ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review

• Published in: Computers in biology and medicine Vol. 172; p. 108235
• Main Authors: Moreno-Sánchez, Pedro A., García-Isla, Guadalupe, Corino, Valentina D.A., Vehkaoja, Antti, Brukamp, Kirsten, van Gils, Mark, Mainardi, Luca
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.04.2024; Elsevier Limited
• Subjects: Algorithms; Artificial Intelligence; Bias; Cardiology; Cardiovascular disease; Cardiovascular diseases; Cardiovascular diseases (CVD); Cardiovascular Diseases - diagnosis; Classification; Decision making; Deep learning; Diagnosis; ECG; EKG; Electrocardiography; Ethical, Legal and societal implications (ELSI); Ethics; Explainable Artificial Intelligence (XAI); Humans; Internal Medicine; Literature reviews; Machine Learning; Medical diagnosis; Medical prognosis; Morbidity; Mortality; Other; Prognosis; Quality of Life; R&D; Research & development; Systematic review; Trustworthiness; Trustworthy Artificial Intelligence; Vein & artery diseases; Deep learning; Explainable Artificial Intelligence (XAI); Bias; Machine learning; ECG; Trustworthy Artificial Intelligence; Ethical, Legal and societal implications (ELSI); Cardiovascular diseases (CVD); Trustworthy artificial intelligence; deep learning; Ethical; Explainable artificial intelligence (XAI); Legal and societal implications (ELSI)
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108235

Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload and potential diagnostic inaccuracies. Data-driven approaches, such as machine learning (ML) and deep learning (DL) have emerged to improve existing computer-assisted solutions and enhance physicians’ ECG interpretation of the complex mechanisms underlying CVD. However, many ML and DL models used to detect ECG-based CVD suffer from a lack of explainability, bias, as well as ethical, legal, and societal implications (ELSI). Despite the critical importance of these Trustworthy Artificial Intelligence (AI) aspects, there is a lack of comprehensive literature reviews that examine the current trends in ECG-based solutions for CVD diagnosis or prognosis that use ML and DL models and address the Trustworthy AI requirements. This review aims to bridge this knowledge gap by providing a systematic review to undertake a holistic analysis across multiple dimensions of these data-driven models such as type of CVD addressed, dataset characteristics, data input modalities, ML and DL algorithms (with a focus on DL), and aspects of Trustworthy AI like explainability, bias and ethical considerations. Additionally, within the analyzed dimensions, various challenges are identified. To these, we provide concrete recommendations, equipping other researchers with valuable insights to understand the current state of the field comprehensively. •Data-driven solutions with ECG as the primary input for CVD diagnosis and prognosis.•PRISMA methodology followed for paper identification, screening, and review.•Multidimensional analysis tackling CVD types, data characteristics, AI algorithms, and Trustworthy AI.•In-depth analysis of Deep Learning architectures and explainable AI techniques.•Comprehensive discussion identifying challenges in the field and providing tailored recommendations.