Robotic Anesthesia: A Vision for 2050.

The last 2 decades have brought important developments in anesthetic technology, including robotic anesthesia. Anesthesiologists titrate the administration of pharmacological agents to the patients' physiology and the needs of surgery, using a variety of sophisticated equipment (we use the term "pilots of the human biosphere"). In anesthesia, increased safety seems coupled with increased technology and innovation. This article gives an overview of the technological developments over the past decades, both in terms of pharmacological and mechanical robots, which have laid the groundwork for robotic anesthesia: target-controlled drug infusion systems, closed-loop administration of anesthesia and sedation, mechanical robots for intubation, and the latest development in the world of communication with the arrival of artificial intelligence (AI)-derived chatbots are presented.

Comparative Analysis of Machine-Learning Model Performance in Image Analysis: The Impact of Dataset Diversity and Size.

BACKGROUND: This study presents an analysis of machine-learning model performance in image analysis, with a specific focus on videolaryngoscopy procedures. The research aimed to explore how dataset diversity and size affect the performance of machine-learning models, an issue vital to the advancement of clinical artificial intelligence tools. METHODS: A total of 377 videolaryngoscopy videos from YouTube were used to create 6 varied datasets, each differing in patient diversity and image count. The study also incorporates data augmentation techniques to enhance these datasets further. Two machine-learning models, YOLOv5-Small and YOLOv8-Small, were trained and evaluated on metrics such as F1 score (a statistical measure that combines the precision and recall of the model into a single metric, reflecting its overall accuracy), precision, recall, mAP@50, and mAP@50-95. RESULTS: The findings indicate a significant impact of dataset configuration on model performance, especially the balance between diversity and quantity. The Multi-25 × 10 dataset, featuring 25 images from 10 different patients, demonstrates superior performance, highlighting the value of a well-balanced dataset. The study also finds that the effects of data augmentation vary across different types of datasets. CONCLUSIONS: Overall, this study emphasizes the critical role of dataset structure in the performance of machine-learning models in medical image analysis. It underscores the necessity of striking an optimal balance between dataset size and diversity, thereby illuminating the complexities inherent in data-driven machine-learning development.

Beyond the Echo: The Evolution and Revolution of Ultrasound in Anesthesia.

This article explores the evolving role of ultrasound technology in anesthesia. Ultrasound emerged decades ago, offering clinicians noninvasive, economical, radiation-free, and real-time imaging capabilities. It might seem that such an old technology with apparent limitations might have had its day, but this review discusses both the current applications of ultrasound (in nerve blocks, vascular access, and airway management) and then, more speculatively, shows how integration of advanced ultrasound modalities such as contrast-enhanced imaging with virtual reality (VR), or nanotechnology can alter perioperative patient care. This article will also explore the potential of robotics and artificial intelligence (AI) in augmenting ultrasound-guided anesthetic procedures and their implications for medical practice and education.

Comparison of time-series models for predicting physiological metrics under sedation.

This study presents a comprehensive comparison of multiple time-series models applied to physiological metric predictions. It aims to explore the effectiveness of both statistical prediction models and pharmacokinetic-pharmacodynamic prediction model and modern deep learning approaches. Specifically, the study focuses on predicting the bispectral index (BIS), a vital metric in anesthesia used to assess the depth of sedation during surgery, using datasets collected from real-life surgeries. The goal is to evaluate and compare model performance considering both univariate and multivariate schemes. Accurate BIS prediction is essential for avoiding under- or over-sedation, which can lead to adverse outcomes. The study investigates a range of models: The traditional mathematical models include the pharmacokinetic-pharmacodynamic model and statistical models such as autoregressive integrated moving average (ARIMA) and vector autoregression (VAR). The deep learning models encompass recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), as well as Temporal Convolutional Networks (TCNs) and Transformer models. The analysis focuses on evaluating model performance in predicting the BIS using two distinct datasets of physiological metrics collected from actual surgical procedures. It explores both univariate and multivariate prediction schemes and investigates how different combinations of features and input sequence lengths impact model accuracy. The experimental findings reveal significant performance differences among the models: In univariate prediction scenarios for predicting BIS, the LSTM model demonstrates a 2.88% improvement over the second-best performing model. For multivariate predictions, the LSTM model outperforms others by 6.67% compared to the next best model. Furthermore, the addition of Electromyography (EMG) and Mean Arterial Pressure (MAP) brings significant accuracy improvement when predicting BIS. The study emphasizes the importance of selecting and building appropriate time-series models to achieve accurate predictions in biomedical applications. This research provides insights to guide future efforts in improving vital sign prediction methodologies for clinical and research purposes. Clinically, with improvements in the prediction of physiological parameters, clinicians can be informed of interventions if an anomaly is detected or predicted.

Pro-Con Debate: Should All General Anesthesia Be Done Using Target-Controlled Propofol Infusion Guided by Objective Monitoring of Depth of Anesthesia?

In this Pro-Con commentary article, we discuss whether all general anesthesia should be done using target-controlled propofol anesthesia guided by monitoring of depth of anesthesia. This is an ongoing debate since more than 25 years, representing a scientific, cultural as well as geographical divide in the anesthesia community. The Pro side argues that total intravenous anesthesia causes less postoperative nausea and higher patient satisfaction than anesthesia using volatile anesthetics. Target-controlled infusion (TCI) of anesthetic agents allows for better titration of intravenous anesthesia using pharmacokinetic models. Processed EEG monitors, such as bispectral index monitoring, allows for better assessing the effect of TCI anesthesia than solely assessment of clinical parameters, such as ECG or blood pressure. The combination of TCI propofol and objective depth of anesthesia monitoring allows creating a pharmacokinetic-pharmacodynamic profile for each patient. Finally, anesthesia using volatile anesthetics poses health risks for healthcare professionals and contributes to greenhouse effect. The Con side argues that for procedures accompanied with ischemia and reperfusion injury of an organ or tissue and for patients suffering from a severe inflammation' the use of volatile anesthetics might well have its advantages above propofol. In times of sudden shortage of drugs, volatile anesthetics can overcome the restriction in the operating theater or even on the intensive care unit, which is another advantage. Volatile anesthetics can be used for induction of anesthesia when IV access is impossible, end-tidal measurements of volatile anesthetic concentration allows confirmation that patients receive anesthetics. Taking environmental considerations into account, both propofol and volatile anesthetics bear certain harm to the environment, be it as waste product or as greenhouse gases. The authors therefore suggest to carefully considering advantages and disadvantages for each patient in its according environment. A well-balanced choice based on the available literature is recommended. The authors recommend careful consideration of advantages and disadvantages of each technique when tailoring an anesthetic to meet patient needs. Where appropriate, anesthesia providers are encouraged to account for unique features of anesthetic drug behavior, patient-reported and observed postoperative outcomes, and economic and environmental considerations when choosing any of the 2 described techniques.

Clinical Performance of Decision Support Systems in Anesthesia, Intensive Care, and Emergency Medicine: A Systematic Review and Meta-Analysis.

BACKGROUND: Medical technology is expanding at an alarming rate, with its integration into health care often reflected by the constant evolution of best practices. This rapid expansion of available treatment modalities, when coupled with progressively increasing amounts of consequential data for health care professionals to manage, creates an environment where complex and timely decision-making without the aid of technology is inconceivable. Decision support systems (DSSs) were, therefore, developed as a means of supporting the clinical duties of health care professionals through immediate point-of-care referencing. The integration of DSS can be especially useful in critical care medicine, where the combination of complex pathologies, the multitude of parameters, and the general state of patients require swift informed decision-making. The systematic review and meta-analysis were performed to evaluate DSS outcomes compared to the standard of care (SOC) in critical care medicine. METHODS: This systematic review and subsequent meta-analysis were performed after the EQUATOR networks Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA). We systematically explored PubMed, Ovid, Central, and Scopus for randomized controlled trials (RCTs) from January 2000 to December 2021. The primary outcome of this study was to evaluate whether DSS is more effective than SOC practice in critical care medicine within the following disciplines: anesthesia, emergency department (ED), and intensive care unit (ICU). A random-effects model was used to estimate the effect of DSS performance, with 95% confidence intervals (CIs) in both continuous and dichotomous results. Outcome-based, department-specific, and study-design subgroup analyses were performed. RESULTS: A total of 34 RCTs were included for analysis. In total, 68,102 participants received DSS intervention, while 111,515 received SOC. Analysis of the continuous (standardized mean difference [SMD], -0.66; 95% CI [-1.01 to -0.30]; P < .01) and binary outcomes (odds ratio [OR], 0.64; 95% CI, [0.44-0.91]; P < .01) was statistically significant and suggests that health interventions are marginally improved with DSS integration in comparison to SOC in critical care medicine. Subgroup analysis in anesthesia (SMD, -0.89; 95% CI, [-1.71 to -0.07]; P < .01) and ICU (SMD, -0.63; 95% CI [-1.14 to -0.12]; P < .01) were deemed statistically supportive of DSS in improving outcome, with evidence being indeterminate in the field of emergency medicine (SMD, -0.24; 95% CI, [-0.71 to 0.23]; P < .01). CONCLUSIONS: DSSs were associated with a beneficial impact in critical care medicine on a continuous and binary scale; however, the ED subgroup was found to be inconclusive. Additional RCTs are required to determine the effectiveness of DSS in critical care medicine.

Description of a Novel Web-Based Liposuction System to Estimate Fat Volume and Distribution.

BACKGROUND: Preoperative planning for liposuction is vital to ensure safe practice and patient satisfaction. However, current standards of fat assessment before surgery are guided by subjective methods such as visual inspection, skin-pinch tests, and waist circumference measurements. OBJECTIVES: This study aimed to develop an inexpensive software-based tool that utilizes ultrasound (US) imaging and an online platform to accurately simulate regional subcutaneous adipose tissue (SAT) distribution and safe volume estimation for liposuction procedures. METHODS: The authors present a web-based platform with integrated 2-dimensional (2D) and 3-dimensional (3D) simulations of SAT to support liposuction planning and execution. SAT-Map was constructed using multiple sub-applications linked with the python framework programming language (Wilmington, DE). RESULTS: The SAT-Map interface provides an intuitive and fluid means of generating patient-specific models and volumetric data. To further accommodate this, an operational manual was prepared to achieve consistent visualization and examination of estimated SAT content. The system currently supports static 2D heatmap simulation and 3D interactive virtual modelling of the SAT distribution. Supplementary clinical studies are needed to evaluate SAT-Map's clinical performance and practicality. CONCLUSIONS: SAT-Map revolutionizes the concept of preoperative planning for liposuction by developing the first combined web-based software that objectively simulates fat distribution and measures safe liposuction volume. Our software approach presents a cost-efficient, accessible, and user-friendly system offering multiple advantages over current SAT assessment modalities. The immediacy of clinically accurate 3D virtual simulation provides objective support to surgeons towards improving patient conversation, outcomes, and satisfaction in liposuction procedures.

Ultrasound Calculation of Fat Volume for Liposuction: A Clinical Software Validation.

BACKGROUND: Fat manipulation procedures such as liposuction contain a degree of subjectivity primarily guided by the surgeon's visual or tactile perception of the underlying fat. Currently, there is no cost-effective, direct method to objectively measure fat depth and volume in real time. OBJECTIVES: Utilizing innovative ultrasound-based software, the authors aimed to validate fat tissue volume and distribution measurements in the preoperative setting. METHODS: Eighteen participants were recruited to evaluate the accuracy of the new software. Recruited participants underwent ultrasound scans within the preoperative markings of the study area before surgery. Ultrasound-estimated fat profiles were generated with the in-house software and compared directly with the intraoperative aspirated fat recorded after gravity separation. RESULTS: Participants' mean age and BMI were 47.6 (11.3) years and 25.6 (2.3) kg/m2, respectively. Evaluation of trial data showed promising results following the use of a Bland Altman agreement analysis. For the 18 patients and 44 volumes estimated, 43 of 44 measurements fell within a confidence interval of 95% when compared with the clinical lipoaspirate (dry) volumes collected postsurgery. The bias was estimated at 9.15 mL with a standard deviation of 17.08 mL and 95% confidence interval between -24.34 mL and 42.63 mL. CONCLUSIONS: Preoperative fat assessment measurements agreed significantly with intraoperative lipoaspirate volumes. The pilot study demonstrates, for the first time, a novel companion tool with the prospect of supporting surgeons in surgical planning, measuring, and executing the transfer of adipose tissues.

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

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 better-individualized 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 clinicians from repetitive tasks but can also reduce mental workload allowing them to focus on tasks that require human intelligence such as analytical and clinical approach, lifesaving decision-making capacity, and interpersonal interaction. Nevertheless, further studies have yet to be done to test the combination of these 3 types of robots to maintain simultaneously the homeostasis of multiple biological variables and to test the safety of such combination on a large-scale population.

Technologies to Optimize the Care of Severe COVID-19 Patients for Health Care Providers Challenged by Limited Resources.

Health care systems are belligerently responding to the new coronavirus disease 2019 (COVID-19). The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a specific condition, whose distinctive features are severe hypoxemia associated with (>50% of cases) normal respiratory system compliance. When a patient requires intubation and invasive ventilation, the outcome is poor, and the length of stay in the intensive care unit (ICU) is usually 2 or 3 weeks. In this article, the authors review several technological devices, which could support health care providers at the bedside to optimize the care for COVID-19 patients who are sedated, paralyzed, and ventilated. Particular attention is provided to the use of videolaryngoscopes (VL) because these can assist anesthetists to perform a successful intubation outside the ICU while protecting health care providers from this viral infection. Authors will also review processed electroencephalographic (EEG) monitors which are used to better titrate sedation and the train-of-four monitors which are utilized to better administer neuromuscular blocking agents in the view of sparing limited pharmacological resources. COVID-19 can rapidly exhaust human and technological resources too within the ICU. This review features a series of technological advancements that can significantly improve the care of patients requiring isolation. The working conditions in isolation could cause gaps or barriers in communication, fatigue, and poor documentation of provided care. The available technology has several advantages including (a) facilitating appropriate paperless documentation and communication between all health care givers working in isolation rooms or large isolation areas; (b) testing patients and staff at the bedside using smart point-of-care diagnostics (SPOCD) to confirm COVID-19 infection; (c) allowing diagnostics and treatment at the bedside through point-of-care ultrasound (POCUS) and thromboelastography (TEG); (d) adapting the use of anesthetic machines and the use of volatile anesthetics. Implementing technologies for safeguarding health care providers as well as monitoring the limited pharmacological resources are paramount. Only by leveraging new technologies, it will be possible to sustain and support health care systems during the expected long course of this pandemic.

Pain management in abdominal surgery.

BACKGROUND: Abdominal surgery has undergone major changes during the last two decades with a general shift from open, invasive surgery to closed minimally invasive surgery. Accordingly, pain management strategies have also changed, especially with the introduction of ultrasound-guided abdominal wall blocks. Despite laparoscopic abdominal surgery classified as minimal, pain can be quite significant and needs to be addressed appropriately. PURPOSE: This narrative review focuses on adequate pain strategies for various types of surgery. The respective techniques are described and examples of specific pain management strategies given. Advantages and disadvantages of techniques are discussed. This review can serve as a sort of empirical guideline and orientation for the reader to develop their own strategy as well as bringing surgeons up-to-date with the latest anesthetic techniques. CONCLUSION: Pain is not less or less relevant in minimally invasive surgery. New hallmarks of a multimodal pain strategy are abdominal wall blocks, either as single shot or continuously. Minor open surgery is best performed under a combination of loco-regional blocks and continuous sedation. Abdominal wall blocks, NSAIDs, and short-acting opioids given by nurses or as PCA present the best multimodal pain strategy in abdominal surgery. Epidural analgesia and spinal anesthesia have become second-line options or are reserved for specific patient morbidities or surgical requirements.

The Montreal Augmentation Mammaplasty Operation (MAMO) Simulator: An Alternative Method to Train and Assess Competence in Breast Augmentation Procedures.

BACKGROUND: Surgical residents' exposure to aesthetic procedures remains limited in residency training. The development of the Montreal augmentation mammaplasty operation (MAMO) simulator aims to provide an adjunctive training method and assessment tool to complement the evolving competency-based surgical curriculum. OBJECTIVES: To perform face, content, and construct validations of the MAMO simulator for subpectoral breast augmentation procedures and assess the reliability of the assessment scales used. METHODS: Plastic surgery staff and residents were recruited to perform a subpectoral breast augmentation on the simulator. Video recordings of their performance were blindly evaluated using the objective structured assessment of technical skills (OSATS) system consisting of the global rating scale (GRS), mammaplasty objective assessment tool (MOAT), and a surgery-specific Checklist score. RESULTS: Fourteen plastic surgery residents and seven expert plastic surgeons were enrolled. Experts' performance was significantly higher than residents' according to each of GRS, MOAT, and Checklist scores. Mean values of residents and experts were 23.4 (2.5) vs 36.9 (3.1) (P < 0.0001) for GRS score, 30.4 (2.2) vs 40 (3.2) (P < 0.0001) for MOAT scores, and 9.7 (1.5) vs 12 (1) (P < 0.001) for Checklist scores, respectively. Face and content validations showed excellent results among parameters evaluated, with an overall mean score of 4.8 (0.3) on 5. Cronbach's alpha was 0.96 and 0.83 for GRS and MOAT scores, respectively. Intraclass correlation coefficients for interrater reliability were excellent at 0.93, 0.92, and 0.89 for the GRS, MOAT, and Checklist scores, respectively. CONCLUSIONS: This study proves the construct simulator to be valid and the assessment scales to be reliable.

Clinical Performance and Safety of Closed-Loop Systems: A Systematic Review and Meta-analysis of Randomized Controlled Trials.

Automated systems can improve the stability of controlled variables and reduce the workload in clinical practice without increasing the risks to patients. We conducted this review and meta-analysis to assess the clinical performance of closed-loop systems compared with manual control. Our primary outcome was the accuracy of closed-loop systems in comparison with manual control to maintain a given variable in a desired target range. The occurrence of overshoot and undershoot episodes was the secondary outcome. We retrieved randomized controlled trials on accuracy and safety of closed-loop systems versus manual control. Our primary outcome was the percentage of time during which the system was able to maintain a given variable (eg, bispectral index or oxygen saturation) in a desired range or the proportion of the target measurements that was within the required range. Our secondary outcome was the percentage of time or the number of episodes that the controlled variable was above or below the target range. The standardized mean difference and 95% confidence interval (CI) were calculated for continuous outcomes, whereas the odds ratio and 95% CI were estimated for dichotomous outcomes. Thirty-six trials were included. Compared with manual control, automated systems allowed better maintenance of the controlled variable in the anesthesia drug delivery setting (95% CI, 11.7%-23.1%; percentage of time, P < 0.0001, number of studies: n = 15), in patients with diabetes mellitus (95% CI, 11.5%-30.9%; percentage of time, P = 0.001, n = 8), and in patients mechanically ventilated (95% CI, 1.5%-23.1%; percentage of time, P = 0.03, n = 8). Heterogeneity among the studies was high (>75%). We observed a significant reduction of episodes of overshooting and undershooting when closed-loop systems were used. The use of automated systems can result in better control of a given target within a selected range. There was a decrease of overshooting or undershooting of a given target with closed-loop systems.

Feasibility of Automated Propofol Sedation for Transcatheter Aortic Valve Implantation: A Pilot Study.

BACKGROUND: Recently, several trials have shown that closed-loop sedation is feasible. No study has used automated sedation in extremely frail patients, such as those scheduled for transcatheter aortic valve implantation (TAVI). We developed and tested a novel automated sedation system for this kind of population and surgery. The system integrates a decision support system that detects respiratory and hemodynamic events via smart alarms, which provide pertinent/related clinical suggestions and treatment options. The main objective was the feasibility of closed-loop sedation, defined as successful automated sedation without manual override. Secondary qualitative observations were clinical and controller performance. METHODS: Twenty patients scheduled for elective TAVI were enrolled. Sedation was administered via a closed-loop delivery system designed for propofol. The clinical performance of sedation was the efficacy to maintain a bispectral index (BIS) of 65. To evaluate the sedation performance, BIS values were stratified into 4 categories: excellent, very good, good, and inadequate sedation control, defined as BIS values within 10%, ranging from 11% to 20%, ranging from 21% to 30%, or >30% from the target value, respectively. The controller performance was calculated using Varvel parameters. Critical respiratory and hemodynamic events were documented. The former was defined as peripheral oxygen saturation <92% and/or respiratory rate <8/min, whereas the latter was considered a mean arterial pressure <60 mm Hg and/or heart rate <40 bpm. RESULTS: Automated sedation was successful in 19 patients undergoing TAVI. One patient was excluded from the final analysis because of conversion to general anesthesia. The secondary observations revealed that the clinical performance allowed an excellent to good control during 69% (99% confidence interval, 53%-77%; interquartile range, 59%-79%) of the sedation time. Fifteen patients presented critical respiratory episodes, with a median of 3 events per hour of sedation. Six patients presented critical hemodynamic episodes, with a median of 2 events per hour of procedure. CONCLUSIONS: The automated closed-loop sedation system tested could be used successfully for patients scheduled for a TAVI procedure. The results showed a satisfactory clinical performance of sedation control.

A novel system for automated propofol sedation: hybrid sedation system (HSS).

Closed-loop systems for propofol have been demonstrated to be safe and reliable for general anesthesia. However, no study has been conducted using a closed-loop system specifically designed for sedation in patients under spinal anesthesia. We developed an automatic anesthesia sedation system that allows for closed-loop delivery of propofol for sedation integrating a decision support system, called the hybrid sedation system (HSS). The objective of this study is to compare this system with standard practice. One hundred fifty patients were enrolled and randomly assigned to two groups: HSS-Group (N = 75), in which propofol was administered using a closed-loop system; Control Group (N = 75), in which propofol was delivered manually. The clinical performance of the propofol sedation control is defined as efficacy to maintain bispectral index (BIS) near 65. The clinical control was called 'Excellent', 'Good', 'Poor' and 'Inadequate' with BIS values within 10 %, from 11 to 20 %, 21 to 30 %, or greater than 30 % of the BIS target of 65, respectively. The controller performance was evaluated using Varvel's parameters. Data are presented as mean ± standard deviation, groups were compared using t test or Chi square test, P < 0.05. Clinical performance of sedation showed 'Excellent' control in the HSS-group for a significantly longer period of time (49 vs. 26 % in the control group, P < 0.0001). 'Poor' and 'Inadequate' sedation was significantly shorter in the HSS Group compared to the Control Group (11 and 10 % vs. 20 and 18 %, respectively, P < 0.0001). The novel, closed-loop system for propofol sedation showed better maintenance of the target BIS value compared to manual administration.