The Role of Artificial Intelligence in Coronary Artery Disease and Atrial Fibrillation

Cardiovascular disease is the primary cause of morbidity and mortality worldwide. Cardiologists face challenges in clinical decision-making because of the demands for better treatment and the translation of the most recent scientific discoveries into executable strategies. On the contrary, the development of artificial intelligence (AI) and machine learning (ML) in recent decades has allowed healthcare professionals to make more effective and data-driven decisions.1 Therefore, cardiology is a specialty that requires the intervention of AI-based systems to provide precise management, particularly in chronic conditions such as coronary artery disease (CAD) and atrial fibrillation (AF).

[1]  Shulin Wu,et al.  An Artificial Intelligence-Enabled ECG Algorithm for Predicting the Risk of Recurrence in Patients with Paroxysmal Atrial Fibrillation after Catheter Ablation , 2023, Journal of clinical medicine.

[2]  R. Cuocolo,et al.  Must-have Qualities of Clinical Research on Artificial Intelligence and Machine Learning , 2022, Balkan medical journal.

[3]  Y. Zhang,et al.  [A deep-learning model for the assessment of coronary heart disease and related risk factors via the evaluation of retinal fundus photographs]. , 2022, Zhonghua xin xue guan bing za zhi.

[4]  L. Tavazzi,et al.  AFA-Recur: an ESC EORP AFA-LT registry machine-learning web calculator predicting atrial fibrillation recurrence after ablation , 2022, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[5]  Wan Azman Wan Ahmad,et al.  Short- and long-term mortality prediction after an acute ST-elevation myocardial infarction (STEMI) in Asians: A machine learning approach , 2021, PloS one.

[6]  F. Fernández‐Avilés,et al.  Machine Learning to Predict Stent Restenosis Based on Daily Demographic, Clinical, and Angiographic Characteristics. , 2020, The Canadian journal of cardiology.

[7]  Rickey E Carter,et al.  An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction , 2019, The Lancet.

[8]  Kipp W. Johnson,et al.  Deep learning for cardiovascular medicine: a practical primer. , 2019, European heart journal.

[9]  C.-C. Jay Kuo,et al.  Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography , 2015, Journal of medical imaging.

[10]  Christopher Piorkowski,et al.  Tailored Atrial Substrate Modification Based on Low-Voltage Areas in Catheter Ablation of Atrial Fibrillation , 2014, Circulation. Arrhythmia and electrophysiology.