利用人工智能革新 LVH 检测:人工智能心跳项目。

IF 3.3 2区 医学 Q1 PERIPHERAL VASCULAR DISEASE
Journal of Hypertension Pub Date : 2025-01-01 Epub Date: 2024-10-07 DOI:10.1097/HJH.0000000000003885
Zafar Aleem Suchal, Noor Ul Ain, Azra Mahmud
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引用次数: 0

摘要

许多研究表明,人工智能(AI)在诊断左心室肥厚(LVH)方面具有实用性和前景。本研究旨在进行一项荟萃分析,比较人工智能工具与心电图标准(包括临床实践中最常用于检测左心室肥厚的索科洛-里昂标准和康奈尔标准)的准确性。符合纳入标准的研究共有九项,其中测试数据集的样本量为 31 657 例患者,训练数据集的样本量为 100 271 例患者。采用层次模型进行了元分析,计算了汇总的敏感性、特异性、准确性以及 95% 置信区间(95% CI)。为确保结果不受某项研究的影响,对所有三项结果都采用了 "撇除一方法 "进行敏感性分析。AI 与更高的汇总估计值相关;准确性为 80.50(95% CI:80.4-80.60),灵敏度为 89.29(95% CI:89.25-89.33),特异性为 93.32(95% CI:93.26-93.38)。结果显示,虽然准确性和特异性没有变化,但调整后的汇总灵敏度为 53.16(95% CI:52.92-53.40)。与传统的心电图标准相比,人工心肌梗死的诊断准确性和灵敏度更高。人工心电图有望成为在不同人群中准确检测 LVH 的可靠而高效的工具。在高血压人群中,尤其是在资源匮乏的环境中,还需要进一步的研究来测试 AI 模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Revolutionizing LVH detection using artificial intelligence: the AI heartbeat project.

Many studies have shown the utility and promise of artificial intelligence (AI), for the diagnosis of left ventricular hypertrophy (LVH). The aim of the present study was to conduct a meta-analysis to compare the accuracy of AI tools to electrocardiographic criteria, including Sokolow-Lyon and the Cornell, most commonly used for the detection of LVH in clinical practice. Nine studies meeting the inclusion criteria were selected, comprising a sample size of 31 657 patients in the testing and 100 271 in the training datasets. Meta-analysis was performed using a hierarchal model, calculating the pooled sensitivity, specificity, accuracy, along with the 95% confidence intervals (95% CIs). To ensure that the results were not skewed by one particular study, a sensitivity analysis using the 'leave-out-one approach' was adopted for all three outcomes. AI was associated with greater pooled estimates; accuracy, 80.50 (95% CI: 80.4-80.60), sensitivity, 89.29 (95% CI: 89.25-89.33) and specificity, 93.32 (95% CI: 93.26-93.38). Adjusting for weightage of individual studies on the outcomes, the results showed that while accuracy and specificity were unchanged, the adjusted pooled sensitivity was 53.16 (95% CI: 52.92-53.40). AI demonstrates higher diagnostic accuracy and sensitivity compared with conventional ECG criteria for LVH detection. AI holds promise as a reliable and efficient tool for the accurate detection of LVH in diverse populations. Further studies are needed to test AI models in hypertensive populations, particularly in low resource settings.

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来源期刊
Journal of Hypertension
Journal of Hypertension 医学-外周血管病
CiteScore
7.90
自引率
6.10%
发文量
1389
审稿时长
3 months
期刊介绍: The Journal of Hypertension publishes papers reporting original clinical and experimental research which are of a high standard and which contribute to the advancement of knowledge in the field of hypertension. The Journal publishes full papers, reviews or editorials (normally by invitation), and correspondence.
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