Trials / Not Yet Recruiting
Not Yet RecruitingNCT07536230
Deep Learning Framework for Continuous Depth of Anesthesia Forecasting
Validation of a Deep Learning Framework for Continuous Forecasting of Pharmacodynamic Responses and Physiological Trajectories During General Anesthesia
- Status
- Not Yet Recruiting
- Phase
- —
- Study type
- Observational
- Enrollment
- 115 (estimated)
- Sponsor
- Universitair Ziekenhuis Brussel · Academic / Other
- Sex
- All
- Age
- —
- Healthy volunteers
- Accepted
Summary
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.
Conditions
- BIS
- BIS-EEG
- Artifical Intelligence
- Intraoperative
- Machine Learning
- Anesthesia
- Anesthesia Awareness
- Predictive Model
Timeline
- Start date
- 2026-06-01
- Primary completion
- 2026-08-01
- Completion
- 2026-09-01
- First posted
- 2026-04-17
- Last updated
- 2026-04-17
Locations
1 site across 1 country: Belgium
Source: ClinicalTrials.gov record NCT07536230. Inclusion in this directory is not an endorsement.