An observational study in BIS, BIS-EEG and Artifical Intelligence, sponsored by Universitair Ziekenhuis Brussel. Not yet recruiting at 1 site in Belgium. Per ClinicalTrials.gov, last updated 2026-04-17.
Sponsored by Universitair Ziekenhuis Brussel · Observational
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.
51 studies on the registry are indexed under Intraoperative Awareness; 7 are open to participants now.
This study's planned enrollment of 115 is close to the median of 108 across 26 observational studies indexed under Intraoperative Awareness.
Browse Intraoperative Awareness studies →Universitair Ziekenhuis Brussel is the lead sponsor of 362 studies on the registry; 103 are open to participants now.
Counted across the registry records on this site, refreshed daily.
Patients undergoing general anesthesia under continuous depth of anesthesia monitoring.
Exclusion Criteria:
- Procedures where the primary anesthetic plan does not involve continuous electronic data capture.
Prospective Cohort
Retrospective Cohort
Calibration error of the predictive uncertainty cone
Calibration error of the predictive uncertainty cone - Calibration error of the predictive uncertainty cone is the discrepancy between a model's stated confidence level (e.g., predicting that 95% of future values will fall within a specific range) and the actual frequency with which the true values actually land inside that predicted boundary.
Time frame: Continuous - Perioperative
Mean Absolute Error (MAE)
Mean Absolute Error (MAE)
Time frame: Continuous - perioperative
Trend accuracy
Trend accuracy measures a predictive model's ability to correctly forecast the future direction and rate of change of a variable (such as whether a patient's anesthesia depth is actively lightening or deepening), independent of the absolute numerical error at any single point in time.
Time frame: Continuous - perioperative
Root Mean Square Error (RMSE)
Root Mean Square Error (RMSE)
Time frame: Continuous - perioperative
Plan to share: Undecided
No publications or documents are linked to this record.
This study is not yet recruiting, as verified in Mar 2026. You cannot join it, but the record below documents what was studied.
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Universitair Ziekenhuis Brussel