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Deep Learning Framework for Continuous Depth of Anesthesia Forecasting

NCT07536230 · Status: NOT_YET_RECRUITING · Type: OBSERVATIONAL · Enrollment: 115

Last updated 2026-04-17

No results posted yet for this study

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.

Conditions

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

Sponsors & Collaborators

  • AZ Sint-Jan AV

    collaborator OTHER

  • Universitair Ziekenhuis Brussel

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2026-06-01
Primary Completion
2026-08-01
Completion
2026-09-01

Countries

  • Belgium

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT07536230 on ClinicalTrials.gov