Artificial Intelligence in Anesthesiology Current Techniques , Clinical Applications , and Limitations

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.

[1]  Yu-Chuan Li,et al.  Predicting hypotensive episodes during spinal anesthesia with the application of artificial neural networks , 2008, Comput. Methods Programs Biomed..

[2]  Application of Artificial Neural Networks as an Indicator of Awareness with Recall During General Anaesthesia , 2004, Journal of Clinical Monitoring and Computing.

[3]  Lucila Ohno-Machado,et al.  Natural language processing: an introduction , 2011, J. Am. Medical Informatics Assoc..

[4]  Robert A Veselis,et al.  Analytical methods to differentiate similar electroencephalographic spectra: neural network and discriminant analysis , 1993, Journal of Clinical Monitoring.

[5]  J. Gulcher,et al.  Profiling of genes expressed in peripheral blood mononuclear cells predicts glucocorticoid sensitivity in asthma patients. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[6]  J. Sigl,et al.  Anesthesia awareness and the bispectral index. , 2008, The New England journal of medicine.

[7]  Richard Ernest Bellman,et al.  An Introduction to Artificial Intelligence: Can Computers Think? , 1978 .

[8]  K. Borgwardt,et al.  Machine Learning in Medicine , 2015, Mach. Learn. under Resour. Constraints Vol. 3.

[9]  Ari Ercole,et al.  Cerebrovascular Signal Complexity Six Hours after Intensive Care Unit Admission Correlates with Outcome after Severe Traumatic Brain Injury. , 2016, Journal of neurotrauma.

[10]  S. Roberts,et al.  EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia. , 2002, British journal of anaesthesia.

[11]  Ari Ercole,et al.  Prediction of early unplanned intensive care unit readmission in a UK tertiary care hospital: a cross-sectional machine learning approach , 2017, BMJ Open.

[12]  Yu-Chuan Li,et al.  Neural network modeling to predict the hypnotic effect of propofol bolus induction , 2002, AMIA.

[13]  Purang Abolmaesumi,et al.  Automatic Localization of the Needle Target for Ultrasound-Guided Epidural Injections , 2018, IEEE Transactions on Medical Imaging.

[14]  Ritankar Das,et al.  Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial , 2017, BMJ Open Respiratory Research.

[15]  J. Sleigh,et al.  Permutation entropy of the electroencephalogram: a measure of anaesthetic drug effect. , 2008, British journal of anaesthesia.

[16]  Guy Rosman,et al.  Machine learning and coresets for automated real-time video segmentation of laparoscopic and robot-assisted surgery , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).

[17]  Jiann-Shing Shieh,et al.  Hierarchical Rule-based Monitoring and Fuzzy Logic Control for Neuromuscular Block , 2004, Journal of Clinical Monitoring and Computing.

[18]  Nassib G. Chamoun,et al.  Hospital Stay and Mortality Are Increased in Patients Having a “Triple Low” of Low Blood Pressure, Low Bispectral Index, and Low Minimum Alveolar Concentration of Volatile Anesthesia , 2012, Anesthesiology.

[19]  Zoubin Ghahramani,et al.  Probabilistic machine learning and artificial intelligence , 2015, Nature.

[20]  Hamid Behnam,et al.  Characterizing Awake and Anesthetized States Using a Dimensionality Reduction Method , 2015, Journal of Medical Systems.

[21]  M. Salto‐Tellez,et al.  Artificial intelligence—the third revolution in pathology , 2019, Histopathology.

[22]  Jing Wang,et al.  Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension , 2018, Anesthesiology.

[23]  A. Aldo Faisal,et al.  Towards efficient, personalized anesthesia using continuous reinforcement learning for propofol infusion control , 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).

[24]  M. Struys,et al.  Influence of Bayesian optimization on the performance of propofol target-controlled infusion , 2017, British Journal of Anaesthesia.

[25]  Jenny Lee,et al.  Fully Automated Deep Learning System for Bone Age Assessment , 2017, Journal of Digital Imaging.

[26]  Guy A. Dumont,et al.  Closed-Loop Control of Anesthesia: A Primer for Anesthesiologists , 2013, Anesthesia and analgesia.

[27]  Bruce G. Buchanan,et al.  A (Very) Brief History of Artificial Intelligence , 2005, AI Mag..

[28]  A F Merry,et al.  TADAA: Towards Automated Detection of Anaesthetic Activity. , 2011, Methods of information in medicine.

[29]  John Tromp,et al.  A Googolplex of Go Games , 2016, Computers and Games.

[30]  M. Paloheimo,et al.  Neural nets and prediction of the recovery rate from neuromuscular block , 2003, European journal of anaesthesiology.

[31]  S. Petersen-Felix,et al.  Fuzzy logic control of mechanical ventilation during anaesthesia. , 1996, British journal of anaesthesia.

[32]  Ari Ercole,et al.  Early Asymmetric Cardio-Cerebral Causality and Outcome after Severe Traumatic Brain Injury. , 2017, Journal of neurotrauma.

[33]  A. Absalom,et al.  Closed-loop Control of Anesthesia Using Bispectral Index: Performance Assessment in Patients Undergoing Major Orthopedic Surgery under Combined General and Regional Anesthesia , 2002, Anesthesiology.

[34]  S. Prasanna Devi,et al.  Prediction of Surgery Times and Scheduling of Operation Theaters in Optholmology Department , 2012, Journal of Medical Systems.

[35]  A M Drewes,et al.  Prediction of postoperative opioid analgesia using clinical‐experimental parameters and electroencephalography , 2017, European journal of pain.

[36]  S. Hoeks,et al.  What we can learn from Big Data about factors influencing perioperative outcome , 2018, Current opinion in anaesthesiology.

[37]  N. Shah,et al.  Implementing Machine Learning in Health Care - Addressing Ethical Challenges. , 2018, The New England journal of medicine.

[38]  Jiann-Shing Shieh,et al.  Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia. , 2006, Medical engineering & physics.

[39]  Stefan Mersmann,et al.  A knowledge- and model-based system for automated weaning from mechanical ventilation: technical description and first clinical application , 2014, Journal of Clinical Monitoring and Computing.

[40]  Ali Jalali,et al.  Advanced analytics for outcome prediction in intensive care units , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[41]  A Gottschalk,et al.  A Comparison of Human and Machine-based Predictions of Successful Weaning from Mechanical Ventilation , 2000, Medical decision making : an international journal of the Society for Medical Decision Making.

[42]  Erik Smistad,et al.  Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks , 2016, LABELS/DLMIA@MICCAI.

[43]  S. Shafer,et al.  Artificial Intelligence for Everyone. , 2017, Anesthesiology.

[44]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[45]  S. Eagleman,et al.  Calculations of consciousness: electroencephalography analyses to determine anesthetic depth , 2018, Current opinion in anaesthesiology.

[46]  Galit Zuckerman,et al.  Monitoring the nociception level: a multi-parameter approach , 2013, Journal of Clinical Monitoring and Computing.

[47]  David J. Stone,et al.  State of the art review: the data revolution in critical care , 2015, Critical Care.

[48]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[49]  Robert A. Veselis,et al.  Use of neural network analysis to classify electroencephalographic patterns against depth of midazolam sedation in intensive care unit patients , 1991, Journal of Clinical Monitoring.

[50]  K A Schulman,et al.  The effect of race and sex on physicians' recommendations for cardiac catheterization. , 1999, The New England journal of medicine.

[51]  Hung-Wen Chiu,et al.  Application of an Artificial Neural Network to Predict Postinduction Hypotension During General Anesthesia , 2011, Medical decision making : an international journal of the Society for Medical Decision Making.

[52]  K. Kalpakis,et al.  Predicting secondary insults after severe traumatic brain injury , 2015, The journal of trauma and acute care surgery.

[53]  J. Wanderer,et al.  Machine Learning for Anesthesiologists: A Primer. , 2018, Anesthesiology.

[54]  H. Berkenstadt,et al.  An evaluation of the Integrated Pulmonary Index (IPI) for the detection of respiratory events in sedated patients undergoing colonoscopy , 2012, Journal of Clinical Monitoring and Computing.

[55]  Steven H. Brown,et al.  Automated identification of postoperative complications within an electronic medical record using natural language processing. , 2011, JAMA.

[56]  D G Altman,et al.  Bayesians and frequentists , 1998, BMJ.

[57]  J. Rinehart,et al.  Machine-learning Algorithm to Predict Hypotension Based on High-fidelity Arterial Pressure Waveform Analysis , 2018, Anesthesiology.

[58]  Peter Norvig,et al.  Artificial intelligence - a modern approach, 2nd Edition , 2003, Prentice Hall series in artificial intelligence.

[59]  Mirza Mansoor Baig,et al.  Anaesthesia monitoring using fuzzy logic , 2011, Journal of Clinical Monitoring and Computing.

[60]  Harlan M Krumholz,et al.  Participation in cancer clinical trials: race-, sex-, and age-based disparities. , 2004, JAMA.

[61]  M. Dambrosio,et al.  Effect of age on the performance of bispectral and entropy indices during sevoflurane pediatric anesthesia: a pharmacometric study , 2017, Paediatric anaesthesia.

[62]  Demis Hassabis,et al.  A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play , 2018, Science.

[63]  B. Palanca,et al.  Association of Perioperative Risk Factors and Cumulative Duration of Low Bispectral Index with Intermediate-term Mortality after Cardiac Surgery in the B-Unaware Trial , 2010, Anesthesiology.

[64]  Azra Bihorac,et al.  Use of machine-learning classifiers to predict requests for preoperative acute pain service consultation. , 2012, Pain medicine.

[65]  Xiaowei Xu,et al.  Mining FDA drug labels using an unsupervised learning technique - topic modeling , 2011, BMC Bioinformatics.

[66]  Gulsah Hancerliogullari,et al.  The use of multi-criteria decision making models in evaluating anesthesia method options in circumcision surgery , 2017, BMC Medical Informatics and Decision Making.

[67]  G. Clermont,et al.  Predicting hospital mortality for patients in the intensive care unit: A comparison of artificial neural networks with logistic regression models , 2001, Critical care medicine.

[68]  Bradley A. Fritz,et al.  Intraoperative electroencephalogram suppression at lower volatile anaesthetic concentrations predicts postoperative delirium occurring in the intensive care unit , 2018, British journal of anaesthesia.

[69]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[70]  C.S. Nunes,et al.  Comparison of Neural Networks, Fuzzy and Stochastic Prediction Models for return of consciousness after general anesthesia , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[71]  Robert LIN,et al.  NOTE ON FUZZY SETS , 2014 .

[72]  T Bouillon,et al.  Model-based control of mechanical ventilation: design and clinical validation. , 2004, British journal of anaesthesia.

[73]  G. Mashour,et al.  Prevention of intraoperative awareness in a high-risk surgical population. , 2011, The New England journal of medicine.

[74]  Torsten B. Moeller,et al.  Books Received Pocket Atlas of Sectional Anatomy — Volume I : Head and Neck : 4th Edition Copyright © North American Neuro- O phthalmology Society . Unauthorized reproduction of this article is prohibited . , 2018 .

[75]  Hyunkwang Lee,et al.  Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability , 2018, Skeletal Radiology.

[76]  Yannis Manolopoulos,et al.  Data Mining techniques for the detection of fraudulent financial statements , 2007, Expert Syst. Appl..

[77]  José Luís Casteleiro-Roca,et al.  A Machine Learning Based System for Analgesic Drug Delivery , 2017, SOCO-CISIS-ICEUTE.

[78]  Khaled Mahar,et al.  Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach , 2017 .

[79]  I. Kohane,et al.  Finding the missing link for big biomedical data. , 2014, JAMA.

[80]  Yu-Chee Tseng,et al.  Decision tree-based learning to predict patient controlled analgesia consumption and readjustment , 2012, BMC Medical Informatics and Decision Making.

[81]  Reza Shalbaf,et al.  Monitoring the Depth of Anesthesia Using a New Adaptive Neurofuzzy System , 2018, IEEE Journal of Biomedical and Health Informatics.

[82]  E. A. Jasmin,et al.  Wavelet Entropy based classification of depth of anesthesia , 2016, 2016 International Conference on Computational Techniques in Information and Communication Technologies (ICCTICT).

[83]  Patrick L. Purdon,et al.  Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability* , 2017, Critical care medicine.

[85]  G. Rosman,et al.  Artificial Intelligence in Surgery: Promises and Perils , 2018, Annals of surgery.

[86]  Thomas M Hemmerling,et al.  The Feasibility of a Completely Automated Total IV Anesthesia Drug Delivery System for Cardiac Surgery , 2016, Anesthesia and analgesia.

[87]  Nadine Meskens,et al.  Using KDD process to forecast a duration of surgery , 2008 .

[88]  Daniel Fabbri,et al.  A System for Automated Determination of Perioperative Patient Acuity , 2018, Journal of Medical Systems.

[89]  D. Ruppert.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2004 .

[90]  Cyrus Motamed,et al.  Influence of real-time Bayesian forecasting of pharmacokinetic parameters on the precision of a rocuronium target-controlled infusion , 2012, European Journal of Clinical Pharmacology.

[91]  James H Thrall,et al.  Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success. , 2018, Journal of the American College of Radiology : JACR.

[92]  Purang Abolmaesumi,et al.  SLIDE: automatic spine level identification system using a deep convolutional neural network , 2017, International Journal of Computer Assisted Radiology and Surgery.

[93]  Rolf Unbehauen,et al.  Nonlinear Model-Based Predictive Control of Non-Depolarizing Muscle Relaxants using Neural Networks , 1999, Journal of Clinical Monitoring and Computing.

[94]  Robert Tibshirani,et al.  The Elements of Statistical Learning: Data Mining, Inference, and Prediction , 2001, Springer Series in Statistics.

[95]  A. Zbinden,et al.  Arterial pressure control with isoflurane using fuzzy logic. , 1995, British journal of anaesthesia.

[96]  W S McCulloch,et al.  A logical calculus of the ideas immanent in nervous activity , 1990, The Philosophy of Artificial Intelligence.

[97]  Uli K. Chettipally,et al.  Prediction of Sepsis in the Intensive Care Unit With Minimal Electronic Health Record Data: A Machine Learning Approach , 2016, JMIR medical informatics.