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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Anesthesia; Machine Learning; Anesthesia Awareness; Predictive Model; BIS-EEG; BIS; Artifical Intelligence; Intraoperative

• Study Type: Observational
• Enrollment (Estimated): 115

Contacts and Locations

• Study Contact: Name: Hugo Carvalho, MD, PhD; Phone Number: +32 50 45 24 19; Email: hugo.nogueiracarvalho@azsintjan.be

Study Locations

• Belgium
  • Bruges, Belgium, 8000
    • AZ Sint-Jan AV

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

Patients undergoing general anesthesia under continuous depth of anesthesia monitoring.

Description

Inclusion Criteria:

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

Exclusion Criteria:

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort
Prospective; Prospective Cohort
Restrospective; Retrospective Cohort

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Continuous - Perioperative
Mean Absolute Error (MAE)	Mean Absolute Error (MAE)	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.	Continuous - perioperative

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Root Mean Square Error (RMSE)	Root Mean Square Error (RMSE)	Continuous - perioperative

Collaborators and Investigators

• Sponsor: Universitair Ziekenhuis Brussel
• Collaborators: AZ Sint-Jan AV

Study record dates

Study Major Dates

• Study Start (Estimated): June 1, 2026
• Primary Completion (Estimated): August 1, 2026
• Study Completion (Estimated): September 1, 2026

Study Registration Dates

• First Submitted: April 2, 2026
• First Submitted That Met QC Criteria: April 10, 2026
• First Posted (Actual): April 17, 2026

Study Record Updates

• Last Update Posted (Actual): April 17, 2026
• Last Update Submitted That Met QC Criteria: April 10, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Pathologic Processes; Intraoperative Complications; Pathological Conditions, Signs and Symptoms; Intraoperative Awareness
• Other Study ID Numbers: AIBIS

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: UNDECIDED

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No