Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

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.

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Key information

Conditions

BIS Anesthesia Anesthesia Awareness Artifical Intelligence BIS-EEG Intraoperative Intraoperative Awareness Intraoperative Complications Machine Learning Pathologic Processes Pathological Conditions, Signs and Symptoms Predictive Model

Sex eligibility

All sexes

Study type

Observational

Primary location

AZ Sint-Jan AV

Bruges, 8000, Belgium

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

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.

Treatment and study plan

Primary outcomes

Calibration error of the predictive uncertainty cone

Time frame: Continuous - Perioperative

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.
Mean Absolute Error (MAE)

Time frame: Continuous - perioperative

Mean Absolute Error (MAE)
Trend accuracy

Time frame: Continuous - perioperative

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.

Secondary outcomes

Root Mean Square Error (RMSE)

Time frame: Continuous - perioperative

Root Mean Square Error (RMSE)

Study contacts

Contact information is provided by the study sponsor or research team.

Hugo Carvalho, MD, PhD

CONTACT

[email protected]

+32 50 45 24 19

Sponsors and collaborators

Lead sponsor

Universitair Ziekenhuis Brussel

Other

Collaborators

AZ Sint-Jan AV

Registry information

Official study title

Validation of a Deep Learning Framework for Continuous Forecasting of Pharmacodynamic Responses and Physiological Trajectories During General Anesthesia

Important dates

Study start: 2026
Primary completion: 2026
Study completion: 2026
First posted: Apr 17, 2026
Registry last updated: Apr 17, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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