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.

中文翻译：

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基于心电图的数据驱动的心血管疾病诊断和预后解决方案：系统评价

心血管疾病 (CVD) 是全球死亡的主要原因，会导致严重的发病率和生活质量下降。心电图（ECG）在CVD诊断、预后和预防中发挥着至关重要的作用；然而，不同的挑战仍然存在，例如对能够准确解释心电图的熟练心脏病专家的需求不断增加，而未得到满足。这会导致更高的工作量和潜在的诊断不准确。机器学习 (ML) 和深度学习 (DL) 等数据驱动方法的出现，可以改进现有的计算机辅助解决方案，并增强医生对 CVD 复杂机制的心电图解释。然而，许多用于检测基于心电图的 CVD 的 ML 和 DL 模型缺乏可解释性、偏见以及道德、法律和社会影响 (ELSI)。尽管这些值得信赖的人工智能 (AI) 方面至关重要，但仍缺乏全面的文献综述来研究基于 ECG 的 CVD 诊断或预后解决方案的当前趋势，这些解决方案使用 ML 和 DL 模型并满足可信赖的 AI 要求。本综述旨在通过提供系统综述来弥合这一知识差距，对这些数据驱动模型的多个维度进行整体分析，例如所处理的 CVD 类型、数据集特征、数据输入模式、ML 和 DL 算法（重点关注DL），以及可信赖人工智能的各个方面，例如可解释性、偏见和道德考虑。此外，在分析的维度中，还发现了各种挑战。为此，我们提供了具体的建议，为其他研究人员提供了宝贵的见解，以全面了解该领域的现状。