Khan Salman, Qayyum Komal, Qadeer Abdul, Khalid Maria, Anthony Somaan, Khan Wafa, Ghulam Moula, Jamil Zainab, Anthony Nouman
Cardiology, Rehman Medical Institute, Peshawar, PAK.
Cardiology, Northwest General Hospital and Research Centre, Peshawar, PAK.
Cureus. 2025 Feb 7;17(2):e78683. doi: 10.7759/cureus.78683. eCollection 2025 Feb.
Heart failure (HF) is the most common cause of death worldwide, characterized by low ejection fraction, substantial mortality, morbidity, and poor quality of life. Recent advancements in artificial intelligence (AI) present a promising avenue for enhancing diagnostic precision, particularly in the analysis of electrocardiogram (ECG) data. This systematic review and meta-analysis aim to synthesize current evidence on the diagnostic performance of AI models in detecting HF using ECG data. PubMed and Google Scholar databases were systematically searched from inception up to July 1, 2023, to identify original articles assessing the predictive ability of AI in HF diagnosis. A total of 218,202 participants were included, with individual studies ranging from 59 to 110,000 participants. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) for the 13 included studies, with a 97.5% confidence interval (CI), were 0.93 (CI: 0.81-0.98), 0.95 (CI: 0.89-0.97), and 303.65 (CI: 53.12-1734), respectively. The sensitivity and specificity ranged from 0.12 to 1.00 and 0.66 to 1.00, respectively, indicating substantial variability in AI model performance, which may impact their generalizability and clinical reliability. AI-based algorithms utilizing ECG data are a reliable, accurate, and promising tool for the screening, detection, and monitoring of HF. However, further prospective studies are needed, particularly randomized controlled trials and large-scale longitudinal studies across diverse populations, to evaluate the long-term clinical impact, generalizability, and real-world applicability of these AI-driven diagnostic tools.
心力衰竭（HF）是全球最常见的死亡原因，其特征为射血分数低、死亡率高、发病率高且生活质量差。人工智能（AI）的最新进展为提高诊断准确性提供了一条有前景的途径，尤其是在心电图（ECG）数据分析方面。本系统评价和荟萃分析旨在综合当前关于使用ECG数据检测HF的AI模型诊断性能的证据。对PubMed和谷歌学术数据库从创建到2023年7月1日进行了系统检索，以识别评估AI在HF诊断中预测能力的原始文章。共纳入218,202名参与者，单个研究的参与者人数从59至110,000不等。纳入的13项研究的合并敏感性、特异性和诊断比值比（DOR）及其97.5%置信区间（CI）分别为0.93（CI：0.81 - 0.98）、0.95（CI：0.89 - 0.97）和303.65（CI：53.12 - 1734）。敏感性和特异性分别在0.12至1.00和0.66至1.00之间，表明AI模型性能存在很大差异，这可能会影响其可推广性和临床可靠性。基于AI的利用ECG数据的算法是用于HF筛查、检测和监测的可靠、准确且有前景的工具。然而，需要进一步的前瞻性研究，特别是跨不同人群的随机对照试验和大规模纵向研究，以评估这些由AI驱动的诊断工具的长期临床影响、可推广性和实际适用性。
