# Artificial Intelligence-derived Photoplethysmography Age as a Digital Biomarker for Cardiovascular Health

arXiv: https://arxiv.org/abs/2502.12990 · HTML: https://mlsanity.com/paper/2502.12990/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [Benchmarking Open-Source PPG Foundation Models for Biological Age Prediction](https://mlsanity.com/paper/2603.14030/) (2026)
- [Artificial intelligence-enabled single-lead ECG for non-invasive hyperkalemia detection: development, multicenter validation, and proof-of-concept deployment](https://mlsanity.com/paper/2603.14177/) (2026)
- [Brain Vascular Age Prediction Using Cerebral Blood Flow Velocity and Machine Learning Algorithms](https://mlsanity.com/paper/2605.16969/) (2026)
- [BCG-FM: A Foundation Model for Ambient Cardiac Health Sensing](https://mlsanity.com/paper/2606.07692/) (2026)

## Abstract (arXiv)

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.