BISNCT07536230
Deep Learning Framework for Continuous Depth of Anesthesia Forecasting
An observational study of BIS and BIS-EEG, run by Universitair Ziekenhuis Brussel at 1 site in Belgium.
Trial 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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- BIS, BIS-EEG, Artifical Intelligence
- How many people
- 115 participants
- Where
- 1 siteBelgium
- Study dates
- 1 Jun 2026to 1 Sep 2026
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Patients undergoing general anesthesia under continuous depth of anesthesia monitoring.
Who may be able to join
- Patients scheduled for elective surgery requiring general anesthesia.
- Procedures requiring continuous depth of anesthesia monitoring (BIS).
Who cannot join
- Procedures where the primary anesthetic plan does not involve continuous electronic data capture.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Validation of a Deep Learning Framework for Continuous Forecasting of Pharmacodynamic Responses and Physiological Trajectories During General Anesthesia
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
- Medical subject headings
- Intraoperative Awareness
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Prospective | Prospective Cohort | |
| Restrospective | Retrospective Cohort |
What is being measured
The primary outcome is the main question the study is designed to answer.
Calibration error of the predictive uncertainty cone
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.
Measured: Continuous - Perioperative
Mean Absolute Error (MAE)
Mean Absolute Error (MAE)
Measured: Continuous - perioperative
Trend accuracy
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.
Measured: Continuous - perioperative
Secondary outcomes (1)
Root Mean Square Error (RMSE)
Root Mean Square Error (RMSE)
Measured: Continuous - perioperative
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted17 Apr 2026
- Study start1 Jun 2026 (estimated)
- Primary completion1 Aug 2026 (estimated)
- Study completion1 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for BISStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 115 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Universitair Ziekenhuis Brussel
- Sponsor type
- University, hospital or other
- Collaborators
- AZ Sint-Jan AV
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07536230
- Sponsor's protocol ID
- AIBIS
- Status last verified
- Mar 2026
- First submitted
- 2 Apr 2026
- Last updated
- 17 Apr 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 17 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of a Deep Learning Framework for Continuous Forecasting of Pharmacodynamic Responses and Physiological Trajectories During General Anesthesia
- ClinicalTrials.gov ID
- NCT07536230
- Acronym / sponsor ID
- AIBIS
- Conditions
- BIS, BIS-EEG, Artifical Intelligence, Intraoperative, Machine Learning, Anesthesia
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 115 participants (estimated)
- Lead sponsor
- Universitair Ziekenhuis BrusselUniversity, hospital or other
- Collaborators
- AZ Sint-Jan AV
- Study start
- 1 Jun 2026 (estimated)
- Primary completion
- 1 Aug 2026 (estimated)
- Study completion
- 1 Sep 2026 (estimated)
- First posted
- 17 Apr 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Other
- Target follow-up
- 1 Day
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Prospective | – | –Prospective Cohort |
| Restrospective | – | –Retrospective Cohort |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients undergoing general anesthesia under continuous depth of anesthesia monitoring.
Key inclusion criteria (2 listed)
- Patients scheduled for elective surgery requiring general anesthesia.
- Procedures requiring continuous depth of anesthesia monitoring (BIS).
Key exclusion criteria (1 listed)
- Procedures where the primary anesthetic plan does not involve continuous electronic data capture.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
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.
1 secondary outcome
Root Mean Square Error (RMSE)
Time frame: Continuous - perioperative
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 17 Apr 2026.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of BIS and BIS-EEG, run by Universitair Ziekenhuis Brussel at 1 site in Belgium.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 17 Apr 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Belgium. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Hugo Carvalho, MD, PhD (+32 50 45 24 19, hugo.nogueiracarvalho@azsintjan.be), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Calibration error of the predictive uncertainty cone (time frame: Continuous - Perioperative). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 17 Apr 2026.