Excited to share that our work has been featured in PNAS.

New article published in the Proceedings of the National Academy of Sciences of the United States of America [PNAS].

In this study, we present a machine learning-driven, wave-based approach for the noninvasive reconstruction of central aortic pressure waveforms. Our method learns complex arterial dynamics to generate high-fidelity central pressure waveforms from brachial cuff recordings, enabling more precise pulse waveform analysis, essential for cardiovascular risk assessment and disease monitoring. This work highlights the growing role of machine learning in advancing cardiovascular diagnostics.