Artificial Intelligence-derived Photoplethysmography Age as a Digital Biomarker for Cardiovascular Health
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912603848048640 |
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| author | Nie, Guangkun Zhao, Qinghao Tang, Gongzheng Li, Yaxin Hong, Shenda |
| author_facet | Nie, Guangkun Zhao, Qinghao Tang, Gongzheng Li, Yaxin Hong, Shenda |
| contents | Background: Photoplethysmography (PPG), increasingly available through wearable devices, provides a non-invasive means of monitoring human hemodynamics. In this study, we introduce artificial intelligence-derived photoplethysmography (AI-PPG) age, a deep learning-based estimate of biological age from raw PPG signals, and evaluate its potential as a digital biomarker for cardiovascular health.
Methods: We developed a deep learning model with a distribution-aware loss function to reduce bias from imbalanced data. The model was trained and evaluated on the UK Biobank cohort (N = 212,231). We analyzed the association between the AI-PPG age gap (AI-PPG age minus calendar age) and multiple cardiovascular and metabolic outcomes, assessed its longitudinal value using serial PPG measurements, and externally validated its generalizability in an independent MIMIC-III-derived cohort (N = 2,343).
Results: After adjusting for key confounders, participants with an AI-PPG age gap greater than 9 years have a significantly higher risk of major adverse cardiovascular and cerebrovascular events (hazard ratio of 2.37, p = 8.46x10$^{-80}$), as well as seven secondary outcomes including coronary heart disease and myocardial infarction (all p < 0.005). Conversely, those with a gap below -9 years show a lower risk profile. Longitudinal analysis demonstrates that changes in AI-PPG age add predictive value over time. In the external validation cohort, each one-year increase in AI-PPG age gap is associated with higher in-hospital mortality (odds ratio of 1.02, p = 0.01).
Conclusions: AI-PPG age is a scalable, non-invasive biomarker for cardiovascular health assessment. Integrated with wearable devices, it may enable population-level screening, personalized monitoring, and early intervention. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_12990 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Artificial Intelligence-derived Photoplethysmography Age as a Digital Biomarker for Cardiovascular Health Nie, Guangkun Zhao, Qinghao Tang, Gongzheng Li, Yaxin Hong, Shenda Signal Processing Background: Photoplethysmography (PPG), increasingly available through wearable devices, provides a non-invasive means of monitoring human hemodynamics. In this study, we introduce artificial intelligence-derived photoplethysmography (AI-PPG) age, a deep learning-based estimate of biological age from raw PPG signals, and evaluate its potential as a digital biomarker for cardiovascular health. Methods: We developed a deep learning model with a distribution-aware loss function to reduce bias from imbalanced data. The model was trained and evaluated on the UK Biobank cohort (N = 212,231). We analyzed the association between the AI-PPG age gap (AI-PPG age minus calendar age) and multiple cardiovascular and metabolic outcomes, assessed its longitudinal value using serial PPG measurements, and externally validated its generalizability in an independent MIMIC-III-derived cohort (N = 2,343). Results: After adjusting for key confounders, participants with an AI-PPG age gap greater than 9 years have a significantly higher risk of major adverse cardiovascular and cerebrovascular events (hazard ratio of 2.37, p = 8.46x10$^{-80}$), as well as seven secondary outcomes including coronary heart disease and myocardial infarction (all p < 0.005). Conversely, those with a gap below -9 years show a lower risk profile. Longitudinal analysis demonstrates that changes in AI-PPG age add predictive value over time. In the external validation cohort, each one-year increase in AI-PPG age gap is associated with higher in-hospital mortality (odds ratio of 1.02, p = 0.01). Conclusions: AI-PPG age is a scalable, non-invasive biomarker for cardiovascular health assessment. Integrated with wearable devices, it may enable population-level screening, personalized monitoring, and early intervention. |
| title | Artificial Intelligence-derived Photoplethysmography Age as a Digital Biomarker for Cardiovascular Health |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2502.12990 |