Leopoldo Ordine, Grazia Canciello, Felice Borrelli, Raffaella Lombardi, Salvatore Di Napoli, Roberto Polizzi, Cristina Falcone, Brigida Napolitano, Lorenzo Moscano, Alessandra Spinelli, Elio Masciari, Giovanni Esposito, Maria-Angela Losi
{"title":"人工智能驱动的心电图:肥厚型心肌病管理的创新。","authors":"Leopoldo Ordine, Grazia Canciello, Felice Borrelli, Raffaella Lombardi, Salvatore Di Napoli, Roberto Polizzi, Cristina Falcone, Brigida Napolitano, Lorenzo Moscano, Alessandra Spinelli, Elio Masciari, Giovanni Esposito, Maria-Angela Losi","doi":"10.1016/j.tcm.2024.08.002","DOIUrl":null,"url":null,"abstract":"<p><p>Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its heterogeneous phenotype and clinical course. Artificial Intelligence (AI) and Machine Learning (ML) techniques hold promise in transforming the role of Electrocardiography (ECG) in HCM diagnosis, prognosis, and management. AI, including Deep Learning (DL), enables computers to learn patterns from data, allowing for the development of models capable of analyzing ECG signals. DL models, such as convolutional neural networks, have shown promise in accurately identifying HCM-related abnormalities in ECGs, surpassing traditional diagnostic methods. In diagnosing HCM, ML models have demonstrated high accuracy in distinguishing between HCM and other cardiac conditions, even in cases with normal ECG findings. Additionally, AI models have enhanced risk assessment by predicting arrhythmic events leading to sudden cardiac death and identifying patients at risk for atrial fibrillation and heart failure. These models incorporate clinical and imaging data, offering a comprehensive evaluation of patient risk profiles. Challenges remain, including the need for larger and more diverse datasets to improve model generalizability and address imbalances inherent in rare event prediction. Nevertheless, AI-driven approaches have the potential to revolutionize HCM management by providing timely and accurate diagnoses, prognoses, and personalized treatment strategies based on individual patient risk profiles. This review explores the current landscape of AI applications in ECG analysis for HCM, focusing on advancements in AI methodologies and their specific implementation in HCM care.</p>","PeriodicalId":51199,"journal":{"name":"Trends in Cardiovascular Medicine","volume":" ","pages":""},"PeriodicalIF":7.3000,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management.\",\"authors\":\"Leopoldo Ordine, Grazia Canciello, Felice Borrelli, Raffaella Lombardi, Salvatore Di Napoli, Roberto Polizzi, Cristina Falcone, Brigida Napolitano, Lorenzo Moscano, Alessandra Spinelli, Elio Masciari, Giovanni Esposito, Maria-Angela Losi\",\"doi\":\"10.1016/j.tcm.2024.08.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its heterogeneous phenotype and clinical course. Artificial Intelligence (AI) and Machine Learning (ML) techniques hold promise in transforming the role of Electrocardiography (ECG) in HCM diagnosis, prognosis, and management. AI, including Deep Learning (DL), enables computers to learn patterns from data, allowing for the development of models capable of analyzing ECG signals. DL models, such as convolutional neural networks, have shown promise in accurately identifying HCM-related abnormalities in ECGs, surpassing traditional diagnostic methods. In diagnosing HCM, ML models have demonstrated high accuracy in distinguishing between HCM and other cardiac conditions, even in cases with normal ECG findings. Additionally, AI models have enhanced risk assessment by predicting arrhythmic events leading to sudden cardiac death and identifying patients at risk for atrial fibrillation and heart failure. These models incorporate clinical and imaging data, offering a comprehensive evaluation of patient risk profiles. Challenges remain, including the need for larger and more diverse datasets to improve model generalizability and address imbalances inherent in rare event prediction. Nevertheless, AI-driven approaches have the potential to revolutionize HCM management by providing timely and accurate diagnoses, prognoses, and personalized treatment strategies based on individual patient risk profiles. This review explores the current landscape of AI applications in ECG analysis for HCM, focusing on advancements in AI methodologies and their specific implementation in HCM care.</p>\",\"PeriodicalId\":51199,\"journal\":{\"name\":\"Trends in Cardiovascular Medicine\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":7.3000,\"publicationDate\":\"2024-08-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Trends in Cardiovascular Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1016/j.tcm.2024.08.002\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CARDIAC & CARDIOVASCULAR SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Trends in Cardiovascular Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.tcm.2024.08.002","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
Artificial intelligence-driven electrocardiography: Innovations in hypertrophic cardiomyopathy management.
Hypertrophic Cardiomyopathy (HCM) presents a complex diagnostic and prognostic challenge due to its heterogeneous phenotype and clinical course. Artificial Intelligence (AI) and Machine Learning (ML) techniques hold promise in transforming the role of Electrocardiography (ECG) in HCM diagnosis, prognosis, and management. AI, including Deep Learning (DL), enables computers to learn patterns from data, allowing for the development of models capable of analyzing ECG signals. DL models, such as convolutional neural networks, have shown promise in accurately identifying HCM-related abnormalities in ECGs, surpassing traditional diagnostic methods. In diagnosing HCM, ML models have demonstrated high accuracy in distinguishing between HCM and other cardiac conditions, even in cases with normal ECG findings. Additionally, AI models have enhanced risk assessment by predicting arrhythmic events leading to sudden cardiac death and identifying patients at risk for atrial fibrillation and heart failure. These models incorporate clinical and imaging data, offering a comprehensive evaluation of patient risk profiles. Challenges remain, including the need for larger and more diverse datasets to improve model generalizability and address imbalances inherent in rare event prediction. Nevertheless, AI-driven approaches have the potential to revolutionize HCM management by providing timely and accurate diagnoses, prognoses, and personalized treatment strategies based on individual patient risk profiles. This review explores the current landscape of AI applications in ECG analysis for HCM, focusing on advancements in AI methodologies and their specific implementation in HCM care.
期刊介绍:
Trends in Cardiovascular Medicine delivers comprehensive, state-of-the-art reviews of scientific advancements in cardiovascular medicine, penned and scrutinized by internationally renowned experts. The articles provide authoritative insights into various topics, encompassing basic mechanisms, diagnosis, treatment, and prognosis of heart and blood vessel disorders, catering to clinicians and basic scientists alike. The journal covers a wide spectrum of cardiology, offering profound insights into aspects ranging from arrhythmias to vasculopathies.