AI-assisted Analgesia Copilot System for Proper Management of Nociception

What this Trial Is About

Researchers are evaluating a new AI-assisted co-pilot system called SEASCAPE to improve pain management during general anesthesia. This system uses machine learning to monitor and control nociception—the nervous system's response to pain—by analyzing real-time physiological data from multiple monitors. The goal is to optimize the dosing of intravenous opioids like remifentanil to reduce both over- and under-dosing, potentially improving patient outcomes and reducing opioid-related complications during surgery. The study involves patients scheduled for elective surgeries requiring general anesthesia, where standard analgesia is provided by continuous remifentanil infusion controlled by a pharmacokinetic/pharmacodynamic model. SEASCAPE integrates this existing model with real-time data from monitors tracking hemodynamics, EEG, neuromuscular relaxation, and analgesia indices to provide personalized dosing recommendations. The project is conducted in three phases: initial data collection from 30 patients, expanded data collection from 100 patients under different anesthetic techniques, and usability assessment involving 20 anesthesiologists using the system in pilot mode. Participants will undergo general anesthesia with standard care while data is collected from multiple devices connected via a digital platform. The system will classify nociception levels and suggest adjustments to opioid dosing without altering usual clinical decisions. Researchers will analyze physiological patterns, anesthetic depth, muscle relaxation, and clinician feedback to assess the system's accuracy and usability. The entire process spans from the start to the end of anesthesia, aiming to refine intraoperative pain control and improve postoperative outcomes.

AI-Assisted Analgesia Copilot System