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English(EN) ECGLight: Compute-Light Framework For Paper ECG Digitization and Myocardial Infarction Screening

新AI框架可数字化纸质心电图,用于远程心脏病发作筛查

研究人员开发了ECGLight,一个轻计算框架,旨在数字化纸质心电图(ECG)打印件并筛查心肌梗死(MI)。该设备端系统将智能手机拍摄的心电图转换为校准的12导联信号,即使在连接性或计算资源有限的远程诊所也能进行诊断。该框架在PTB-XL数据集上实现了95.51%的心肌梗死检测准确率,在ECG-Matrix数据集上实现了88.89%的氧耗心肌梗死(OMI)检测准确率,并且在纯CPU资源下每份心电图的运行时间不到30秒。 AI

影响 使低资源环境下的AI驱动心脏诊断成为可能,从而普及关键健康信息的获取。

排序理由 该集群包含一篇详细介绍用于医学诊断的新AI框架的研究论文。

在 arXiv cs.LG 阅读 →

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新AI框架可数字化纸质心电图,用于远程心脏病发作筛查

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Shreyasvi Natraj, Cyrus Achtari, Felice Gragnano, Andrea Milzi, Marco Valgimigli, Diego Paez-Granados ·

    ECGLight:用于纸质心电图数字化和心肌梗死筛查的计算轻量级框架

    arXiv:2607.07683v1 Announce Type: new Abstract: Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper ECG printouts for their analysis due to limited connectivity and computational capa…

  2. arXiv cs.LG TIER_1 English(EN) · Diego Paez-Granados ·

    ECGLight:用于纸质心电图数字化和心肌梗死筛查的计算轻量级框架

    Electrocardiography (ECG) is one of the most widely used tests for diagnosing cardiovascular disease. Yet several remote clinics still utilize paper ECG printouts for their analysis due to limited connectivity and computational capacity. As a result, vast numbers of physical ECGs…