Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

The integration of Artificial Intelligence (AI) in anesthesiology offers the potential to shift patient monitoring from reactive to predictive. Deep learning architectures, specifically Long Short-Term Memory (LSTM) networks, excel at processing complex, time-series data to forecast future clinical states. While standard PK/PD models (such as the state of the art Eleveld model for Propofol and Remifentanil) estimate target-site drug concentrations (Ce), they do not account for real-time, patient-specific dynamic responses. This study aims to deploy an AI framework designed to predict future physiological states.

NCT Number	NCT07536230
Lead Sponsor	Universitair Ziekenhuis Brussel Collaborators: AZ Sint-Jan AV
Conditions	BIS, BIS-EEG, Artifical Intelligence, Intraoperative, Machine Learning, Anesthesia, Anesthesia Awareness, Predictive Model
Enrollment	115 participants
Start Date	2026-06-01
Primary Completion	2026-08-01 (estimated)
Study Completion	2026-09-01 (estimated)
Updated on ClinicalTrials.gov	2026-04-17