Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

Universitair Ziekenhuis Brussel

Status: Not yet enrolling

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

Study type: Observational

Funder types: Other

Identifiers

NCT07536230

AIBIS

Details and patient eligibility

About

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.

Enrollment: 115 estimated patients

Sex: All

Volunteers: Accepts Healthy Volunteers

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.

Trial design

115 participants in 2 patient groups

Prospective

Description:

Prospective Cohort

Restrospective

Description:

Retrospective Cohort