用人脸影像实时预警冠心病，无感检测更安全
Facial Foundational Model Advances Early Warning of Coronary Artery Disease from Live Videos with DigitalShadow
- 基于2100万张人脸预训练，微调出可识冠心病风险的模型
- 在7004张人脸数据上验证，实现非接触式早期预警
- 支持本地部署，保护隐私，适合慢病筛查场景
全球人口老龄化加剧医疗系统压力，冠状动脉疾病（CAD）每年导致约1780万例死亡，是主要死因之一。由于CAD具有高度可预防性，早期发现与主动管理至关重要。本文提出DigitalShadow系统，基于微调的人脸基础模型构建，该模型在2100万张人脸图像上预训练，并在来自中国四家医院、涵盖1751名受试者的7004张人脸图像上进一步微调为LiveCAD模型，用于评估冠心病风险。系统可被动、无接触地从实时视频流中提取面部特征，无需用户主动配合。结合个人数据库，生成自然语言风险报告与个性化健康建议。以隐私为核心设计原则，支持本地化部署，确保用户数据安全。
原文摘要 · Abstract (English)
Global population aging presents increasing challenges to healthcare systems, with coronary artery disease (CAD) responsible for approximately 17.8 million deaths annually, making it a leading cause of global mortality. As CAD is largely preventable, early detection and proactive management are essential. In this work, we introduce DigitalShadow, an advanced early warning system for CAD, powered by a fine-tuned facial foundation model. The system is pre-trained on 21 million facial images and subsequently fine-tuned into LiveCAD, a specialized CAD risk assessment model trained on 7,004 facial images from 1,751 subjects across four hospitals in China. DigitalShadow functions passively and contactlessly, extracting facial features from live video streams without requiring active user engagement. Integrated with a personalized database, it generates natural language risk reports and individualized health recommendations. With privacy as a core design principle, DigitalShadow supports local deployment to ensure secure handling of user data.
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