Year : 2022 | Volume
| Issue : 1 | Page : 86-93
Artificial intelligence and anesthesia: A narrative review
Madhavi Singh1, Gita Nath2
1 Department of Anesthesia, Apollo Institute of Medical Sciences and Research (AIMSR), Hyderabad, Telangana, India
2 Department of Anesthesia, Axon Anesthesia Associates, Hyderabad, Telangana, India
Associate Professor of Anesthesia, Apollo Institute of Medical Sciences and Research (AIMSR), Hyderabad, Telangana - 500033
Source of Support: None, Conflict of Interest: None
|Date of Submission||13-Sep-2021|
|Date of Decision||12-Sep-2021|
|Date of Acceptance||14-Sep-2021|
|Date of Web Publication||04-Jan-2022|
Rapid advances in Artificial Intelligence (AI) have led to diagnostic, therapeutic, and intervention-based applications in the field of medicine. Today, there is a deep chasm between AI-based research articles and their translation to clinical anesthesia, which needs to be addressed. Machine learning (ML), the most widely applied arm of AI in medicine, confers the ability to analyze large volumes of data, find associations, and predict outcomes with ongoing learning by the computer. It involves algorithm creation, testing and analyses with the ability to perform cognitive functions including association between variables, pattern recognition, and prediction of outcomes. AI-supported closed loops have been designed for pharmacological maintenance of anesthesia and hemodynamic management. Mechanical robots can perform dexterity and skill-based tasks such as intubation and regional blocks with precision, whereas clinical-decision support systems in crisis situations may augment the role of the clinician. The possibilities are boundless, yet widespread adoption of AI is still far from the ground reality. Patient-related “Big Data” collection, validation, transfer, and testing are under ethical scrutiny. For this narrative review, we conducted a PubMed search in 2020-21 and retrieved articles related to AI and anesthesia. After careful consideration of the content, we prepared the review to highlight the growing importance of AI in anesthesia. Awareness and understanding of the basics of AI are the first steps to be undertaken by clinicians. In this narrative review, we have discussed salient features of ongoing AI research related to anesthesia and perioperative care.
Keywords: Advances in anesthesia, artificial intelligence, machine learning
|How to cite this article:|
Singh M, Nath G. Artificial intelligence and anesthesia: A narrative review. Saudi J Anaesth 2022;16:86-93
| Introduction|| |
Artificial intelligence (AI) is defined as the broad concept of machines designed to understand and perform tasks on their own in a “smart” manner. Early attempts at automation in medicine relied on hand-crafted algorithms based on rigid rules and therefore they failed in complex clinical situations. The growing human resource crisis in healthcare today may be an ideal setting for using technology to fill in the gaps; starting with telemedicine, digital health platforms and progressing to the adoption of AI.
So, what exactly is AI? It can be thought of as the programming of computers to simulate cognitive functions of the human mind, such as pattern recognition and problem solving. One important feature of AI is the ability to learn; that is, modification of actions based on previous experience. One of the simpler ways in which a computer can be used in anesthesia is to design a servo system, for instance, to maintain Bi-spectral Index (BIS) within a specified range by continuous assessment and adjustment of the infusion rate of the anesthetic agent(s). If the program is designed so that it can learn from its experience, this would come under machine learning. The various subsets of AI include machine learning, neural network, deep learning, robotics, and computer vision. This narrative review aims to discuss the principles and current advances in AI that are likely to impact clinical anesthesia in the near future. A literature search was conducted in the year 2020-21 using the keywords “artificial intelligence, machine learning, and anesthesia” in the database of MEDLINE/PubMed. This review is divided into sections dealing with machine learning, use of robots in anesthesia, implications of AI in anaesthetic management and the ethics involved in deployment of this technology.
| Machine Learning (ML)|| |
ML is now regarded as a mature technology and is employed in various industries across the world for quality control, error detection, and product recommendations. It involves collection and analysis of data by the computer, with a focus on model development and pattern detection to make intelligent predictions. Where humans can be overwhelmed by voluminous and complex data, machine learning models have the intrinsic capacity to provide valuable insights based on extensive analysis and computation. Broadly classifying ML into supervised, unsupervised, and reinforcement learning is useful to understand applications. While pattern recognition is unsupervised, supervised applications include training and testing data sets. Reinforcement learning relies on continual feedback for algorithm modification. The ultimate goal of AI-based systems in anesthesia is to give the computer access to real-time patient data as well as information from medical publications; and from these, the system would integrate the various types of data and “learn” the various relationships producing actionable information. The main challenges are firstly, incompatibility between data from different electronic health record (EHR) systems and secondly, the cost of development. A detailed understanding of basic AI concepts in medicine including fuzzy logic, neural networks, deep learning process, and Bayesian method is available in a recent review by Hashimoto and colleagues.
Various ML models have been developed to answer single relevant clinical questions by using representative data, such as using perioperative data to predict postoperative mortality and postinduction hypotension by analyzing preoperative and induction data., The reliability of these models depends on the quality and methodology of the studies on which they are based. Another issue is the difficulty clinicians face in understanding the mechanism by which prediction is done. This is especially seen in high accuracy models, which are often complex and may not provide adequate insight into the “why” of a recommendation in a clinical scenario. It is important to combine accuracy with explanations in healthcare settings; and even more so in anesthesiology, where understanding of mechanisms is critical and consequences of incorrect prediction can be serious.
According to a recent editorial, there is a large discrepancy between the number of ML articles published in technical versus medical journals, highlighting the fact that translation into clinical practice is still fraught with multiple hurdles. The suggested areas for AI application today include risk assessment and clinical treatment strategies. Computers are set to become indispensable tools not only for delivery of anesthetics but also for providing help in day-to-day clinical care and decision-making in anesthesia.
| Robots in Anesthesia|| |
A robot is any mechanical system capable of interacting with the environment according to directed interventions. In medicine, the precise and reproducible interventions possible with robots make their role in complex surgery and anesthesia especially attractive. They free the anesthesiologist from repetitive technical tasks, allowing them to concentrate on overall assessment and decision-making. Robots are designed to support clinicians by automating tasks and offer pertinent recommendations based on the clinical scenario to aid decision-making.
Target controlled infusion (TCI) systems can be considered the first-generation open-loop pharmacological robots. Their software has built-in pharmacokinetic models of different drugs, based on which they deliver loading doses to achieve specific plasma drug levels rapidly and then maintain a steady state. They display estimated (not measured) plasma and effect-site concentrations, which may differ from the actual concentrations, especially in patients who display extreme anthropomorphic features or in patients with different racial characteristics.,
In closed-loop systems, a “goal” for a measured variable is set by the operator and drug delivery is adjusted to make the gap between the set goal and actual variable as small as possible. In the newer versions, refinements in the algorithms such as reinforcement learning, neural network, and fuzzy logic minimize fluctuations from the set target. The initial models were single input single output (SISO) systems, recent advances have led to multiple input multiple output (MIMO) robots, addressing hypnosis, analgesia, and muscle relaxation simultaneously. Anesthesia parameters targeted by the SISO systems initially included hypnosis (BIS-guided) or muscle relaxation (nerve-stimulator guided) as feedback for drug delivery.,,, Subsequently, the “Analgoscore” was described for measuring pain during general anesthesia, calculated from the deviation of the patient's target values for heart rate and mean arterial pressure. Hemodynamic parameters used in SISO closed loop systems include blood pressure, stroke volume, and stroke volume variation.,,,,
BIS-guided autonomous systems have shown to be consistently superior to manually controlled propofol infusions in a number of trials.,,, Reinforcement learning is now used to rapidly achieve steady state based on a wide range of clinical parameters. A recent meta-analysis suggested that BIS-guided closed loop systems provide better clinical performance than manual control of drug delivery for intravenous anesthesia. BIS, respiratory rate, and oximetry have been combined to develop a hybrid sedation system (HSS) and has been shown to outperform manual administration.
Liu et al. were the first to use a dual closed loop system to control propofol and remifentanil infusions, but this was not truly a MIMO, as BIS was the only input guiding propofol infusion, whereas remifentanil infusion rate was linked to that of propofol. In 2013, Hemmerling and colleagues developed the McSleepy for automated closed-loop delivery of propofol, remifentanil, and rocuronium; targeting BIS, analgoscore, and train of four ratio for muscle relaxation. The efficacy of this system has been demonstrated in multiple scenarios including complex cardiac procedures and telemedicine, where a team of anesthesiologists based in Montreal remotely controlled TIVA in 20 patients located in Pisa.,
Closed loop control of hemodynamic parameters was first demonstrated by Ngan Kee et al., who used a simple on–off algorithm to control a phenylephrine infusion based on noninvasive blood pressure to manage postspinal hypotension. They further refined their system using a proportional algorithm and demonstrated its performance in a randomized controlled trial comparing it to manual control of the infusion. A novel closed loop vasopressor controller was developed by Rinehart et al. and was shown to maintain the mean arterial pressure within 5 mmHg of baseline in cases lasting over 2 hours. Joosten and colleagues have developed a closed-loop-assisted goal-directed fluid therapy system, which is guided by stroke volume and stroke volume variation.
Automated anesthesia delivery was combined with closed-loop fluid management in the ultimate MIMO system, where two independent CL systems were used for hypnosis, analgesia, and fluid management during major vascular surgery. Investigators are looking into the feasibility of combining fluid administration along with appropriate vasopressor use in a single automated system for guiding perioperative care. The advances in robot guided delivery of anesthetics have been summarized in [Table 1] and [Table 2].
The specialty of anesthesia includes cognitive as well as dexterity-based tasks; and robots have been designed to address both aspects. The first robotic intubation was done using the Da Vinci surgical system. Simulated fiber-optic-assisted oral and nasal intubations of a manikin were successful but technically difficult because of the sub-optimal robotic interface, with multiple arms. The Kepler intubation system consists of a single robotic arm controlled by a joystick to guide endotracheal intubation remotely. Successful intubation within 40–60 seconds was demonstrated in 90 simulated cases. For regional anesthesia, the Magellan system has been developed with a block needle mounted on a robotic arm. It also incorporates custom software for ultrasound-guided nerve recognition. The system was found to decrease variability among operators in the time taken for correct needle placement.
Clinical decision support system
Clinical decision support system (CDSS) may be knowledge-based with built-in algorithms or alternatively, nonknowledge based. Essentially designed to provide cognitive aids to the anesthetist, they are not autonomous in execution. Detailed understanding of cognitive robots and their role for clinical decision-making can be obtained in a narrative review by Nair et al.
Preoperative cognitive robots are used for the detection of abnormal laboratory findings, ensuring drug compliance, and conduct of checklists. Intraoperative robots have been used for accurate identification of appropriate prophylactic antibiotics with latest recommendations and dosing schedules, smart alarms, efficient gas delivery, and billing. Multiple components of the closed-loop system may lead to software failure, whereas incorrect data entry may lead to system failure. Safety checks, possibility to override the system, and then return to fully automated mode are important safety considerations. Edge cases are defined as scenarios wherein artificial intelligence is unable to perform in an expected manner, typically in a rare case. In this situation, humans are able to navigate smoothly even in a complex case; hence, physicians must be able to override automated systems when necessary. CDSS can integrate various physiologic data with preoperative data to produce guidance, which may be accepted or rejected by the anesthesiologist. This human–machine interface has been extensively studied in the aviation industry and may be extrapolated to anesthesia.,
| Current Trends Involving AI in Anesthesia|| |
Prompt adoption of technology has been the hall mark of anesthesia and is responsible for the significant increase in patient safety in this specialty over the last few decades. We need to understand the benefits that AI can bring to clinical care, as it aims at assisting the clinician and not replacing them. In this section, we have discussed some AI-based advances, which may not have reached clinical use yet but hold promise for future implementation.
| AI and Preanaesthetic Evaluation|| |
Traditionally, logistic regression has been the basis of scoring indices widely applied for risk assessment in anesthesia. Today, ML is being studied for risk stratification based on analysis of millions of perioperative data and intervention-based outcomes extracted from EHR of multiple centers. This type of risk prediction is especially useful for counselling, optimization, and planning the anesthetic management of individual cases with rare co-morbidities. Deep neural network (DNN), a branch of AI, has been used for prediction of postsurgical mortality by Lee et al. Data related to nearly 60,000 patients was extracted from EHR, with 82 features included for each patient. The algorithm was trained on 80% of data and validated on the remaining 20%. The intrinsic ability to integrate pre, intra and postoperative data seamlessly makes it superior to traditional risk assessment modules.
AI has been employed for airway assessment and encouraging results reported include ability to correctly predict the Cormack–Lehane view on direct laryngoscopy with analysis of face and neck. Preoperative checklists have contributed to increased surgical safety. CDSS for selection of the most appropriate antibiotic and dosing schedule have been developed and widely tested in multicenter trials.
| AI and Intraoperative Anesthetic Care|| |
Applications of AI in the operation theater include monitoring and alarm fatigue, administration of anesthesia, hemodynamic management, and clinical decision support. Intraoperative cognitive robots can be integrated into alarm systems for simultaneous analysis of several parameters thereby lowering the rate of false alarms.,
There is a growing interest in monitoring the target organ of anesthesia, the brain, via the electroencephalogram (EEG) as it may help in measuring the anesthetic effect. Currently, clinical trials are needed to validate any newly developed processed EEG monitor for each anesthetic drug, due to specific differences in drug-induced EEG changes. Research in AI may eliminate the need to perform clinical trials on hypnosis level monitors by the use of deep learning models.
Automated closed loop anesthesia delivery (CLAD) involves hypnosis, analgesia, and muscle relaxant delivery systems incorporating hemodynamic feedback mechanisms being studied with promising results. Advances in noninvasive cardiac output monitoring, cerebral oximetry, EEG processing, and nociception assessment form the basis for CLAD. Intraoperative analgesia-nociception monitors are now available to titrate opioid administration based on changes in the sympathetic and/or parasympathetic systems. The pain indices available include Nociception level Index, Analgesia-nociception index, Surgical plethysmographic index, Pupillometry, and Pupillary pain index, each of which is measured by its respective monitor. Currently, they are being evaluated in various surgical settings; however, there is insufficient data showing clinically significant changes in the outcomes.
Investigators have developed predictive models for intraoperative hypotension after analysis of more than 2 million arterial waveforms. To preempt critical events, investigators have developed “super learner” algorithms specifically trained to predict an acute hypotensive episode 10–30 minutes prior to the event, thereby allowing enough time for intervention. This ML algorithm relies on patient demographics, critical care assessment scores as well as invasive monitoring data, and has been proven to be reliable in critical care settings. “Predictive therapy” refers to prediction of long-term outcomes with the intention to start preemptive therapy for effective use of resources. This concept has been studied and validated in critical care by the use of “Artificial Intelligence Clinician” for lowering mortality rates in sepsis. Rapid technological advances such as three-dimensional ECG imaging as well as analysis of arterial waveforms have been used to provide clues to early detection of coronary insufficiency. Manifold learning, a subtype of AI has been used to study both ECG and ABP waveforms to reflect the underlying cardiovascular status via 3D image visualization. EHR along with real-time monitoring have been studied to predict the onset of reduced ejection fraction in patients undergoing complex surgical procedures. Obviously, confirmatory tests including echocardiography and biomarker assay may be indicated; however, the implications of early suspicion and suitable intervention are vital in decreasing perioperative morbidity and mortality. The ability to predict and investigate these rare but devastating complications may become critical for overall morbidity and mortality reduction.
| AI and Postoperative Care|| |
Opioid-induced respiratory depression is traditionally detected by assessment of respiratory rate, pulse oximetry, and mental status. Investigators have studied ataxic or irregular breathing pattern and developed a ML algorithm for quantifying breathing pattern to help with prediction of respiratory depression in the postoperative period. Advances in telecommunication have led to the development of wireless intelligent patient-controlled analgesia for feedback-enabled pain management in surgical homes. Early discharge and ambulation in patients can be closely monitored via video conferences and effective feedback mechanisms, impacting healthcare costs and patient satisfaction in a positive manner.
| Ethics of Artificial Intelligence|| |
Today, the future of AI in medicine, which relies heavily on patient data, cannot progress without sound ethical practices and regulations that place patients and their privacy uppermost. Ethical and humanistic principles remain significant as new interventions relying on AI are developed using medical records by being outsourced to researchers or companies. AI-based medical research needs a strong regulatory body to weigh in all factors for ethical data stewardship. Apart from checking the veracity of the data, there exists the fundamental issue of implicit or explicit bias being integrated into codes written by human beings. Flawed sampling and lack of diversity are major contributors resulting in bias within ML.
ML offers strategies to revolutionize outcomes via computational science in perioperative care by impacting risk assessment, optimization, intra and postoperative interventions, which are backed by best evidence. An ensuing challenge therefore is the evaluation of the methods employed in ML research, which can be explained by the author and understood by the readers. We may expect sophisticated closed loops in anesthesia to become standard of care, yet continual engagement with the clinician is vital for optimal patient care. Guidelines for developing and reporting ML-based predictive models in biomedical research have been developed to ensure ethical research.
Currently available AI solutions focus on providing CDSS and do not replace the physician's judgment. Obviously, the anesthetist needs to take a call in a crisis to follow or ignore AI-based intervention with medicolegal implications. Validation and understanding of the AI systems in clinical care by the clinician is very important to clearly understand strength and flaws of technology. AI-driven healthcare promises better patient outcomes yet risks due to autonomous technology need careful consideration in the healthcare sector where incorrect deductions can be disastrous. Currently, liability of negligence is based on what a reasonable physician would do under similar circumstances. These standards will evolve as technology enters multiple areas of patient care and clinicians need to update and familiarize themselves on a continual basis. While promoting responsible AI, anticipated disruption secondary to loss of jobs is an important consideration.
| Conclusions|| |
Only a minority of AI-based studies focus on integration of AI in daily clinical workflow in anesthesia; and just a handful of these have been shown to impact clinical care. Improvement of anesthesia provider's productivity and patient outcome by using “augmented intelligence” based on pooled patient data as well as incorporation of clinical guidelines is the underlying goal for adoption of AI. While this may seem distant to some healthcare workers, there is a raging debate on the socio-economic impact of robots on the human workforce in every field.
In advanced nations, strong collaboration among clinicians, scientists, manufacturers, regulators, and administrators has led to consistent electronic health records amenable to data storage and exchange. In India, we foresee multiple difficulties starting from data viability, standardization of monitoring, acceptance, and validation of basic technological tools. Handwritten medical records continue to be the rule rather than an exception in advanced surgical setups. The timeline may be unpredictable, yet the medical profession cannot steer clear of this eventuality. This review aims to provide a primer on the immense potential of AI and its role in anesthetic care with the objective of stimulating interest and awareness among the anesthesia fraternity.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
Hashimoto DA, Walkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology. Anesthesiology 2020;132:379-94.
Lee CK, Hofer I, Gabel E, Baldi P, Cannesson M. Development and validation of a deep neural network model for prediction of postoperative in hospital mortality. Anesthesiology 2018;129:649-2.
Kendale S, Kulkarni P, Rosenberg AD, Wang J. Supervised machine-learning predictive analytics for prediction of postinduction hypotension. Anesthesiology 2018;129:675-88.
Mathis MR, Kheterpal S, Najarian K. Artificial intelligence for anesthesia: What the practicing clinician needs to know: More than black magic for the art of the dark. Anesthesiology 2018;129:619-22.
Lonsdale H, Jalali A, Gálvez JA, Ahumada LM, Simpao AF. Artificial intelligence in anesthesiology: Hype, hope, and hurdles. Anesth Analg 2020;130:1111-3.
Zaouter C, Joosten A, Rinehart J, Struys MMRF, Hemmerling TM. Autonomous systems in anesthesia: Where do we stand in 2020? A narrative review. Anesth Analg 2020;130:1120-32.
Donati F, Miller DR, Fiset P. Target-controlled infusion devices: Are we missing much? Can J Anaesth 2011;58:349-53.
Engbers FHM, Dahan A. Anomalies in target-controlled infusion: An analysis after 20 years of clinical use. Anesthesia 2018;73:619-30.
Morley A, Derrick J, Mainland P. Closed loop control of anesthesia: An assessment of the bispectral index as the target of control. Anesthesia 2000;5510:953-9.
Struys MM, De Smet T, Versichelen LF, Van De Velde S, Van den Broecke R, Mortier EP. Comparison of closed-loop controlled administration of propofol using Bispectral Index as the controlled variable versus “standard practice” controlled administration. Anesthesiology 2001;95:6-17.
Absalom AR, Sutcliffe N, Kenny GN. Closed-loop control of anesthesia using Bispectral index: Performance assessment in patients undergoing major orthopedic surgery under combined general and regional anesthesia. Anesthesiology 2002;96:67-73.
Eleveld DJ, Proost JH, Wierda JM. Evaluation of a closed-loop muscle relaxation control system. Anesth Analg 2005;101:758-64.
Hemmerling TM, Charabati S, Salhab E, Bracco D, Mathieu PA. The Analgoscore: A novel score to monitor intraoperative nociception and its use for closed-loop application of remifentanil. J Comput 2009;4:311-8.
Ngan Kee WD, Tam YH, Khaw KS, Ng FF, Critchley LA, Karmakar MK. Closed-loop feedback computer-controlled infusion of phenylephrine for maintaining blood pressure during spinal anesthesia for caesarean section: A preliminary descriptive study. Anesthesia 2007;62:1251-6.
Ngan Kee WD, Khaw KS, Ng FF, Tam YH. Randomized comparison of closed-loop feedback computer-controlled with manual-controlled infusion of phenylephrine for maintaining arterial pressure during spinal anesthesia for caesarean delivery. Br J Anaesth 2013;110:59-65.
Rinehart J, Ma M, Calderon MD, Cannesson M. Feasibility of automated titration of vasopressor infusions using a novel closed-loop controller. J Clin Monit Comput 2018;32:5-11.
Joosten A, Alexander B, Duranteau J, Taccone FS, Creteur J, Vincent JL, et al
. Feasibility of closed-loop titration of norepinephrine infusion in patients undergoing moderate- and high-risk surgery. Br J Anaesth 2019;123:430-8.
Joosten A, Coekelenbergh S, Delaporte A, Ickx B, Closset J, Roumeguere T, et al
. Implementation of closed-loop-assisted intraoperative goal directed fluid therapy during major abdominal surgery: A case control study with propensity matching. Eur J Anaesthesiol 2018;35:650-8.
Liu N, Chazot T, Genty A, Landais A, Restoux A, McGee K, et al
. Titration of propofol for anesthetic induction and maintenance guided by the bispectral index: Closed-loop versus manual control: A prospective, randomized, multicenter study. Anesthesiology 2006;104:686-95.
Puri GD, Kumar B, Aveek J. Closed-loop anesthesia delivery system (CLADS) using bispectral index: A performance assessment study. Anaesth Intensive Care 2007;353:357-62.
Madhavan JS, Puri GD, Mathew PJ. Closed-loop isoflurane administration with bispectral index in open heart surgery: Randomized controlled trial with manual control. Acta Anaesthesiol Taiwan 2011;49:130-5.
Neckebroek M, Ionescu CM, van Amsterdam K. De Smet T, De Baets P, Decruyenaere J, et al
. A comparison of propofol-to-BIS post-operative intensive care sedation by means of target controlled infusion, Bayesian-based and predictive control methods: An observational, open-label pilot study. J Clin Monit Comput 2019;33:675-86.
Moore BL, Doufas AG, Pyeatt LD. Reinforcement learning: A novel method for optimal control of propofol-induced hypnosis. Anesth Analg 2011;112:360-7.
Pasin L, Nardelli P, Pintaudi M, Greco M, Zambon M, Cabrini L, et al
. Closed-loop delivery systems versus manually controlled administration of total IV anesthesia: A meta-analysis of randomized clinical trials. Anesth Analg 2017;124:456-64.
Zaouter C, Taddei R, Wehbe M, Arbeid E, Cyr S, Giunta F, et al.
A novel system for automated propofol sedation: Hybrid sedation system (HSS). J Clin Monit Comput 2017;31:309-17.
Liu N, Chazot T, Hamada S, Landais A, Boichut N, Dussaussoy C, et al
. Closed-loop coadministration of propofol and remifentanil guided by bispectral index: A randomized multicenter study. Anesth Analg 2011;112:546-57.
Hemmerling TM, Arbeid E, Wehbe M, Cyr S, Taddei R, Zaouter C. Evaluation of a novel closed-loop total intravenous anesthesia drug delivery system: A randomized controlled trial. Br J Anaesth 2013;110:1031-9.
Zaouter C, Hemmerling TM, Lanchon R, Valoti E, Remy A, Leuillet S, et al
. The feasibility of a completely automated total IV anesthesia drug delivery system for cardiac surgery. Anesth Analg 2016;123:885-93.
Hemmerling TM, Arbeid E, Wehbe M, Cyr S, Giunta F, Zaouter C. Transcontinental anesthesia: A pilot study. Br J Anaesth 2013;110:758-63.
Joosten A, Jame V, Alexander B, Chazot T, Liu N, Cannesson M, et al
. Feasibility of fully automated hypnosis, analgesia, and fluid management using 2 independent closed-loop systems during major vascular surgery: A pilot study. Anesth Analg 2019;128:e88-92.
Michard F, Liu N, Kurz A. The future of intraoperative blood pressure management. J Clin Monit Comput 2018;32:1-4.
Tighe PJ, Badiyan SJ, Luria I, Lampotang S, Parekattil S. Robot-assisted airway support: A simulated case. Anesth Analg 2010;111:929-31.
Hemmerling TM, Wehbe M, Zaouter C, Taddei R, Morse J. The Kepler intubation system. Anesth Analg 2012;114:590-4.
Morse J, Terrasini N, Wehbe M, Philippona C, Zaouter C, Cyr S, et al
. Comparison of success rates, learning curves, and inter-subject performance variability of robot-assisted and manual ultrasound-guided nerve block needle guidance in simulation. Br J Anaesth 2014;112:1092-7.
Nair BG, Gabel E, Hofer I, Schwid HA, Cannesson M. Intraoperative clinical decision support for anesthesia: A narrative review of available systems. Anesth Analg 2017;124:603-17.
Canales C, Lee C, Cannesson M. Science without conscience is but the ruin of the soul: The ethics of big data and artificial intelligence in perioperative medicine. Anesth Analg 2020;130:1234-43.
Jabaley CS, Lynde GC, Caridi-Scheible ME, O'Reilly-Shah VN. The human-machine interface in anesthesiology: Corollaries and lessons learned from aviation and crewed spaceflight. Anesth Analg 2020;130:1255-60.
Voas RB. A description of the astronaut's task in project mercury. Hum Factors 1961;3:149-65.
Hofer IS, Burns M, Kendale S, Wanderer JP. Realistically integrating machine learning into clinical practice: A road map of opportunities, challenges, and a potential future Anesth Analg 2020;130:1115-8.
Connor CW, Segal S. Accurate classification of difficult intubation by computerized facial analysis. Anesth Analg 2011;112:84-93.
Freundlich RE, Grondin L, Tremper KK, Saran KA, Kheterpal S. Automated electronic reminders to prevent miscommunication among primary medical, surgical and anesthesia providers: A root cause analysis. BMJ Qual Saf 2012;21:850-4.
Edworthy J, Hellier E. Alarms and human behaviour: Implications for medical alarms. Br J Anaesth 2006;97:12-7.
Belur Nagaraj S, Ramaswamy SM, Weerink MAS, Struys MMRF. Predicting deep hypnotic state from sleep brain rhythms using deep learning: A data-repurposing approach. Anesth Analg 2020;130:1211-21.
Upton HD, Ludbrook GL, Wing A, Sleigh JW. Intraoperative “analgesia nociception index”-guided fentanyl administration during sevoflurane anesthesia in lumbar discectomy and laminectomy: A randomized clinical trial. Anesth Analg 2017;125:81-90.
Funcke S, Pinnschmidt HO, Wesseler S, Brinkmann C, Beyer B, Jazbutyte V, et al
. Guiding opioid administration by 3 different analgesia nociception monitoring indices during general anesthesia alters intraoperative sufentanil consumption and stress hormone release: A randomized controlled pilot study. Anesth Analg 2020;130:1264-73.
Hatib F, Jian Z, Buddi S, Lee C, Settels J, Sibert K, et al
. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology 2018;129:663-74.
Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. The artificial intelligence clinician learns optimal treatment strategies for sepsis in intensive care. Nat Med 2018;24:1716-20.
Wang SC, Wu HT, Huang PH, Chang CH, Ting CK, Lin YT. Novel imaging revealing inner dynamics for cardiovascular waveform analysis via unsupervised manifold learning. Anesth Analg 2020;130:1244-54.
Mathis MR, Engoren MC, Joo H, Maile MD, Aaronson KD, Burns ML, et al
. Early detection of heart failure with reduced ejection fraction using perioperative data among noncardiac surgical patients: A machine-learning approach. Anesth Analg 2020;130:1188-200.
Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, et al
. Do no harm: A roadmap for responsible machine learning for health care. Nat Med 2019;25:1337-40.
Ermer SC, Farney RJ, Johnson KB, Orr JA, Egan TD, Brewer LM. An automated algorithm incorporating poincaré analysis can quantify the severity of opioid-induced ataxic breathing. Anesth Analg 2020;130:1147-56.
Wang R, Wang S, Duan N, Wang Q. From patient-controlled analgesia to artificial intelligence-assisted patient-controlled analgesia: Practices and perspectives. Front Med (Lausanne) 2020;7:145.
Keyes KM, Westreich D. UK Biobank, big data, and the consequences of non-representativeness. Lancet 2019;393:1297.
Luo W, Phung D, Tran T, Gupta S, Rana S, Karmakar C, et al
. Guidelines for developing and reporting Machine Learning predictive models in biomedical research: A multidisciplinary view. J Med Internet Res 2016;18:e323.
Hemmerling TM. Robots will perform anesthesia in the near future. Anesthesiology 2020;132:219-20.
Parvinian B, Scully C, Wiyor H, Kumar A, Weininger S. Regulatory considerations for physiological closed-loop controlled medical devices used for automated critical care: Food and drug administration workshop discussion topics. Anesth Analg 2018;126:1916-25.
Casas-Arroyave FD, Fernandez JM, Zuleta Tabon JJ. Evaluation of a closed-loop intravenous total anesthesia delivery system with BIS monitoring compared to an open-loop target-controlled infusion (TCI) system: randomised controlled clinical trial. Colombian Journal of Anaesthesiology 2019:47;84-91.
[Table 1], [Table 2]
|This article has been cited by|
||Technological Advances in Cardiac Anesthesia: We Live in Exciting Times Ensuring Better Patient Safety and Care
| ||Kunal Sarin, Ajay Chandra, Anurag Garg |
| ||Bali Journal of Anesthesiology. 2022; 6(2): 123 |
|[Pubmed] | [DOI]|