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In an order of the abstract structure of the human body from inside to outside, this paper lists the state-of-the-art research advances of artificial intelligence (AI) especially deep neural network in ECG analysis from aspects of electrical conduction system and the structure of the heart ( including heart valves, myocardium, coronary arteries, etc. ), other systems out of the heart, ect. This paper introduces the application of AI-enabled ECG analysis technology in identifying arhythmias, electrolyte disturbances, valvular heart disease, coronary heart disease, and diseases of the circulation system and digestive system. This paper also covers its innovative application in the field of personal identification, and how to use AI technologies such as transfer learning in practical research. This paper is aimed to promote more cross-field cooperations, explore deep values of ECG, and help to realize the screening, diagnosis and treatment of multi-system diseases. [ABSTRACT FROM AUTHOR]

Since the outbreak of corona virus disease 2019, deep learning has been widely used in predicting epidemic trends, screening high-risk groups and making early diagnosis. Novel coronavirus infection mainly causes pulmonary diseases, and also affects cardiovascular system, resulting in electrocardiogram (ECG) abnormalities. ECG tracking image technology is a hot spot in the research of deep learning. Compared with traditional ECG judgment methods, its accuracy and sensitivity are both higher. It can be used in the effective diagnosis, clinical decision-making and prognosis evaluation of patients with novel coronavirus infection. This paper reviews on the ECG changes after novel coronavirus infection, and the advances of ECG tracking image technology applied in the auxiliary diagnosis of novel coronavirus infection, so as to provide references for clinical healthcare workers. [ABSTRACT FROM AUTHOR]

Since its first clinical application 120 years ago, electrocardiogram (ECG) has still been one of the most important routine examinations in clinical practice till now. The accelerated popularity of remote, portable ECG and cloud ECG platforms promotes ECG technology—from hospitals to the society, family and even every one. The arrival of body surface three-dimensional electroanatomical mapping visualizes the presentation of abstract ECG morphologies, and clearly reveals the origin of arrhythmia and ECG signal conduction process. This amounts to a “pathological anatomy” like gold-standard validation diagnosis for the ECG. Through deep learning, artificial intelligence (AI)-ECG can explore the fields that cannot be interpreted by ECG before, which greatly expands the connotation of ECG. This paper briefly reviews the development process of ECG technology from remote ECG, three-dimensional ECG and portable ECG to AI-ECG in the past 120 years, and looks forward to its development trend in the future. [ABSTRACT FROM AUTHOR]

Since its first clinical application 120 years ago, electrocardiogram (ECG) has still been one of the most important routine examinations in clinical practice till now. The accelerated popularity of remote, portable ECG and cloud ECG platforms promotes ECG technology—from hospitals to the society, family and even every one. The arrival of body surface three-dimensional electroanatomical mapping visualizes the presentation of abstract ECG morphologies, and clearly reveals the origin of arrhythmia and ECG signal conduction process. This amounts to a “pathological anatomy” like gold-standard validation diagnosis for the ECG. Through deep learning, artificial intelligence (AI)-ECG can explore the fields that cannot be interpreted by ECG before, which greatly expands the connotation of ECG. This paper briefly reviews the development process of ECG technology from remote ECG, three-dimensional ECG and portable ECG to AI-ECG in the past 120 years, and looks forward to its development trend in the future. [ABSTRACT FROM AUTHOR]

Valvular heart disease (VHD) is a common organic heart disease, and its incidence gradually increases with age. With the cross-integration of artificial intelligence and medical field, artificial intelligence algorithm models have become effective tools for identifying a variety of heart diseases. Artificial intelligence-enabled ECG (AI-ECG) diagnostic models can identify VHD patients by analyzing their ECG features, which may become an auxiliary tool for the identification of VHD in clinical practice. This paper summarizes the research progress of AI-ECG diagnostic models in the identification of VHD. [ABSTRACT FROM AUTHOR]

With the emerging and mutation of various pathogenic genes,the prevalence of corresponding genetic diseases also increases year by year. The penetrance of potentially pathogenic mutation is often atypical or the mutation manifests itself only in one’s later years,which tends to complicate and delay the diagnosis of genetic heart diseases. With the development and application of new technologies such as machine learning,artificial intelligence（ AI） has become a powerful tool in many fields including personalized medicine,medical imaging,and disease diagnosis. By using AI,we can specifically analyze and summarize the characteristic manifestations of ECG in patients with genetic arrhythmias,contributing to the early diagnosis of related diseases. This paper reviews the latest research progress of AI-enabled ECG utilized in the diagnosis of inherited arrhythmias,mainly including congenital long QT syndrome,Brugada syndrome,and catecholaminergic polymorphic ventricular tachycardia. [ABSTRACT FROM AUTHOR]