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2024, Number 4

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Rev Mex Anest 2024; 47 (4)

Artificial intelligence, the new tool in perioperative medicine and postoperative pain management

Verdugo-Velázquez FF, Hernández-Badillo LE, Reyes-Rojas JE, Garduño-López AL

Language: Spanish
References: 35
Page: 291-295
PDF size: 287.82 Kb.


ABSTRACT

Throughout history, science and technology have become allies in the area of healthcare. We are in a new era where the development of artificial intelligence (AI) and its application in medicine can improve the decision making of healthcare professionals to reduce risks, based on tools such as predictive algorithms or artificial neural networks. The application of artificial intelligence is part of both the present and the future of anesthesiology and perioperative medicine, being a useful tool for the anesthesiologist. This article focuses on the application of AI for the creation of algorithms, as well as its potential to revolutionize clinical practice in the management of post-surgical pain.


REFERENCES

  1. Maheshwari K, Cywinski JB, Papay F, Khanna AK, Mathur P. Artificial intelligence for perioperative medicine: perioperative intelligence. Anesth Analg. 2023;136:637-645. Available in: http://dx.doi.org/10.1213/ane.0000000000005952

  2. Cascella M, Tracey MC, Petrucci E, Bignami EG. Exploring artificial intelligence in anesthesia: a primer on ethics, and clinical applications. Surgeries (Basel). 2023;4:264-274. Available in: http://dx.doi.org/10.3390/surgeries4020027

  3. Lanzagorta-Ortega D, Carrillo-Pérez DL, Carrillo-Esper R. Inteligencia artificial en medicina: presente y futuro. Gac Med Mex. 2022;158:17-21. Available in: http://dx.doi.org/10.24875/gmm.m22000688

  4. Yoon H-K, Yang H-L, Jung C-W, Lee H-C. Artificial intelligence in perioperative medicine: a narrative review. Korean J Anesthesiol. 2022;75:202-215. Available in: http://dx.doi.org/10.4097/kja.22157

  5. 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;129:663-674. Available in: http://dx.doi.org/10.1097/aln.0000000000002300

  6. Lee J, Mawla I, Kim J, Loggia ML, Ortiz A, Jung C, et al. Machine Learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics. Pain. 2019;160:550-560. Available in: http://dx.doi.org/10.1097/j.pain.0000000000001417

  7. Misi? VV, Gabel E, Hofer I, Rajaram K, Mahajan A. Machine Learning prediction of postoperative emergency department hospital readmission. Anesthesiology. 2020;132:968-980. Available in: http://dx.doi.org/10.1097/aln.0000000000003140

  8. Wang Y, Zhu Y, Xue Q, Ji M, Tong J, Yang J-J, et al. Predicting chronic pain in postoperative breast cancer patients with multiple Machine Learning and Deep Learning models. J Clin Anesth. 2021;74:110423. Available in: http://dx.doi.org/10.1016/j.jclinane.2021.110423

  9. Hsiao F-J, Chen W-T, Pan L-LH, Liu H-Y, Wang Y-F, Chen S-P, et al. Machine Learning–based prediction of heat pain sensitivity by using resting-state EEG. Front Biosci (Landmark Ed). 2021;26:1537-1547. Available in: http://dx.doi.org/10.52586/5047

  10. Huang L, Chen X, Liu W, Shih P-C, Bao J. Automatic surgery and anesthesia emergence duration prediction using artificial neural networks. J Healthc Eng. 2022;2022:1-17. Available in: http://dx.doi.org/10.1155/2022/2921775

  11. Persson I, Grünwald A, Morvan L, Becedas D, Arlbrandt M. A Machine Learning algorithm predicting acute kidney injury in intensive care unit patients (NAVOY Acute Kidney Injury): proof-of-concept study. JMIR Form Res. 2023;7:e45979. Available in: http://dx.doi.org/10.2196/45979

  12. Bellini V, Valente M, Gaddi AV, Pelosi P, Bignami E. Artificial intelligence and telemedicine in anesthesia: potential and problems. Minerva Anestesiol. 2022;88:729-734. Available in: http://dx.doi.org/10.23736/s0375-9393.21.16241-8

  13. Bellini V, Valente M, Bertorelli G, Pifferi B, Craca M, Mordonini M, et al. Machine Learning in perioperative medicine: a systematic review. J Anesth Analg Crit Care. 2022;2:2. Available in: http://dx.doi.org/10.1186/s44158-022-00033-y

  14. Batko K, Slezak A. The use of big data analytics in healthcare. J Big Data. 2022;9:3. Available in: http://dx.doi.org/10.1186/s40537-021-00553-4

  15. Rothaug J, Zaslansky R, Schwenkglenks M, Komann M, Allvin R, Backström R, et al. Patients' perception of postoperative pain management: Validation of the international pain outcomes (IPO) questionnaire. J Pain. 2013;14:1361-1370. Available in: http://dx.doi.org/10.1016/j.jpain.2013.05.016

  16. Müller-Wirtz LM, Volk T. Big data in studying acute pain and regional anesthesia. J Clin Med. 2021;10:1425. Available in: https://doi.org/10.3390/jcm10071425

  17. Wall J, Dhesi J, Snowden C, Swart M. Perioperative medicine. Future Healthcare J. 2022;9:138-143. Available in: http://dx.doi.org/10.7861/fhj.2022-0051

  18. Gkikas S, Tsiknakis M. Automatic assessment of pain based on Deep Learning methods: a systematic review. Comput Methods Programs Biomed. 2023;231:107365. Available in: http://dx.doi.org/10.1016/j.cmpb.2023.107365

  19. Semwal A, Londhe ND. Automated pain severity detection using convolutional neural network. In: 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS). IEEE; 2018.

  20. Lee H-C, Ryu H-G, Chung E-J, Jung C-W. Prediction of bispectral index during target-controlled infusion of propofol and remifentanil. Anesthesiology. 2018;128:492-501. Available in: http://dx.doi.org/10.1097/aln.0000000000001892

  21. Salekin MS, Zamzmi G, Goldgof D, Kasturi R, Ho T, Sun Y. Multimodal spatio-temporal Deep Learning approach for neonatal postoperative pain assessment. Comput Biol Med. 2021;129:104150. Available in: http://dx.doi.org/10.1016/j.compbiomed.2020.104150

  22. Wang R, Xu K, Feng H, Chen W. Hybrid RNN-ANN based deep physiological network for pain recognition. In: 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2020.

  23. Zhi R, Zhou C, Yu J, Li T, Zamzmi G. Multimodal-based stream integrated neural networks for pain assessment. IEICE Trans Inf Syst. 2021;E104.D:2184-294. Available in: http://dx.doi.org/10.1587/transinf.2021edp7065

  24. Guan B, Liu F, Mizaian AH, Demehri S, Samsonov A, Guermazi A, et al. Deep Learning approach to predict pain progression in knee osteoarthritis. Skeletal Radiol. 2022;51:363-373. Available in: http://dx.doi.org/10.1007/s00256-021-03773-0

  25. Liu Y-L, Lin C-S, Cheng C-C, Lin C. A Deep Learning algorithm for detecting acute pericarditis by electrocardiogram. J Pers Med. 2022;12:1150. Available in: http://dx.doi.org/10.3390/jpm12071150

  26. Yoon H, Bak MS, Kim SH, Lee JH, Chung G, Kim SJ, et al. Development of a spontaneous pain indicator based on brain cellular calcium using Deep Learning. Exp Mol Med. 2022;54:1179-1187. Available in: http://dx.doi.org/10.1038/s12276-022-00828-7

  27. Fang J, Wu W, Liu J, Zhang S. Deep Learning–guided postoperative pain assessment in children. Pain. 2023;164:2029-2035. Disponible en: http://dx.doi.org/10.1097/j.pain.0000000000002900

  28. Fontaine D, Vielzeuf V, Genestier P, Limeux P, Santucci-Sivilotto S, Mory E, et al. Artificial intelligence to evaluate postoperative pain based on facial expression recognition. Eur J Pain. 2022;26:1282-1291. Available in: http://dx.doi.org/10.1002/ejp.1948

  29. Pinzon-Arenas JO, Kong Y, Chon KH, Posada-Quintero HF. Design and evaluation of Deep Learning models for continuous acute pain detection based on phasic electrodermal activity. IEEE J Biomed Health Inform. 2023;27:4250-4260. Available in: http://dx.doi.org/10.1109/jbhi.2023.3291955

  30. Melzack R, Katz J. Pain assessment in adult patients. In: McMahon SB, Koltzenburg M, Tracey I, Turk D, editors. Wall and Melzack textbook of pain. Elsevier Saunders; 2013. pp. 301-314.

  31. Apfelbaum JL, Chen C, Mehta SS, Gan TJ. Postoperative pain experience: results from a national survey suggest postoperative pain continues to be undermanaged. Anesth Analg. 2003;97:534-540. doi: 10.1213/01.Ane.0000068822.10113.9e

  32. Park I, Park JH, Yoon J, Song IA, Na HS, Ryu JH, Oh AY. Artificial intelligence model predicting postoperative pain using facial expressions: a pilot study. J Clin Monit Comput. 2024;38:261-270. doi: 10.1007/s10877-023-01100-7.

  33. Ekman P, Friesen WV. Measuring facial movement. Environ Psychol Nonverbal Behav. 1976;1:56-75. doi: 10.1007/BF01115465.

  34. Balavenkatasubramanian J, Kumar S, Sanjayan RD. Artificial intelligence in regional anaesthesia. Indian J Anaesth. 2024;68:100-104. Available in: http://dx.doi.org/10.4103/ija.ija_1274_23

  35. Bowness J, Varsou O, Turbitt L, Burkett-St Laurent D. Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound?guided regional anesthesia. Clin Anat. 2021;34:802-809. Available in: http://dx.doi.org/10.1002/ca.23742




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