发表机构
ETH Zürich; Swiss Paraplegic Research; University of Campania “Luigi Vanvitelli”; University of Italian Switzerland, Cardiocentro Ticino Institute（苏黎世联邦理工学院; 瑞士截瘫研究中心; 坎帕尼亚“路易吉·万维泰利”大学; 意大利瑞士大学提契诺心脏中心研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对偏远诊所因资源限制无法利用AI分析纸质心电图的问题，提出端到端轻量级设备端数字化到诊断管道，能将纸质心电图转换为校准信号并筛查病变，经数据集验证，准确率高且运行快，可普及纸质记录并提供决策支持。
AI 中文摘要
心电图（ECG）是诊断心血管疾病最常用的测试之一。然而，由于连接性和计算能力有限，一些偏远诊所仍使用纸质ECG打印件进行分析。因此，偏远地区获取的大量物理ECG无法通过当代基于人工智能（AI）的决策支持来访问，因为它们需要高计算资源或强大的高速互联网连接。这导致急性冠状动脉闭塞（ACS）等情况被忽视，再灌注治疗延迟。虽然先前的工作分别处理了数字化和诊断，并为它们使用了先进的AI模型，但仍然缺乏一个轻计算的设备端框架，该框架可以高保真地重建纸质ECG，同时准确支持多个临床相关终点。我们通过一个端到端的轻量级设备端数字化到诊断管道来满足这一需求，该管道将智能手机拍摄的纸质ECG照片或扫描转换为校准的12导联信号，并筛查心肌梗死（MI）病变，使用SHapley Additive exPlanations（SHAP）来支持可解释性。在PTB-XL数据集中的21,799份ECG上进行训练和评估，并在医院获取的ECG-Matrix数据集上进一步验证，完整系统仅使用CPU资源时，每份ECG的运行时间<30秒，在PTB-XL上进行MI检测的准确率为95.51%（F1 = 0.9519），在ECG-Matrix上进行OMI检测的准确率为88.89%（F1 = 0.8862）。这项工作表明，传统的纸质记录可以在世界任何地方可靠地普及，在数字ECG导出、连接性或高端计算不可用时提供可扩展的决策支持。
英文摘要
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 obtained in remote areas still remain incapable of being accessed by contemporary artificial-intelligence (AI)-based decision support as they require high computational resources or strong high-speed internet connectivity. This causes several cases where conditions like acute coronary occlusion (ACS) is overlooked and reperfusion therapy delayed. Although prior work has tackled digitization and diagnosis separately, and utilized advanced AI models for them, there still remains a lack of a compute-light, on-device framework that reconstructs paper ECGs at high fidelity, while accurately supporting multiple clinically relevant endpoints. We address this need with an end-to-end lightweight on-device digitization-to-diagnosis pipeline that converts a smartphone photo or scan of a paper ECG into a calibrated 12-lead signal and screens for Myocardial Infarction (MI) pathologies, with SHapley Additive exPlanations (SHAP) to support interpretability. Trained and evaluated on 21,799 ECGs from the PTB-XL dataset and further validated on hospital-acquired ECG-Matrix dataset, the complete system runs in <30 s per ECG on CPU-only resources, achieving 95.51% accuracy (F1 = 0.9519) for MI detection on PTB-XL and 88.89% accuracy (F1 = 0.8862) for OMI detection on ECG-Matrix. This work showcases that legacy paper records can be reliably democratized in any part of the world, providing a scalable decision support when digital ECG export, connectivity, or high-end compute are unavailable. Github: https://github.com/nshreyasvi/ECGLight
