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

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07536230

Enrollment

115

Registered

2026-04-17

Start date

2026-06-01

Completion date

2026-09-01

Last updated

2026-04-17

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

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

Brief 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.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

OTHER

Eligibility

Sex/Gender

ALL

Healthy volunteers

Yes

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.

Design outcomes

Primary

Measure	Time frame	Description
Calibration error of the predictive uncertainty cone	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)	Continuous - perioperative	Mean Absolute Error (MAE)
Trend accuracy	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

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

Countries

Belgium

Contacts

CONTACTHugo Carvalho, MD, PhD

hugo.nogueiracarvalho@azsintjan.be+32 50 45 24 19

Outcome results