-learning methods could effectively identify the severity of HF in patients with HCM. A residual neural network-based model was developed using 12-lead ECG data from 218 patients with HCM and 245 patients with non-HCM, categorised them into two (mild-to-moderate and severe) or three (mild, moderate and severe) severities of HF. These severities were defined according to the New York Heart Association functional class and levels of the N-terminal prohormone of brain natriuretic peptide. In addition, the patients were divided into groups according to Kansas City Cardiomyopathy Questionnaire (KCCQ)-12. A transfer learning method was applied to resolve the issue of the low number of target samples. The model was trained in advance using PTB-XL, which is an open ECG dataset. RESULTS: The model trained with our dataset achieved a weighted average F1 score of 0.745 and precision of 0.750 for the mild-to-moderate class samples. Similar results were obtained for grouping based on KCCQ-12. Through data analyses using the Guided Gradient Weighted-Class Activation Map and Integrated Gradients, QRS waves were intensively highlighted among true-positive mild-to-moderate class cases, while the highlighted part was highly variable among true-positive severe class cases. CONCLUSIONS: We developed a model for classifying HF severity in patients with HCM using a deep neural network algorithm with 12-lead ECG data. Our findings suggest that applications of this DL algorithm for using 12-lead ECG data may be useful to classify the HF status in patients with HCM.

Establishment of tyrosinase sequence database in normally pigmented Indians and Japanese for rapid determination of novel mutations.

BACKGROUND: Many mutations of the tyrosinase gene have been reported in oculocutaneous albinism type I (OCA1) patient. In the future, a greater number of novel mutations will be found as the search for pathological mutations in the tyrosinase genes of OCA patients from various ethnic origins. For rapid determination in future whether an observed mutation is a polymorphism or a novel pathological one, sequence databases of the gene of various ethnic people are needed. OBJECTIVE: We established a sequence database of the tyrosinase gene of Japanese as well as Indian people. METHOD: We collected DNA from 109 Japanese and 103 Indians with normal pigmentation and analyzed their tyrosinase gene using a direct sequencing method. RESULT: The database shows an apparent difference between the two ethnic groups in polymorphisms of the tyrosinase gene namely, Q402 allele, Y192 allele and IV2+24 insT were found in the Indian population, but not in the Japanese. On the other hand, some Japanese had IV2-21 insT but none of the Indians did. The database supports the notion that the tyrosinase gene evolved and extended separately in the two ethnic groups. And the developing database confirmed that the reported mutations causing Indian and Japanese OCA were not among the polymorphisms in the database, which conversely gives genetical proof of the "genuine" pathological mutations. CONCLUSION: Eventually, the sequence database we established will contribute to demonstrating novel mutations of albinism in Indians and Japanese.