ABSTRACT
Artificial intelligence is defined as the study of algorithms that give machines the ability to reason and perform functions such as problem solving, object and word recognition, inference of world situations and decision making, and it has been shown to be usable in many fields of medicine. While the main task of anesthesiology is the patient’s clinic, processing lots of data also needs to be concerned. This improves the coexistence of artificial intelligence and anesthesia day by day.
Keywords: Artificial Intelligence, Anesthesia, Depth Of Anesthesia
References
1
Bellman R. An introduction to artificial intelligence: Can computers think? San Francisco, Boyd & Fraser Pub Co, 1978.
2
Sutton RS, Barto AG: Reinforcement Learning: An Introduction, MIT press Cambridge, 1998.
3
Baig MM, Gholamhosseini H, Kouzani A, et al. Anaesthesia monitoring using fuzzy logic. J Clin Monit Comput. 2011;25:339-347.
4
Fritz BA, Maybrier HR, Avidan MS. Intraoperative electroencephalogram suppression at lower volatile anaesthetic concentrations predicts postoperative delirium occurring in the intensive care unit. Br J Anaesth. 2018;121:241-248.
5
Kertai MD, Pal N, Palanca BJ, 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. Anesthesiology. 2010;112:1116-11127.
6
Sessler DI, Sigl JC, Kelley SD, 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. Anesthesiology. 2012;116:1195-1203.
7
Ortolani O, Conti A, Di Filippo A, et al. EEG signal processing in anaesthesia. Use of a neural network technique for monitoring depth of anaesthesia. Br J Anaesth. 2002;88:644-648.
8
Nagaraj SB, Biswal S, Boyle EJ, et al. Patient-Specific Classification of ICU Sedation Levels From Heart Rate Variability. Crit Care Med. 2017;45:e683-e690.
9
Dumont GA, Ansermino JM. Closed-loop control of anesthesia: a primer for anesthesiologists. Anesth Analg. 2013;117:1130-1138.
10
Shieh JS, Kao MH, et al. Genetic fuzzy modelling and control of bispectral index (BIS) for general intravenous anaesthesia. Med Eng Phys. 2006;28:134-148.
11
Shieh JS, Fan SZ, Chang LW, et al. Hierarchical rule-based monitoring and fuzzy logic control for neuromuscular block. J Clin Monit Comput. 2000;16:583-592.
12
Lin CS, Li YC, Mok MS, et al. Neural network modeling to predict the hypnotic effect of propofol bolus induction. Proc AMIA Symp. 2002:450-3.
13
Shimabukuro DW, Barton CW, Feldman MD, et al. Effect of a machine learning-based severe sepsis prediction algorithm on patient survival and hospital length of stay: a randomised clinical trial. BMJ Open Respir Res. 2017;4:e000234.
14
Smistad E, Lovstakken L, Carneiro G, et al: Vessel Detection in Ultrasound Images Using Deep Convolutional Neural Networks, Med Image Comput Comput Assist Inter, Springer, 2016. p. 30-8.
15
Ben-Israel N, Kliger M, Zuckerman G, et al. Monitoring the nociception level: a multi-parameter approach. J Clin Monit Comput. 2013;27:659-668.
16
Combes C, Meskens N, Rivat C, et al: Using a KDD process to forecast the duration of surgery. Int J Prod Econ. 2008;112:279-293
17
Liem VGB, Hoeks SE, van Lier F, et al. What we can learn from Big Data about factors influencing perioperative outcome. Curr Opin Anaesthesiol. 2018;31:723-731.
18
Hashimoto DA, Witkowski E, Gao L, et al. Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations. Anesthesiology. 2020;132:379-394.
