AI LSTM Forecasting Depth of Anesthesia Trial
Summary
The National Institutes of Health registered observational clinical trial NCT07536230 on ClinicalTrials.gov, investigating the use of Long Short-Term Memory (LSTM) deep learning networks to forecast depth of anesthesia. The study aims to deploy AI frameworks to predict patient-specific physiological states by processing time-series BIS-EEG data, moving beyond traditional PK/PD models like the Eleveld model for Propofol and Remifentanil. This trial involves adult patients undergoing procedures requiring general anesthesia and will evaluate the predictive accuracy of AI-driven forecasting versus standard concentration estimation models.
What changed
NIH registered a new observational clinical trial on ClinicalTrials.gov focused on using artificial intelligence and deep learning to predict depth of anesthesia during surgical procedures. The trial, designated NCT07536230, will study Long Short-Term Memory (LSTM) networks for processing time-series BIS-EEG data to forecast patient-specific responses to anesthetic agents Propofol and Remifentanil, potentially improving upon traditional pharmacokinetic/pharmacodynamic models.
For anesthesiologists, clinical researchers, and healthcare institutions evaluating AI-assisted monitoring technologies, this trial represents an emerging application of machine learning in perioperative care. While it does not create immediate regulatory obligations, it reflects the growing integration of AI in clinical practice that may attract regulatory attention from FDA regarding AI/ML-enabled medical devices.
Archived snapshot
Apr 17, 2026GovPing captured this document from the original source. If the source has since changed or been removed, this is the text as it existed at that time.
Deep Learning Framework for Continuous Depth of Anesthesia Forecasting
Observational NCT07536230 Kind: OBSERVATIONAL Apr 17, 2026
Abstract
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
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