Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

Starting soon

Conditions studied: BIS, BIS-EEG, Artifical Intelligence, Intraoperative, Machine Learning, Anesthesia, Anesthesia Awareness, Predictive Model

In brief

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.

Key facts

Study ID

NCT07536230

Run by

Universitair Ziekenhuis Brussel

People needed

115

Starts

2026-06-01

Expected to finish

2026-09-01

Last updated by the study team

2026-04-17

Who can join

Age: any. Sex: any. Healthy volunteers: accepted.

You may qualify if…

• Patients scheduled for elective surgery requiring general anesthesia.
• Procedures requiring continuous depth of anesthesia monitoring (BIS).

You may not qualify if…

• Procedures where the primary anesthetic plan does not involve continuous electronic data capture.

Where it is running

• AZ Sint-Jan AV — Bruges, Belgium

Full record on ClinicalTrials.gov

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