The Application of Artificial Intelligence in Atrial Fibrillation Patients: From Detection to Treatment

被引：2

作者：

Liang, Hanyang ^{[1
]}

Zhang, Han ^{[1
]}

Wang, Juan ^{[1
]}

Shao, Xinghui ^{[1
]}

Wu, Shuang ^{[1
]}

Lyu, Siqi ^{[1
]}

Xu, Wei ^{[1
]}

Wang, Lulu ^{[1
]}

Tan, Jiangshan ^{[1
]}

Wang, Jingyang ^{[1
]}

Yang, Yanmin ^{[1
]}

机构：

[1] Chinese Acad Med Sci & Peking Union Med Coll, Natl Ctr Cardiovasc Dis, Natl Clin Res Ctr Cardiovasc Dis, Emergency Ctr,Fuwai Hosp,State Key Lab Cardiovasc, Beijing 100037, Peoples R China

来源：

REVIEWS IN CARDIOVASCULAR MEDICINE | 2024年 / 25卷 / 07期

关键词：

artificial intelligence; atrial fibrillation; machine learning; deep learning; MOBILE HEALTH TECHNOLOGY; RISK SCORE; PREDICTION; CARE; THROMBOEMBOLISM; RECURRENCE; MANAGEMENT; OUTCOMES; DIGOXIN; IMPACT;

D O I：

10.31083/j.rcm2507257

中图分类号：

R5 [内科学];

学科分类号：

1002 ; 100201 ;

摘要：

Atrial fibrillation (AF) is the most prevalent arrhythmia worldwide. Although the guidelines for AF have been updated in recent years, its gradual onset and associated risk of stroke pose challenges for both patients and cardiologists in real-world practice. Artificial intelligence (AI) is a powerful tool in image analysis, data processing, and for establishing models. It has been widely applied in various medical fields, including AF. In this review, we focus on the progress and knowledge gap regarding the use of AI in AF patients and highlight its potential throughout the entire cycle of AF management, from detection to drug treatment. More evidence is needed to demonstrate its ability to improve prognosis through high-quality randomized controlled trials.

引用

页数：13

共 107 条

[1] Machine Learning to Classify Intracardiac Electrical Patterns During Atrial Fibrillation Machine Learning of Atrial Fibrillation [J].

Alhusseini, Mahmood I. ;

Abuzaid, Firas ;

Rogers, Albert J. ;

Zaman, Junaid A. B. ;

Baykaner, Tina ;

Clopton, Paul ;

Bailis, Peter ;

Zaharia, Matei ;

Wang, Paul J. ;

Rappel, Wouter-Jan ;

Narayan, Sanjiv M. .

CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY, 2020, 13 (08) :E008160

[2] Simple Risk Model Predicts Incidence of Atrial Fibrillation in a Racially and Geographically Diverse Population: the CHARGE-AF Consortium [J].

Alonso, Alvaro ;

Krijthe, Bouwe P. ;

Aspelund, Thor ;

Stepas, Katherine A. ;

Pencina, Michael J. ;

Moser, Carlee B. ;

Sinner, Moritz F. ;

Sotoodehnia, Nona ;

Fontes, Joao D. ;

Janssens, A. Cecile J. W. ;

Kronmal, Richard A. ;

Magnani, Jared W. ;

Witteman, Jacqueline C. ;

Chamberlain, Alanna M. ;

Lubitz, Steven A. ;

Schnabel, Renate B. ;

Agarwal, Sunil K. ;

McManus, David D. ;

Ellinor, Patrick T. ;

Larson, Martin G. ;

Burke, Gregory L. ;

Launer, Lenore J. ;

Hofman, Albert ;

Levy, Daniel ;

Gottdiener, John S. ;

Kaeaeb, Stefan ;

Couper, David ;

Harris, Tamara B. ;

Soliman, Elsayed Z. ;

Stricker, Bruno H. C. ;

Gudnason, Vilmundur ;

Heckbert, Susan R. ;

Benjamin, Emelia J. .

JOURNAL OF THE AMERICAN HEART ASSOCIATION, 2013, 2 (02) :e000102

[3]

Asai Y, 2023, BIOL PHARM BULL, V46, P614, DOI 10.1248/bpb.b22-00823

[4] Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm [J].

Asiimwe, Innocent G. ;

Blockman, Marc ;

Cohen, Karen ;

Cupido, Clint ;

Hutchinson, Claire ;

Jacobson, Barry ;

Lamorde, Mohammed ;

Morgan, Jennie ;

Mouton, Johannes P. ;

Nakagaayi, Doreen ;

Okello, Emmy ;

Schapkaitz, Elise ;

Sekaggya-Wiltshire, Christine ;

Semakula, Jerome R. ;

Waitt, Catriona ;

Zhang, Eunice J. ;

Jorgensen, Andrea L. ;

Pirmohamed, Munir .

CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY, 2022, 11 (01) :20-29

[5] An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction [J].

Attia, Zachi, I ;

Noseworthy, Peter A. ;

Lopez-Jimenez, Francisco ;

Asirvatham, Samuel J. ;

Deshmukh, Abhishek J. ;

Gersh, Bernard J. ;

Carter, Rickey E. ;

Yao, Xiaoxi ;

Rabinstein, Alejandro A. ;

Erickson, Brad J. ;

Kapa, Suraj ;

Friedman, Paul A. .

LANCET, 2019, 394 (10201) :861-867

[6] Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: A proof of concept study [J].

Attia, Zachi, I ;

Sugrue, Alan ;

Asirvatham, Samuel J. ;

Ackerman, Michael J. ;

Kapa, Suraj ;

Friedman, Paul A. ;

Noseworthy, Peter A. .

PLOS ONE, 2018, 13 (08)

[7] Evaluating Recommendations About Atrial Fibrillation for Patients and Clinicians Obtained From Chat-Based Artificial Intelligence Algorithms [J].

Azizi, Zahra ;

Alipour, Pouria ;

Gomez, Sofia ;

Broadwin, Cassandra ;

Islam, Sumaiya ;

Sarraju, Ashish ;

Rogers, A. J. ;

Sandhu, Alexander T. ;

Rodriguez, Fatima .

CIRCULATION-ARRHYTHMIA AND ELECTROPHYSIOLOGY, 2023, 16 (07) :415-417

[8] Novel Density Poincare Plot Based Machine Learning Method to Detect Atrial Fibrillation From Premature Atrial/Ventricular Contractions [J].

Bashar, Syed Khairul ;

Han, Dong ;

Zieneddin, Fearass ;

Ding, Eric ;

Fitzgibbons, Timothy P. ;

Walkey, Allan J. ;

McManus, David D. ;

Javidi, Bahram ;

Chon, Ki H. .

IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2021, 68 (02) :448-460

[9] Photoplethysmography wave morphology in patients with atrial fibrillation [J].

Basza, Mikolaj ;

Walag, Damian ;

Kowalczyk, Weronika ;

Bozym, Aleksandra ;

Ciurla, Michalina ;

Krzyzanowska, Malgorzata ;

Maciejewski, Cezary ;

Bojanowicz, Wojciech ;

Solinski, Mateusz ;

Koltowski, Lukasz .

PHYSIOLOGICAL MEASUREMENT, 2023, 44 (04)

[10] Phenotypes and outcomes in non-anticoagulated patients with atrial fibrillation: An unsupervised cluster analysis [J].

Bisson, Arnaud ;

Fawzy, Ameenathul M. ;

Romiti, Giulio Francesco ;

Proietti, Marco ;

Angoulvant, Denis ;

El-Bouri, Wahbi ;

Lip, Gregory Y. H. ;

Fauchier, Laurent .

ARCHIVES OF CARDIOVASCULAR DISEASES, 2023, 116 (6-7) :342-351

← 1 2 3 4 5 6 7 8 9 10 →