AI-assisted Analgesia Copilot System for Proper Management of Nociception

The assessment of pain during General Anesthesia (GA) constitutes a significant clinical challenge because the direct evaluation of pain is impossible due to the abolition of conscious responses. Consequently, clinicians are required to interpret a large volume of indirect physiological data (heart rate, blood pressure, EEG, skin conductance) to determine the level of nociception-the process of detecting noxious stimuli by the nervous system-with data captured from multiple, often independent, monitors.

This manual method is intrinsically inefficient and susceptible to errors in judgment. The result of this deficiency is suboptimal pain management, where the over- or under-dosing of opioids increases the risk of serious adverse events, such as the chronification of pain. This represents a public health problem that causes disability and a significant deterioration in the quality of life. The opportunity to improve this management lies in the integration of data through advanced technologies. Although pharmacokinetic/pharmacodynamic (PK/PD) models exist to estimate individualized opioid doses, they are insufficient on their own. Their validation has primarily relied on EEG parameters, without considering the total complexity of the physiological variables involved in the nociception. The information gap and the inherent inefficiency of the current manual process open the door for a disruptive solution: the development of a co-pilot system based on Machine Learning (ML)/AI, framed within the line of biotechnology to face global challenges.

This project proposes leveraging the opportunity of ML to develop a co-pilot system capable of intelligently integrating the remifentanil PK/PD model with real-time data from multiple monitors (hemodynamic, EEG, neuromuscular relaxation, and nociception). The solution aims for the individualized optimization of pain management, minimizing the risk of inappropriate dosing. Implementing this technology is crucial, as failure to do so would perpetuate the inefficiency and enormous expenses associated with poor clinical outcomes. Conversely, this system promises to improve postoperative outcomes, such as reduce the incidence of persistent pain, and generate a direct positive impact on healthcare costs and quality of care.

The SEASCAPE clinical co-pilot seeks to integrate real-time data from multiparameter monitors, EEG, TOF (Train-of-Four), ANI (Analgesia Nociception Index), ventilators, and infusion pumps via the Mindray m-Connect platform.

Assist in decision-making by classifying nociception and generating prioritized alerts ("increase," "maintain," or "decrease" remifentanil dose).