ECG-LENS：导联感知且融入临床上下文的心电图报告生成与评估
ECG-LENS: Lead-Aware Clinical Context Enriched ECG Report Generation and Evaluation
AI总结：
本文提出ECG-LENS框架，结合多导联信号建模等技术实现心电图报告生成，还提出F1-ECGBERT指标评估报告，实验显示其在PTB-XL和MIMIC-IV-ECG上性能优于现有最优方法。
AI中文摘要：
心电图（ECG）是诊断心血管疾病最广泛使用的无创工具之一，但将多导联心电图记录转换为可靠的临床报告仍具挑战性。自动化心电图报告生成可减少临床医生的解读工作量、提高诊断效率，并扩大服务欠缺社区的心脏评估可及性。与基于图像的报告生成任务不同，心电图解读需要分析细微的时间形态，再用密集的临床术语表达连贯的诊断推理。现有系统主要聚焦于分类，而当前的报告生成方法往往生成的输出难以满足实际临床应用需求。为应对这些挑战，本文提出ECG-LENS，这是一种端到端的心电图报告生成框架，联合整合多导联信号建模、诊断感知表征以及基于临床的文本生成。ECG-LENS结合保留局部波形形态的导联级编码器与捕获导联间依赖关系的全局编码器；为引导报告生成，将信号表征与融入临床信息的文本提示融合，以条件化GPT-2解码器；还引入了一种心电图专用的报告预处理策略，帮助模型聚焦于具有临床意义的发现。此外，由于词汇指标可能低估或高估报告质量，本文提出F1-ECGBERT，这是一种基于BERT的心电图专用指标，用于衡量从生成报告与参考报告中提取的诊断标签间的一致性。在PTB-XL上的域内实验及在MIMIC-IV-ECG上的跨域评估显示，ECG-LENS始终优于现有最优方法，相较于最强基线，在METEOR、ROUGE-L和F1-ECGBERT上分别取得了4.0%、6.3%和11.5%的绝对提升。
英文摘要：
Electrocardiography (ECG) is one of the most widely used non-invasive tools for diagnosing cardiovascular disease, but transforming multi-lead ECG recordings into reliable clinical reports remains challenging. Automating ECG report generation could reduce clinicians' interpretive workload, improve diagnostic efficiency, and expand access to cardiac assessment in underserved communities. Unlike image-based report-generation tasks, ECG interpretation requires the analysis of subtle temporal morphologies, followed by coherent diagnostic reasoning expressed in dense clinical terminology. Existing systems predominantly focus on classification, while current report-generation methods often produce outputs that remain inadequate for practical clinical use. To address these challenges, we propose ECG-LENS, an end-to-end ECG report-generation framework that jointly integrates multi-lead signal modeling, diagnosis-aware representations, and clinically grounded text generation. ECG-LENS combines lead-wise encoders that preserve localized waveform morphology with a global encoder that captures inter-lead dependencies. To guide report generation, we fuse signal representations with clinically enriched textual prompts that condition a GPT-2 decoder. We further introduce an ECG-specific report-preprocessing strategy that helps the model focus on clinically meaningful findings. Finally, because lexical metrics may under- or overestimate report quality, we propose F1-ECGBERT, a BERT-based, ECG-specific metric that measures agreement between diagnostic labels extracted from generated and reference reports. In-domain experiments on PTB-XL and cross-domain evaluation on MIMIC-IV-ECG show that ECG-LENS consistently outperforms state-of-the-art methods, with absolute gains of 4.0%, 6.3%, and 11.5% in METEOR, ROUGE-L, and F1-ECGBERT, respectively, over the strongest baselines.
