Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

N/ANot Yet RecruitingNCT07536230

Universitair Ziekenhuis Brussel115 enrolled

Overview

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 Type

OBSERVATIONAL

Enrollment

115

Conditions

BIS BIS-EEG Artifical Intelligence Intraoperative Machine Learning Anesthesia Anesthesia Awareness Predictive Model

Eligibility

Sex: ALLHealthy volunteers:

Medical Language ↔ Plain English

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.

Outcomes

Primary Outcomes

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)

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

Secondary Outcomes

Root Mean Square Error (RMSE)

Time frame: Continuous - perioperative

Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

Overview

Conditions

Eligibility

Locations (1)

Outcomes

Primary Outcomes

Secondary Outcomes

Central Contacts