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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.06283 |
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| _version_ | 1866915331751018496 |
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| author | Zhou, Juexiao Han, Zhongyi Xin, Mankun He, Xingwei Wang, Guotao Song, Jiaoyan Luo, Gongning He, Wenjia Li, Xintong Chu, Yuetan Chen, Juanwen Wang, Bo Wu, Xia Duan, Wenwen Guo, Zhixia Bai, Liyan Pan, Yilin Bi, Xuefei Liu, Lu Feng, Long He, Xiaonan Gao, Xin |
| author_facet | Zhou, Juexiao Han, Zhongyi Xin, Mankun He, Xingwei Wang, Guotao Song, Jiaoyan Luo, Gongning He, Wenjia Li, Xintong Chu, Yuetan Chen, Juanwen Wang, Bo Wu, Xia Duan, Wenwen Guo, Zhixia Bai, Liyan Pan, Yilin Bi, Xuefei Liu, Lu Feng, Long He, Xiaonan Gao, Xin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_06283 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Facial Foundational Model Advances Early Warning of Coronary Artery Disease from Live Videos with DigitalShadow Zhou, Juexiao Han, Zhongyi Xin, Mankun He, Xingwei Wang, Guotao Song, Jiaoyan Luo, Gongning He, Wenjia Li, Xintong Chu, Yuetan Chen, Juanwen Wang, Bo Wu, Xia Duan, Wenwen Guo, Zhixia Bai, Liyan Pan, Yilin Bi, Xuefei Liu, Lu Feng, Long He, Xiaonan Gao, Xin Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Facial Foundational Model Advances Early Warning of Coronary Artery Disease from Live Videos with DigitalShadow |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.06283 |