Feasibility of intelligent drug control in the maintenance phase of general anesthesia based on convolutional neural network

Background: The growth and aging process of the human population has accelerated the increase in surgical procedures. Yet, the demand for increasing operations can be hardly met since the training of anesthesiologists is usually a long-term process. Closed-loop artificial intelligence (AI) model provides the possibility to solve intelligent decision-making for anesthesia auxiliary control and, as such, has allowed breakthroughs in closed-loop control of clinical practices in intensive care units (ICUs). However, applying an open-loop artificial intelligence algorithm to build up personalized medication for anesthesia still needs to be further explored. Currently, anesthesiologists have selected doses of intravenously pumped anesthetic drugs mainly based on the blood pressure and bispectral index (BIS), which can express the depth of anesthesia. Unfortunately, BIS cannot be monitored at some medical centers or operational procedures and only be regulated by blood pressure. As a result, here we aim to inaugurally explore the feasibility of a basic intelligent control system applied to drug delivery in the maintenance phase of general anesthesia, based on a convolutional neural network model with open-loop design, according to AI learning of existing anesthesia protocols. Methods: A convolutional neural network, combined with both sliding window sampling method and residual learning module, was utilized to establish an ''AI anesthesiologist'' model for intraoperative dosing of personalized anesthetic drugs (propofol and remifentanil). The fitting degree and difference in pumping dose decision, between the AI anesthesiologist and the clinical anesthesiologist, for these personalized anesthetic drugs were examined during the maintenance phase of anesthesia. Results: The medication level established by the “AI anesthesiologist” was comparable to that obtained by the clinical anesthesiologist during the maintenance phase of anesthesia. Conclusion: The application of an open-loop decision-making plan by convolutional neural network showed that intelligent anesthesia control is consistent with the actual anesthesia control, thus providing possibility for further evolution and optimization of auxiliary intelligent control of depth of anesthesia.