A physics-informed peripheral hemodynamic digital twin for personalized estimation of arterial stiffness
Abstract
Arterial stiffness, commonly assessed by pulse wave velocity (PWV), is an important biomarker of cardiovascular risk, yet conventional PWV measurements require dual-site recordings that limit scalability in clinical settings. Here we introduce a physics-informed peripheral hemodynamic digital twin framework for estimating central-brachial arterial stiffness from a single brachial pressure waveform. Using our laboratory-developed cuff device, high-resolution brachial pulse waveforms were collected in a cohort of 118 subjects. The digital twin then personalizes a reduced-order wave propagation model through PDE-constrained optimization, generating a compact parameter space that encodes ventricular–vascular characteristics relevant to stiffness. Incorporating digital-twin-derived parameters improved PWV estimation on a blind test cohort ( r = 0.67, RMSE = 1.12 m/s) compared with a baseline model using only age and blood pressure ( r = 0.22, RMSE = 1.90 m/s). These results indicate that physics-informed digital twins can enable scalable, noninvasive assessment of vascular aging from accessible peripheral waveforms.
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BibTeX
@article{aghilinejad2026physicsinformed,
title = {{A physics-informed peripheral hemodynamic digital twin for personalized estimation of arterial stiffness}},
author = {Arian Aghilinejad and Alessio Tamborini and Faisal Amlani and Morteza Gharib},
journal = {npj Digital Medicine},
year = {2026},
month = oct,
doi = {10.1038/s41746-026-03431-6},
url = {https://hal.science/hal-05847017v1},
note = {CC BY},
}
RIS
TY - JOUR
TI - A physics-informed peripheral hemodynamic digital twin for personalized estimation of arterial stiffness
AU - Arian Aghilinejad
AU - Alessio Tamborini
AU - Faisal Amlani
AU - Morteza Gharib
PY - 2026
DA - 2026/10/28
JO - npj Digital Medicine
DO - 10.1038/s41746-026-03431-6
UR - https://hal.science/hal-05847017v1
AB - Arterial stiffness, commonly assessed by pulse wave velocity (PWV), is an important biomarker of cardiovascular risk, yet conventional PWV measurements require dual-site recordings that limit scalability in clinical settings. Here we introduce a physics-informed peripheral hemodynamic digital twin framework for estimating central-brachial arterial stiffness from a single brachial pressure waveform. Using our laboratory-developed cuff device, high-resolution brachial pulse waveforms were collected in a cohort of 118 subjects. The digital twin then personalizes a reduced-order wave propagation model through PDE-constrained optimization, generating a compact parameter space that encodes ventricular–vascular characteristics relevant to stiffness. Incorporating digital-twin-derived parameters improved PWV estimation on a blind test cohort ( r = 0.67, RMSE = 1.12 m/s) compared with a baseline model using only age and blood pressure ( r = 0.22, RMSE = 1.90 m/s). These results indicate that physics-informed digital twins can enable scalable, noninvasive assessment of vascular aging from accessible peripheral waveforms.
SN - 2398-6352
ER -