Optimization of Propofol Dose Estimated During Anesthesia Through Artificial Intelligence by Genetic Algorithm: Design and Clinical Assessment

This paper addresses the application of an adaptive neuro-fuzzy inference system (ANFIS) to assign the optimal dose of propofol as a vital anesthetic drug considering patient needs. The purpose of this research was to explore the factors that influence the propofol dosage needed to sedate patients. This paper estimates the drug dose to regulate the depth of anesthesia by administrating propofol. In this regard, two artificial intelligence approaches; a feedforward neural network and ANFIS are applied to predict the propofol dose. Introducing an estimator to control automatically might provide remarkable advantages for the patient in reducing the risk for under- and over-dosing. The suggested estimations are compared with results extracted from the classical model revised method and then evaluated patients undergoing surgery in a Mashhad’s hospital to identify a research innovation. The propofol doses are optimized using a genetic algorithm. Sensitivity analysis methods are used to test the estimator using a collection of patient models consisting of some populations. Finally, during anesthesia, an optimal dose estimator allows for a rapid period of induction with reasonable overshoot and adequate disturbance rejection results. The novelty of this study is in estimating without using Bi-spectral Index signal and also there is a significant reduction in anesthesia costs by optimizing the drug dose. The results of the optimization model show a 14.06% saving of propofol dose with MSE 5.3 × 10⁻⁶.