Abstract
Background: Artificial intelligence enabled electrocardiography (AI-ECG) has demonstrated the ability to detect diverse pathologies, but most existing models focus on single disease identification, neglecting comorbidities and future risk prediction. Although ECGFounder expanded cardiac disease coverage, a holistic health profiling model remains needed. Methods: We constructed a large multicenter dataset comprising 13.3 million ECGs from 2.98 million patients. Using transfer learning, ECGFounder was fine-tuned to develop AnyECG, a foundation model for holistic health profiling. Performance was evaluated using external validation cohorts and a 10-year longitudinal cohort for current diagnosis, future risk prediction, and comorbidity identification. Results: AnyECG demonstrated systemic predictive capability across 1172 conditions, achieving an AUROC greater than 0.7 for 306 diseases. The model revealed novel disease associations, robust comorbidity patterns, and future disease risks. Representative examples included high diagnostic performance for hyperparathyroidism (AUROC 0.941), type 2 diabetes (0.803), Crohn disease (0.817), lymphoid leukemia (0.856), and chronic obstructive pulmonary disease (0.773). Conclusion: The AnyECG foundation model provides substantial evidence that AI-ECG can serve as a systemic tool for concurrent disease detection and long-term risk prediction.
Abstract (translated)
背景：人工智能赋能的心电图（AI-ECG）展示了检测多种疾病的能力，但大多数现有的模型主要集中在单一疾病的识别上，忽略了共病和未来风险预测。尽管ECGFounder扩大了心脏疾病的覆盖范围，但仍需构建一个能够全面健康评估的基础模型。方法：我们构建了一个大型多中心数据集，包含298万名患者共计1330万份心电图记录。通过迁移学习，对ECGFounder进行微调以开发AnyECG，这是一种用于全面健康评估的基础模型。使用外部验证队列和一个长达十年的纵向队列来评估当前诊断、未来风险预测以及共病识别方面的性能。结果：AnyECG在1172种条件下的系统性预测能力表现出色，在306种疾病中达到了AUROC大于0.7的成绩。该模型揭示了新的疾病关联，稳健的共病模式和未来的疾病风险。代表性的例子包括高甲状旁腺功能亢进症（AUROC 0.941）、2型糖尿病（0.803）、克罗恩病（0.817）、淋巴性白血病（0.856）以及慢性阻塞性肺病（0.773）。结论：AnyECG基础模型提供了有力的证据，证明AI-ECG可以作为一个系统工具用于同时检测当前疾病和长期风险预测。
URL
https://arxiv.org/abs/2601.10748
