Deep Learning Framework for Continuous Depth of Anesthesia Forecasting
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: BIS, BIS-EEG, Artifical Intelligence, Intraoperative. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Belgium
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Validation of a Deep Learning Framework for Continuous Forecasting of Pharmacodynamic Responses and Physiological Trajectories During General Anesthesia
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.
Primary outcome measures
- Calibration error of the predictive uncertainty cone [Time frame: Continuous - Perioperative]
- Mean Absolute Error (MAE) [Time frame: Continuous - perioperative]
- Trend accuracy [Time frame: Continuous - perioperative]
Secondary outcome measures (1)
- Root Mean Square Error (RMSE) [Time frame: Continuous - perioperative]
Eligibility criteria
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.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
Belgium · 1 center
- AZ Sint-Jan AV — Bruges
Identifiers
NCT: NCT07536230 · AIBIS