Closed-Loop Control of Anesthesia: Survey on Actual Trends, Challenges and Perspectives

Abstract

Automation empowers self-sustainable adaptive processes and personalized services in many industries. The implementation of the integrated healthcare paradigm built on Health 4.0 is expected to transform any area in medicine due to the lightning-speed advances in control, robotics, artificial intelligence, sensors etc. The two objectives of this article, as addressed to different entities, are: i) to raise awareness throughout the anesthesiologists about the usefulness of integrating automation and data exchange in their clinical practice for providing increased attention to alarming situations, ii) to provide the actualized insights of drug-delivery research in order to create an opening horizon towards precision medicine with significantly improved human outcomes. This article presents a concise overview on the recent evolution of closed-loop anesthesia delivery control systems by means of control strategies, depth of anesthesia monitors, patient modelling, safety systems, and validation in clinical trials. For decades, anesthesia control has been in the midst of transformative changes, going from simple controllers to integrative strategies of two or more components, but not achieving yet the breakthrough of an integrated system. However, the scientific advances that happen at high speed need a modern review to identify the current technological gaps, societal implications, and implementation barriers. This article provides a good basis for control research in clinical anesthesia to endorse new challenges for intelligent systems towards individualized patient care. At this connection point of clinical and engineering frameworks through (semi-) automation, the following can be granted: patient safety, economical efficiency, and clinicians' efficacy.INDEX TERMS Closed-loop control, drug-delivery control, anesthesia, (semi-)automated anesthesia delivery, cyber physical medical systems, decision support systems, adaptive control.

AnesthesiaGUIDE: a MATLAB tool to control the anesthesia

“…Some relevant examples may be mentioned here: Ilyas proposed a sliding-mode control algorithm [6] [7], Khaqan developed a nonlinear strategy [8], Navarro designed a fractional-order closed-loop model reference adaptive control based on a fractional-order model [9], Mendez used fuzzy predictive control techniques [10], Caiado investigated H-inf robust control [11], Reznanian developed a predictive controller [12], Usharani developed a PID controller strategy [16], Pasin performed a metaanalysis comparing closed-loop with manual drug delivery [17], Sopasakis built a fractional pharmacokinetics model https://doi.org/10.1007/s42452-021-04885-x [18], and Gonzalez-Cava proposed a study where an adaptive model deal simultaneously with the variabilities in the clinical response of the patients [19]. Actual trends and challenges are presented by Ghita in [20] and Zaouter in [21]. All the approaches guarantee a steady and safe hypnosis level.…”

Identifiability of pharmacological models for online individualization

“…This setting is well-studied in the literature and has been the subject of a handful of clinical studies. Without attempting a comprehensive survey, we suggest the survey Ghita et al (2000) as a starting point for the interested reader.…”

GALENO: Computer aided system for modeling, monitoring, and control in anesthesia

“…In the survey 16 a concise overview on the recent evolution of closed‐loop anesthesia is presented, several platforms for real‐time control of anesthesia (McSleepy, iControl, Infusion Toolbox 95, RUGLOOP II, CONCERT‐CL, CLADS) are briefly described. The majority of them use proportional‐integral‐derivative (PID) controllers or target controlled infusion (TCI).…”

“…However, it is important to develop control strategies that are robust 14 . For a review on the literature, other works 14,16‐18 should be considered.…”