Postoperative cognitive dysfunction (POCD) and delirium (POD) are frequent complications after major surgeries, particularly in cardiac procedures, and can significantly impact recovery and quality of life. These cognitive impairments are often tied to disruptions in cerebrovascular autoregulation (CA), the brain’s ability to maintain stable blood flow despite changes in blood pressure. Traditional monitoring methods like Doppler ultrasonography, though effective, are complex and challenging to implement in fast-paced clinical settings. Archimedes, an innovative non-invasive device, addresses this by providing continuous, real-time monitoring of intracranial pressure (ICP) waves. This study explores the potential of Archimedes, enhanced by AI and machine learning, to predict POCD and POD by analyzing subtle changes in ICP waveforms. By identifying early warning signs, it aims to improve patient outcomes and reduce the risk of long-term cognitive decline after surgery.
Project funding:
Research Council of Lithuania, Information technologies for the development of science and knowledge society
Period of project implementation: 2024-10-01 - 2026-09-30
Project coordinator: Kaunas University of Technology
