Automation of anaesthesia: a review on multivariable control

Chang, Jing; Syafiie, S.; Kamil, Raja; Lim, Thien Thien

doi:10.1007/s10877-014-9590-6

Cited by 20 publications

(10 citation statements)

References 102 publications

(99 reference statements)

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“…More concretely, the administration of the hypnotic is made automatically, but the administration of the analgesic is manually made by a clinician. A detailed introduction to anesthesia as a control problem together with a good overview of the state of the art can be found in Lemos et al [15] and Chang et al [16].…”

Section: Introductionmentioning

confidence: 99%

Positive state observer for the automatic control of the depth of anesthesia—Clinical results

Nogueira¹,

Mendonça²,

Rocha³

2019

Computer Methods and Programs in Biomedicine

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In this paper, a positive state observer is designed for the implementation of a control law proposed for the automatic administration of propofol and of remifentanil in order to track a desired level for the bispectral index (BIS). The BIS is used as a measure of the depth of anesthesia. It is proved and illustrated by simulations that the controller-observer scheme has a very good performance. This control scheme was implemented, tested and evaluated in real patients during surgical procedures. A set of clinical results are here presented.

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Section: Introductionmentioning

confidence: 99%

Positive state observer for the automatic control of the depth of anesthesia—Clinical results

Nogueira¹,

Mendonça²,

Rocha³

2019

Computer Methods and Programs in Biomedicine

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“…1) SISO Model: The pharmacokinetic/pharmacodynamic (PK/PD) model is a compartmental model that illustrates both the drug distribution in the human body and its effect on certain physiological systems. For anesthesia, the pharmacokinetics are represented by a linear compartmental model that defines the concentration of the administered drug in different parts of human body, while the pharmacodynamics are represented by a static nonlinear function (i.e., Hill curve) relating the resulting drug concentration to the measured effect (e.g., BIS index) [174], [190].…”

Section: B Modelingmentioning

confidence: 99%

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

et al. 2020

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Over the past decade, there has been an unprecedented international focus on improved quality and availability of medical care, which has reignited interest in clinical automation and drawn researchers toward novel solutions in the field of physiological closed-loop control systems (PCLCs). Today, multidisciplinary groups of expert scientists, engineers, clinicians, mathematicians, and policy-makers are combining their knowledge and experience to develop both the next generation of PCLC-based medical equipment and a collaborative commercial/academic infrastructure to support this rapidly expanding frontier. In the following article, we provide a robust introduction to the various aspects of this growing field motivated by the recent and ongoing work supporting two leading technologies: the artificial pancreas (AP) and automated anesthesia. Following a brief high-level overview of the main concepts in automated therapy and some relevant tools from systems and control theory, we explore -separately -the developments, challenges, state-ofthe-art, and probable directions for AP and automated anesthesia systems. We then close the review with a consideration of the common lessons gleaned from these ventures and the implications they present for future investigations and adjacent research.

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“…Concurrently, other research groups developed closed-loop systems for propofol using a variety of mathematical algorithms for robotic drug delivery. 21 In 2001, Struys et al 22 demonstrated that induction and maintenance of hypnosis using a patient individualized model-based adaptive closedloop control system for propofol is feasible. In their randomized trial, 10 patients received automated administration of propofol setting the target BIS value at 50.…”

Section: Anesthesia and Analgesiamentioning

confidence: 99%

Autonomous Systems in Anesthesia: Where Do We Stand in 2020? A Narrative Review

Zaouter

Joosten

Rinehart

et al. 2020

Anesthesia &Amp; Analgesia

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BIS = bispectral index; CDS = clinical decision support system; EEG = electroencephalogram; HSS = hybrid sedation system; MIMO = multiple input multiple output; SISO = single input single output; TCI = target-controlled infusion BACKGROUND: THE RATIONALE FOR ROBOTS IN ANESTHESIA Robots now surround our daily activities, doing everything from cleaning our homes to flying airplanes, and they exist in fields ranging from industry to medicine.In science fiction, we tend to conceptualize "robots" as human-shaped automata, but in general, a "robot" generally refers to any mechanical system capable of interacting with the environment with directed interventions.In medicine, automation offers precision therapy in combination with a high level of reproducibility. These features make robots appealing in the medical fields, and they have been used in the field of anesthesia for several decades assisting clinicians. 1,2 However, many well-tested autonomous systems and known to be safe have not been adopted yet into clinical practice on a daily basis. When we consider that repetitive execution of trivial technical tasks is subject to vigilance decrement in humans, as well as the fact that human providers may suffer from fatigue, boredom, and bias, there is a strong rationale for the role such systems may play in clinical care. 3,4 Using robots to assist in the acquisition of patient data, simple decision-making, and manual tasks may leave physicians freer to focus efficiently on tasks requiring human intelligence and judgment. 5 As most of us are aware, almost every facet of our society is becoming, for better or worse, progressively more technology-dependent. Technological advancement has made autonomous systems, also known as robots, an integral part of our life in several fields, including medicine. The application of robots in anesthesia could be classified into 3 types of robots. The first ones are pharmacological robots. These robots are based on closed-loop systems that allow betterindividualized anesthetic drug titration for optimal homeostasis during general anesthesia and sedation. Recent evidence also demonstrates that autonomous systems could control hemodynamic parameters proficiently outperforming manual control in the operating room. The second type of robot is mechanical. They enable automated motorized reproduction of tasks requiring high manual dexterity level. Such robots have been advocated to be more accurate than humans and, thus, could be safer for the patient. The third type is a cognitive robot also known as decision support system. This type of robot is able to recognize crucial clinical situation that requires human intervention. When these events occur, the system notifies the attending clinician, describes relevant related clinical observations, proposes pertinent therapeutic options and, when allowed by the attending clinician, may even administer treatment. It seems that cognitive robots could increase patients' safety. Robots in anesthesia offer not only the possibility to free the attending cl...

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Automation of anaesthesia: a review on multivariable control

Cited by 20 publications

References 102 publications

Positive state observer for the automatic control of the depth of anesthesia—Clinical results

Positive state observer for the automatic control of the depth of anesthesia—Clinical results

Physiological Closed-Loop Control (PCLC) Systems: Review of a Modern Frontier in Automation

Autonomous Systems in Anesthesia: Where Do We Stand in 2020? A Narrative Review

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