将纸质心电图数字化，让旧数据可用
Digitizing Paper ECGs at Scale: An Open-Source Algorithm for Clinical Research
- 全自动模块化算法，可处理扫描或照片心电图
- 在3.7万张图片上实现19.65 dB信噪比，优于现有方法
- 开源工具助力科研复现，推动心电数据民主化
数以百万计的临床心电图仅以纸质扫描形式存在，无法用于现代自动化诊断。我们提出一种全自动化、模块化的框架，可将扫描或拍摄的心电图图像转化为数字信号，适用于临床与研究场景。该框架在37,191张心电图图像上进行验证，其中1,596张来自阿克舒斯大学医院，对带有常见伪影的扫描纸张实现平均信噪比19.65 dB。进一步在埃默里大学纸质数字化心电图数据集（35,595张图像）上评估，涵盖透视畸变、褶皱和污渍等复杂情况，模型在所有子类别中均超越现有最佳水平。完整软件已开源，促进可复现性与持续开发。我们期望该工具能激活回顾性心电图档案，推动人工智能诊断的普及。
原文摘要 · Abstract (English)
Millions of clinical ECGs exist only as paper scans, making them unusable for modern automated diagnostics. We introduce a fully automated, modular framework that converts scanned or photographed ECGs into digital signals, suitable for both clinical and research applications. The framework is validated on 37,191 ECG images with 1,596 collected at Akershus University Hospital, where the algorithm obtains a mean signal-to-noise ratio of 19.65 dB on scanned papers with common artifacts. It is further evaluated on the Emory Paper Digitization ECG Dataset, comprising 35,595 images, including images with perspective distortion, wrinkles, and stains. The model improves on the state-of-the-art in all subcategories. The full software is released as open-source, promoting reproducibility and further development. We hope the software will contribute to unlocking retrospective ECG archives and democratize access to AI-driven diagnostics.
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