Cardiac Magnetic Resonance as Risk Stratification Tool in Non-Ischemic Dilated Cardiomyopathy Referred for Implantable Cardioverter Defibrillator Therapy—State of Art and Perspectives

Non-ischemic dilated cardiomyopathy (DCM) is a disease characterized by left ventricular dilation and systolic dysfunction. Patients with DCM are at higher risk for ventricular arrhythmias and sudden cardiac death (SCD). According to current international guidelines, left ventricular ejection fraction (LVEF) <= 35% represents the main indication for prophylactic implantable cardioverter defibrillator (ICD) implantation in patients with DCM. However, LVEF lacks sensitivity and specificity as a risk marker for SCD. It has been seen that the majority of patients with DCM do not actually benefit from the ICD implantation and, on the contrary, that many patients at risk of SCD are not identified as they have preserved or mildly depressed LVEF. Therefore, the use of LVEF as unique decision parameter does not maximize the benefit of ICD therapy. Multiple risk factors used in combination could likely predict SCD risk better than any single risk parameter. Several predictors have been proposed including genetic variants, electric indexes, and volumetric parameters of LV. Cardiac magnetic resonance (CMR) can improve risk stratification thanks to tissue characterization sequences such as LGE sequence, parametric mapping, and feature tracking. This review evaluates the role of CMR as a risk stratification tool in DCM patients referred for ICD

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy

Background: Machine learning (ML) and artificial intelligence are emerging as important components of precision medicine that enhance diagnosis and risk stratification. Risk stratification tools for hypertrophic cardiomyopathy (HCM) exist, but they are based on traditional statistical methods. The aim was to develop a novel machine learning risk stratification tool for the prediction of 5-year risk in HCM. The goal was to determine if its predictive accuracy is higher than the accuracy of the state-of-the-art tools. Method: Data from a total of 2302 patients were used. The data were comprised of demographic characteristics, genetic data, clinical investigations, medications, and disease-related events. Four classification models were applied to model the risk level, and their decisions were explained using the SHAP (SHapley Additive exPlanations) method. Unwanted cardiac events were defined as sustained ventricular tachycardia occurrence (VT), heart failure (HF), ICD activation, sudden cardiac death (SCD), cardiac death, and all-cause death. Results: The proposed machine learning approach outperformed the similar existing risk-stratification models for SCD, cardiac death, and all-cause death risk-stratification: it achieved higher AUC by 17%, 9%, and 1%, respectively. The boosted trees achieved the best performing AUC of 0.82. The resulting model most accurately predicts VT, HF, and ICD with AUCs of 0.90, 0.88, and 0.87, respectively. Conclusions: The proposed risk-stratification model demonstrates high accuracy in predicting events in patients with hypertrophic cardiomyopathy. The use of a machine-learning risk stratification model may improve patient management, clinical practice, and outcomes in general

Mining Themes in Clinical Notes to Identify Phenotypes and to Predict Length of Stay in Patients admitted with Heart Failure

Heart failure is a syndrome which occurs when the heart is not able to pump blood and oxygen to support other organs in the body. Identifying the underlying themes in the diagnostic codes and procedure reports of patients admitted for heart failure could reveal the clinical phenotypes associated with heart failure and to group patients based on their similar characteristics which could also help in predicting patient outcomes like length of stay. These clinical phenotypes usually have a probabilistic latent structure and hence, as there has been no previous work on identifying phenotypes in clinical notes of heart failure patients using a probabilistic framework and to predict length of stay of these patients using data-driven artificial intelligence-based methods, we apply natural language processing technique, topic modeling, to identify the themes present in diagnostic codes and in procedure reports of 1,200 patients admitted for heart failure at the University of Illinois Hospital and Health Sciences System (UI Health). Topic modeling identified twelve themes each in diagnostic codes and procedure reports which revealed information about different phenotypes related to various perspectives about heart failure, to study patients\u27 profiles and to discover new relationships among medical concepts. Each theme had a set of keywords and each clinical note was labeled with two themes - one corresponding to its diagnostic code and the other corresponding to its procedure reports along with their percentage contribution. We used these themes and their percentage contribution to predict length of stay. We found that the themes discovered in diagnostic codes and procedure reports using topic modeling together were able to predict length of stay of the patients with an accuracy of 61.1% and an Area under the Receiver Operating Characteristic Curve (ROC AUC) value of 0.828

Detection of heart pathology using deep learning methods

In the directions of modern medicine, a new area of processing and analysis of visual data is actively developing - a radio municipality - a computer technology that allows you to deeply analyze medical images, such as computed tomography (CT), magnetic resonance imaging (MRI), chest radiography (CXR), electrocardiography and electrocardiography. This approach allows us to extract quantitative texture signs from signals and distinguish informative features to describe the heart's pathology, providing a personified approach to diagnosis and treatment. Cardiovascular diseases (SVD) are one of the main causes of death in the world, and early detection is crucial for timely intervention and improvement of results. This experiment aims to increase the accuracy of deep learning algorithms to determine cardiovascular diseases. To achieve the goal, the methods of deep learning were considered used to analyze cardiograms. To solve the tasks set in the work, 50 patients were used who are classified by three indicators, 13 anomalous, 24 nonbeat, and 1 healthy parameter, which is taken from the MIT-BIH Arrhythmia database

Heart Rate Variability: A possible machine learning biomarker for mechanical circulatory device complications and heart recovery

Cardiovascular disease continues to be the number one cause of death in the United States, with heart failure patients expected to increase to \u3e8 million by 2030. Mechanical circulatory support (MCS) devices are now better able to manage acute and chronic heart failure refractory to medical therapy, both as bridge to transplant or as bridge to destination. Despite significant advances in MCS device design and surgical implantation technique, it remains difficult to predict response to device therapy. Heart rate variability (HRV), measuring the variation in time interval between adjacent heartbeats, is an objective device diagnostic regularly recorded by various MCS devices that has been shown to have significant prognostic value for both sudden cardiac death as well as all-cause mortality in congestive heart failure (CHF) patients. Limited studies have examined HRV indices as promising risk factors and predictors of complication and recovery from left ventricular assist device therapy in end-stage CHF patients. If paired with new advances in machine learning utilization in medicine, HRV represents a potential dynamic biomarker for monitoring and predicting patient status as more patients enter the mechanotrope era of MCS devices for destination therapy

개 심장 기능 검사를 위한 인공지능 보조 심전도 활용

학위논문(석사) -- 서울대학교대학원 : 수의과대학 수의학과, 2023. 2. 김민수.This study aimed to determine the proportion of patients with electrocardiographic changes and to suggest the clinical utility of AI-assisted single-lead ECG for primary screening of canine heart function. Data was obtained from 116 owned dogs referred to Veterinary Medical Teaching Hospital, Seoul National University from June 2020 to December 2021. Single lead ECG traces were recorded and initially interpreted by the provided machine-learning software (CARDIOBIRD®, ANIWARE Ltd., Hong Kong). Among the 116 traces, 36 (31%) had abnormal ECG findings, according to the AI software. The most common abnormalities were wide or tall QRS (n=20), atrioventricular block (AVB) (n=8), and sinus pause (n=4). All data were suitable for interpretation and compatible to manual interpretation by clinicians. Despite some limitations, the newly developed AI-assisted ECG has shown promise for the screening of heart diseases in veterinary emergency or primary hospital without board certified cardiologist.심전도 검사(ECG)는 심장질환을 진단하는데 유용한 검사이지만, 많은 임상환경에서 임상가들은 심전도의 검사해석의 모호함을 느끼고 있다. 이 연구는 인공 지능(AI) 보조 심전도 기기를 이용하여 심전도 이상이 발견된 환자의 비율을 확인하고, 응급 상황에서 간편한 스크리닝 진단도구로써 AI 보조, 단일유도(lead II) 심전도의 임상적 유용성을 제안하는 것을 목표로 하였다. 검사는 2020년 6월부터 2021년 12월까지 서울대학교 수의과대학 응급의학과에 내원한 116마리의 환자로부터 측정되었다. 본 연구에 사용된 AI 기반 심전도 기기에(CARDIOBIRD®, ANIWARE Ltd., 홍콩) 따르면 116개의 심전도 기록 중 36마리(31%)가 비정상적인 심전도 소견을 보였다. 가장 흔한 이상은 넓거나 높은 QRS (n=17), 방실 차단(AVB) (n=8), 동정지 (n=4)였다. 모든 데이터는 해석에 적합한 품질을 보였으며 임상의의 해석과도 높은 일치율을 보였다. 개의 심장기능을 스크리닝하는데 있어 새로 개발된 스마트폰 기반의 인공지능 보조 심전도의 효용성이 평가되었다. 인공지능 보조 심전도는 특히 심장전문의가 없는 일차병원이나 응급실 환경에서 유용할 것으로 기대된다.Introduction 1 Materials and Methods 2 1. Animals 2 2. Ethical statement 2 3. ECG measuring device 2 4. ECG data analysis 2 5. Clinical setting 5 6. Diagnostic criteria 5 Results 7 1. Animals 7 2. AI-assisted ECG report 9 3. Comparison between standard ECG and AI-assisted ECG 11 4. Analysis of abnormal ECG findings 13 Discussion 15 Conclusion 20 References 21 Abstract in Korean 26석