Key result
A machine learning algorithm trained on a synthetic pulse wave population predicted cardio-ankle vascular index (CAVI) values for real individuals with an average error of 6.5%.
A synthetic pulse wave database generated via hemodynamic modeling can successfully train machine learning algorithms to predict vascular aging markers like CAVI in real patients with reasonable accuracy.
Synthetic pulse wave databases may expand limited real data for CAVI ML prediction; leaves open prospective human validation before clinical use.
This paper presents a methodology to generate synthetic pulse wave database. Each virtual subject is generated with the help of one-dimensional hemodynamics model of systemic circulation with lumped model of the left heart. This paper describes and compares two parameter optimization methods: unscented Kalman filter and Bayesian optimization. As a case study, an experiment is conducted to predict cardio-ankle vascular index (CAVI) values for real individuals with a machine learning algorithm trained on a synthetic population. The average error of 6.5% is achieved
No takes yet. Share an insight, caveat, or question.
Rogov et al. (2024) studied Vascular ageing. Machine learning algorithm trained on a synthetic population was evaluated on Prediction of cardio-ankle vascular index (CAVI) values. A machine learning algorithm trained on a synthetic pulse wave population predicted cardio-ankle vascular index (CAVI) values for real individuals with an average error of 6.5%.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: