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Abstract：
Several studies suggested the utility of artificial intelligence (AI) in screening left ventricular hypertrophy (LVH). We hence conducted systematic review and meta-analysis comparing diagnostic accuracy of AI to Sokolow-Lyon\'s and Cornell\'s criteria. Our aim was to provide a comprehensive overview of the newly developed AI tools for diagnosing LVH. We searched MEDLINE, EMBASE, and Cochrane databases for relevant studies until May 2023. Included were observational studies evaluating AI\'s accuracy in LVH detection. The area under the receiver operating characteristic curves (ROC) and pooled sensitivities and specificities assessed AI\'s performance against standard criteria. A total of 66,479 participants, with and without LVH, were included. Use of AI was associated with improved diagnostic accuracy with summary ROC (SROC) of 0.87. Sokolow-Lyon\'s and Cornell\'s criteria had lower accuracy (0.68 and 0.60). AI had sensitivity and specificity of 69% and 87%. In comparison, Sokolow-Lyon\'s specificity was 92% with a sensitivity of 25%, while Cornell\'s specificity was 94% with a sensitivity of 19%. This indicating its superior diagnostic accuracy of AI based algorithm in LVH detection. Our study demonstrates that AI-based methods for diagnosing LVH exhibit higher diagnostic accuracy compared to conventional criteria, with notable increases in sensitivity. These findings contribute to the validation of AI as a promising tool for LVH detection.
摘要：
一些研究表明人工智能（AI）在筛查左心室肥厚（LVH）中的实用性。因此，我们进行了系统评价和荟萃分析，比较了AI与Sokolow-Lyon和Cornell标准的诊断准确性。我们的目标是提供新开发的用于诊断LVH的AI工具的全面概述。我们搜索了MEDLINE,EMBASE,和Cochrane数据库进行相关研究，直到2023年5月。包括评估AI在LVH检测中的准确性的观察性研究。受试者工作特征曲线（ROC）下的面积以及合并的敏感性和特异性根据标准标准评估了AI的性能。共有66,479人参加,有和没有LVH，包括在内。使用AI与提高的诊断准确性相关，总结ROC（SROC）为0.87。索科洛-里昂标准和康奈尔标准的准确性较低(0.68和0.60)。AI的敏感性和特异性分别为69%和87%。相比之下,Sokolow-Lyon的特异性为92%，灵敏度为25%，而康奈尔的特异性为94%，敏感性为19%。这表明基于AI的算法在LVH检测中具有优越的诊断准确性。我们的研究表明，与传统标准相比，基于AI的LVH诊断方法具有更高的诊断准确性。灵敏度显著提高。这些发现有助于验证AI作为LVH检测的有前途的工具。
