Machine Learning in Cardiology: A Potential Real-World Solution in Low- and Middle-Income Countries

Abstract

Artificial intelligence (AI) and machine learning (ML) is a promising field of cardiovascular medicine. Many AI tools have been shown to be efficacious with a high level of accuracy. Yet, their use in real life is not well established. In the era of health technology and data science, it is crucial to consider how these tools could improve healthcare delivery. This is particularly important in countries with limited resources, such as low- and middle-income countries (LMICs). LMICs have many barriers in the care continuum of cardiovascular diseases (CVD), and big portion of these barriers come from scarcity of resources, mainly financial and human power constraints. AI/ML could potentially improve healthcare delivery if appropriately applied in these countries. Expectedly, the current literature lacks original articles about AI/ML originating from these countries. It is important to start early with a stepwise approach to understand the obstacles these countries face in order to develop AI/ML-based solutions. This could be detrimental to many patients’ lives, in addition to other expected advantages in other sectors, including the economy sector. In this report, we aim to review what is known about AI/ML in cardiovascular medicine, and to discuss how it could benefit LMICs.

Keywords:

• artificial intelligence
• machine learning
• cardiology
• low
• middle
• income
• countries

Abbreviations

AF, Atrial fibrillation; AI, Artificial Intelligence; AUC, Area under the curve; CVD, Cardiovascular disease; HR, Hazard ratios; LMICs, Low- and middle-income countries; LVEF, Left ventricular ejection fraction; ML, Machine learning; NSR, Normal sinus rhythm; RCT, Randomized clinical trials; ROC, Receiver operating characteristic; SL, Supervised learning; SLUL, Unsupervised learning; ULUS, United States; USUS-FDA, US Food and Drug Administration.

Acknowledgments

We would like to thank Dr. Mohamad Adnan Alkhouli (Mayo Clinic, Cardiovascular Medicine) for his efforts in reviewing the manuscript and for his valuable feedback.

Disclosure

SKH has received honoraria from Novartis, Mallinckrodt, Pfizer, and Janssen. SKH has received travel grants from MSD, Gilead, Sanofi, and Amgen. PAN reports Licensed IP (potential Royalty) from Anumana, during the conduct of the study; In addition, Dr Peter Noseworthy has a patent Various AI-ECG algorithms pending to Anumana. The authors report no other conflicts of interest in this work.

Additional information

Funding

There is no funding to report.