Division of Cardiology, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, 1873 Rama 4 Road, Pathumwan, Bangkok, 10330, Thailand.
Division of Cardiovascular Medicine, Center of Excellence in Arrhythmia Research, Cardiac Center, King Chulalongkorn Memorial Hospital, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Sci Rep. 2024 Jul 10;14(1):15882. doi: 10.1038/s41598-024-66247-y.
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.
多项研究表明，人工智能（AI）在左心室肥厚（LVH）筛查方面具有一定的应用价值。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 标准在诊断准确性方面的差异。我们旨在全面综述新开发的 AI 工具在 LVH 诊断中的应用。我们检索了 MEDLINE、EMBASE 和 Cochrane 数据库，以获取截至 2023 年 5 月的相关研究。纳入的研究为评估 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下面积（ROC）和汇总敏感度和特异度评估了 AI 与标准标准相比的性能。共有 66479 名伴有和不伴有 LVH 的参与者纳入研究。AI 的使用与诊断准确性的提高相关，汇总 ROC（SROC）为 0.87。Sokolow-Lyon 和 Cornell 标准的准确性较低（分别为 0.68 和 0.60）。AI 的敏感度和特异度分别为 69%和 87%。相比之下，Sokolow-Lyon 的特异性为 92%，敏感度为 25%，而 Cornell 的特异性为 94%，敏感度为 19%。这表明 AI 算法在 LVH 检测方面具有更高的诊断准确性。我们的研究表明，基于 AI 的方法在 LVH 诊断方面具有更高的诊断准确性，敏感性显著提高。这些发现为 AI 作为一种有前途的 LVH 检测工具提供了验证。
