CC BY 4.0 · J Neuroanaesth Crit Care
DOI: 10.1055/s-0044-1787844
Review Article

The Promise of Artificial Intelligence in Neuroanesthesia: An Update

Zhenrui Liao
1   Department of Neuroscience, Columbia University, New York, New York, United States
,
Niharika Mathur
2   School of Interactive Computing, College of Computing, Georgia Institute of Technology, Atlanta, Georgia, United States
,
Vidur Joshi
3   Department of Biomedical Engineering, Steven's Institute of Technology, Hoboken, New Jersey, United States
,
Shailendra Joshi
4   Department of Anesthesiology, Columbia University, New York, New York, United States
› Author Affiliations

Abstract

Artificial intelligence (AI) is poised to transform health care across medical specialties. Although the application of AI to neuroanesthesiology is just emerging, it will undoubtedly affect neuroanesthesiologists in foreseeable and unforeseeable ways, with potential roles in preoperative patient assessment, airway assessment, predicting intraoperative complications, and monitoring and interpreting vital signs. It will advance the diagnosis and treatment of neurological diseases due to improved risk identification, data integration, early diagnosis, image analysis, and pharmacological and surgical robotic assistance. Beyond direct medical care, AI could also automate many routine administrative tasks in health care, assist with teaching and training, and profoundly impact neuroscience research. This article introduces AI and its various approaches from a neuroanesthesiology perspective. A basic understanding of the computational underpinnings, advantages, limitations, and ethical implications is necessary for using AI tools in clinical practice and research. The update summarizes recent reports of AI applications relevant to neuroanesthesiology. Providing a holistic view of AI applications, this review shows how AI could usher in a new era in the specialty, significantly improving patient care and advancing neuroanesthesiology research.



Publication History

Article published online:
06 August 2024

© 2024. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/)

Thieme Medical and Scientific Publishers Pvt. Ltd.
A-12, 2nd Floor, Sector 2, Noida-201301 UP, India

 
  • References

  • 1 Chalmers DJ. The singularity: a philosophical review. J Conscious Stud 2010; 17: 7-65
  • 2 Garg PK. Chapter 1: Overview of artificial intelligence. In: Sharma L, Garg PK. eds. Artificial Intelligence Technologies, Applications, and Challenges. Boca Raton, FL:: CRC Press, Taylor & Francis Group;; 2022: 3-18
  • 3 McCarthy J. What is Artificial Intelligence. Accessed 6 June 2024 at: http://www-formal.stanford.edu/jmc/
  • 4 Asan O, Bayrak AE, Choudhury A. Artificial intelligence and human trust in healthcare: focus on clinicians. J Med Internet Res 2020; 22 (06) e15154
  • 5 Hazarika I. Artificial intelligence: opportunities and implications for the health workforce. Int Health 2020; 12 (04) 241-245
  • 6 Feinstein M, Katz D, Demaria S, Hofer IS. Remote monitoring and artificial intelligence: outlook for 2050. Anesth Analg 2024; 138 (02) 350-357
  • 7 Wu YH. Huang KY, Tseng AC. Development of an artificial intelligence-based image recognition system for time-sequence analysis of tracheal intubation. Anesth Analg 2024; (e-pub ahead of print).
  • 8 Fritz BA, Pugazenthi S, Budelier TP. et al. User-centered design of a machine learning dashboard for prediction of postoperative complications. Anesth Analg 2024; 138 (04) 804-813
  • 9 Nathan N. Robotics and the future of anesthesia. Anesth Analg 2024; 138 (02) 238
  • 10 Blacker SN, Kang M, Chakraborty I. et al. Utilizing artificial intelligence and chat generative pretrained transformer to answer questions about clinical scenarios in neuroanesthesiology. J Neurosurg Anesthesiol 2023; (e-pub ahead of print).
  • 11 Rajagopalan V, Kulkarni DK. Artificial intelligence in neuroanesthesiology and neurocritical care. J Neuroanesthesiology Critical Care 2020; 7: 11-18
  • 12 Chae D. Data science and machine learning in anesthesiology. Korean J Anesthesiol 2020; 73 (04) 285-295
  • 13 Anonymous. Types of Neural Networks and Definition of Neural Networks. Updated 23 Nov 2022 . Accessed 6 June 2024 at: https://www.mygreatlearning.com/blog/types-of-neural-networks/
  • 14 Logunova I. Backpropagation in Neural Networks. Updated 18 Dec 2023 . Accessed 6 June 2024 at: https://serokell.io/blog/understanding-backpropagation
  • 15 Bishop CM. Pattern Recognition and Machine Learning. New York, NY:: Springer Inc.;; 2006
  • 16 Hastie T, Tibshirani R, Friedman JH. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Vol 2. New York, NY:: Springer Inc.;; 2009
  • 17 Goodfellow IJ, Bengio Ya, Courville A. Deep Learning. Cambridge, MA:: MIT Press;; 2016
  • 18 Anonymous. Machine learning techniques. Accessed 6 June 2024 at: https://www.javatpoint.com/machine-learning-techniques
  • 19 Spatharou A, Solveigh H, Jenkins J. Transforming healthcare with AI: The impact on the workforce and organisations. McKinsey & Company. 2020 . Accessed 6 June 2024 at: https://www.mckinsey.com/industries/healthcare/our-insights/transforming-healthcare-with-ai
  • 20 Blobel B, Ruotsalainen P, Brochhausen M, Prestes E, Houghtaling MA. Designing and managing advanced, intelligent and ethical health and social care ecosystems. J Pers Med 2023; 13 (08) 1209
  • 21 Özdemir V. Digital is political: why we need a feminist conceptual lens on determinants of digital health. OMICS 2021; 25 (04) 249-254
  • 22 Hernandez-Boussard T, Siddique SM, Bierman AS, Hightower M, Burstin H. Promoting equity in clinical decision making: dismantling race-based medicine. Health Aff (Millwood) 2023; 42 (10) 1369-1373
  • 23 Davenport TH, Glaser JP. Factors governing the adoption of artificial intelligence in healthcare providers. Discov Health Syst 2022; 1 (01) 4
  • 24 Seah J, Boeken T, Sapoval M, Goh GS. Prime time for artificial intelligence in interventional radiology. Cardiovasc Intervent Radiol 2022; 45 (03) 283-289
  • 25 Henssen D, Meijer F, Verburg FA, Smits M. Challenges and opportunities for advanced neuroimaging of glioblastoma. Br J Radiol 2023; 96 (1141) 20211232
  • 26 de Godoy LL, Chawla S, Brem S, Mohan S. Taming glioblastoma in “real time”: integrating multimodal advanced neuroimaging/AI tools towards creating a robust and therapy agnostic model for response assessment in neuro-oncology. Clin Cancer Res 2023; 29 (14) 2588-2592
  • 27 Asif S, Zhao M, Chen X, Zhu Y. BMRI-NET: a deep stacked ensemble model for multi-class brain tumor classification from MRI images. Interdiscip Sci 2023; 15 (03) 499-514
  • 28 Aggarwal K, Manso Jimeno M, Ravi KS, Gonzalez G, Geethanath S. Developing and deploying deep learning models in brain magnetic resonance imaging: a review. NMR Biomed 2023; 36 (12) e5014
  • 29 Vinny PW, Vishnu VY, Padma Srivastava MV. Artificial intelligence shaping the future of neurology practice. Med J Armed Forces India 2021; 77 (03) 276-282
  • 30 Davey Z, Gupta PB, Li DR, Nayak RU, Govindarajan P. Rapid response EEG: current state and future directions. Curr Neurol Neurosci Rep 2022; 22 (12) 839-846
  • 31 Rabinowitch I. What would a synthetic connectome look like?. Phys Life Rev 2020; 33: 1-15
  • 32 Harms MP, Somerville LH, Ances BM. et al. Extending the human connectome project across ages: imaging protocols for the lifespan development and aging projects. Neuroimage 2018; 183: 972-984
  • 33 Sun L, Zhao T, Liang X. et al. Functional connectome through the human life span. bioRxiv . Sep 19 2023;
  • 34 Corrias G, Mazzotta A, Melis M. et al. Emerging role of artificial intelligence in stroke imaging. Expert Rev Neurother 2021; 21 (07) 745-754
  • 35 Raghavendra U, Acharya UR, Adeli H. Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur Neurol 2019; 82 (1–3): 41-64
  • 36 Hakeem H, Feng W, Chen Z. et al. Development and validation of a deep learning model for predicting treatment response in patients with newly diagnosed epilepsy. JAMA Neurol 2022; 79 (10) 986-996
  • 37 Cortés-Ferre L, Gutiérrez-Naranjo MA, Egea-Guerrero JJ, Pérez-Sánchez S, Balcerzyk M. Deep learning applied to intracranial hemorrhage detection. J Imaging 2023; 9 (02) 37
  • 38 Warman R, Warman A, Warman P. et al. Deep learning system boosts radiologist detection of intracranial hemorrhage. Cureus 2022; 14 (10) e30264
  • 39 Surianarayanan C, Lawrence JJ, Chelliah PR, Prakash E, Hewage C. Convergence of artificial intelligence and neuroscience towards the diagnosis of neurological disorders-a scoping review. Sensors (Basel) 2023; 23 (06) 3062
  • 40 Luo J, Pan M, Mo K, Mao Y, Zou D. Emerging role of artificial intelligence in diagnosis, classification and clinical management of glioma. Semin Cancer Biol 2023; 91: 110-123
  • 41 Song B, Zhou M, Zhu J. Necessity and importance of developing AI in anesthesia from the perspective of clinical safety and information security. Med Sci Monit 2023; 29: e938835
  • 42 Lopes S, Rocha G, Guimaraes-Pereira L. Artificial intelligence and its clinical application in Anesthesiology: a systematic review. J Clin Monit Comput 2024; 38 (02) 247-259
  • 43 Singh M, Nath G. Artificial intelligence and anesthesia: a narrative review. Saudi J Anaesth 2022; 16 (01) 86-93
  • 44 Yoon HK, Yang HL, Jung CW, Lee HC. Artificial intelligence in perioperative medicine: a narrative review. Korean J Anesthesiol 2022; 75 (03) 202-215
  • 45 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
  • 46 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 (04) 619-622
  • 47 Hashimoto DA, Witkowski E, Gao L, Meireles O, Rosman G. Artificial intelligence in anesthesiology: current techniques, clinical applications, and limitations. Anesthesiology 2020; 132 (02) 379-394
  • 48 Lee HC, Ryu HG, Chung EJ, Jung CW. Prediction of bispectral index during target-controlled infusion of propofol and remifentanil: a deep learning approach. Anesthesiology 2018; 128 (03) 492-501
  • 49 Lin Q, Chng C-B, Too J. et al. Towards artificial intelligence-enabled medical pre-operative airway assessment. Paper presented at: 2022 IEEE International Conference on E-health Networking, Application & Services (HealthCom); 17–19 October 2022, Genoa, Italy
  • 50 Hayasaka T, Kawano K, Kurihara K, Suzuki H, Nakane M, Kawamae K. Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study. J Intensive Care 2021; 9 (01) 38
  • 51 Park JB, Lee HJ, Yang HL. et al. Machine learning-based prediction of intraoperative hypoxemia for pediatric patients. PLoS One 2023; 18 (03) e0282303
  • 52 Kendale S, Kulkarni P, Rosenberg AD, Wang J. Supervised machine-learning predictive analytics for prediction of postinduction hypotension. Anesthesiology 2018; 129 (04) 675-688
  • 53 Lee S, Lee M, Kim SH, Woo J. Intraoperative hypotension prediction model based on systematic feature engineering and machine learning. Sensors (Basel) 2022; 22 (09) 3108
  • 54 Maciąg TT, van Amsterdam K, Ballast A, Cnossen F, Struys MM. Machine learning in anesthesiology: detecting adverse events in clinical practice. Health Informatics J 2022; 28 (03) 14 604582221112855
  • 55 Bellini V, Valente M, Gaddi AV, Pelosi P, Bignami E. Artificial intelligence and telemedicine in anesthesia: potential and problems. Minerva Anestesiol 2022; 88 (09) 729-734
  • 56 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 (04) 649-662
  • 57 Angel MC, Rinehart JB, Canneson MP, Baldi P. Clinical knowledge and reasoning abilities of AI large language models in anesthesiology: a comparative study on the ABA exam. medRxiv . May 16 2023;
  • 58 Roy S, Kiral I, Mirmomeni M. et al; IBM Epilepsy Consortium. Evaluation of artificial intelligence systems for assisting neurologists with fast and accurate annotations of scalp electroencephalography data. EBioMedicine 2021; 66: 103275
  • 59 Li R, Wu Q, Liu J, Wu Q, Li C, Zhao Q. Monitoring depth of anesthesia based on hybrid features and recurrent neural network. Front Neurosci 2020; 14: 26
  • 60 Wang Y, Zhang H, Fan Y. et al. Propofol anesthesia depth monitoring based on self-attention and residual structure convolutional neural network. Comput Math Methods Med 2022; 2022: 8501948
  • 61 Nsugbe E, Connelly S, Mutanga I. Towards an affordable means of surgical depth of anesthesia monitoring: an EMG-ECG-EEG case study. BioMedInformatics 2023; 3: 769-790
  • 62 Wang G, Li C, Tang F. et al. A fully-automatic semi-supervised deep learning model for difficult airway assessment. Heliyon 2023; 9 (05) e15629
  • 63 Yamanaka S, Goto T, Morikawa K. et al. Machine learning approaches for predicting difficult airway and first-pass success in the emergency department: multicenter prospective observational study. Interact J Med Res 2022; 11 (01) e28366
  • 64 Khan MJ, Karmakar A. Emerging robotic innovations and artificial intelligence in endotracheal intubation and airway management: current state of the art. Cureus 2023; 15 (07) e42625
  • 65 Wang X, Tao Y, Tao X. et al. An original design of remote robot-assisted intubation system. Sci Rep 2018; 8 (01) 13403
  • 66 Biro P, Hofmann P, Gage D. et al. Automated tracheal intubation in an airway manikin using a robotic endoscope: a proof of concept study. Anaesthesia 2020; 75 (07) 881-886
  • 67 Brown MS, Wong KP, Shrestha L. et al. Automated endotracheal tube placement check using semantically embedded deep neural networks. Acad Radiol 2023; 30 (03) 412-420
  • 68 Lundberg SM, Nair B, Vavilala MS. et al. Explainable machine-learning predictions for the prevention of hypoxaemia during surgery. Nat Biomed Eng 2018; 2 (10) 749-760
  • 69 Hatib F, Jian Z, Buddi S. et al. Machine-learning algorithm to predict hypotension based on high-fidelity arterial pressure waveform analysis. Anesthesiology 2018; 129 (04) 663-674
  • 70 van der Ven WH, Veelo DP, Wijnberge M, van der Ster BJP, Vlaar APJ, Geerts BF. One of the first validations of an artificial intelligence algorithm for clinical use: the impact on intraoperative hypotension prediction and clinical decision-making. Surgery 2021; 169 (06) 1300-1303
  • 71 Lavecchia A. Deep learning in drug discovery: opportunities, challenges and future prospects. Drug Discov Today 2019; 24 (10) 2017-2032
  • 72 Stephenson N, Shane E, Chase J. et al. Survey of machine learning techniques in drug discovery. Curr Drug Metab 2019; 20 (03) 185-193
  • 73 Carracedo-Reboredo P, Liñares-Blanco J, Rodríguez-Fernández N. et al. A review on machine learning approaches and trends in drug discovery. Comput Struct Biotechnol J 2021; 19: 4538-4558
  • 74 de Oliveira ECL, da Costa KS, Taube PS, Lima AH, Junior CSS. Biological membrane-penetrating peptides: computational prediction and applications. Front Cell Infect Microbiol 2022; 12: 838259
  • 75 Sharma S, Borski C, Hanson J. et al. Identifying an optimal neuroinflammation treatment using a nanoligomer discovery engine. ACS Chem Neurosci 2022; 13 (23) 3247-3256
  • 76 Gupta S, Basant N, Singh KP. Qualitative and quantitative structure-activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals. SAR QSAR Environ Res 2015; 26 (02) 95-124
  • 77 Getz K, Smith Z, Shafner L, Hanina A. Assessing the scope and predictors of intentional dose non-adherence in clinical trials. Ther Innov Regul Sci 2020; 54 (06) 1330-1338
  • 78 Beig N, Bera K, Tiwari P. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges. Neurooncol Adv 2021; 2 (Suppl. 04) iv3-iv14
  • 79 McCague C, Ramlee S, Reinius M. et al. Introduction to radiomics for a clinical audience. Clin Radiol 2023; 78 (02) 83-98
  • 80 Dragoș HM, Stan A, Pintican R. et al. MRI radiomics and predictive models in assessing ischemic stroke outcome-a systematic review. Diagnostics (Basel) 2023; 13 (05) 857
  • 81 Ramos LA, van Os H, Hilbert A. et al. Combination of radiological and clinical baseline data for outcome prediction of patients with an acute ischemic stroke. Front Neurol 2022; 13: 809343
  • 82 Mammadov O, Akkurt BH, Musigmann M. et al. Radiomics for pseudoprogression prediction in high grade gliomas: added value of MR contrast agent. Heliyon 2022; 8 (08) e10023
  • 83 Sakly H, Said M, Seekins J, Guetari R, Kraiem N, Marzougui M. Brain tumor radiogenomic classification of O6-methylguanine-DNA methyltransferase promoter methylation in malignant gliomas-based transfer learning. Cancer Contr 2023; 30: 10 732748231169149
  • 84 Åkerlund CAI, Holst A, Stocchetti N. et al; CENTER-TBI Participants and Investigators. Clustering identifies endotypes of traumatic brain injury in an intensive care cohort: a CENTER-TBI study. Crit Care 2022; 26 (01) 228
  • 85 Brossard C, Grèze J, de Busschère JA. et al. Prediction of therapeutic intensity level from automatic multiclass segmentation of traumatic brain injury lesions on CT-scans. Sci Rep 2023; 13 (01) 20155
  • 86 Pease M, Arefan D, Barber J. et al; TRACK-TBI Investigators. Outcome prediction in patients with severe traumatic brain injury using deep learning from head CT scans. Radiology 2022; 304 (02) 385-394
  • 87 Luo X, Lin D, Xia S. et al. Machine learning classification of mild traumatic brain injury using whole-brain functional activity: a radiomics analysis. Dis Markers 2021; 2021: 3015238
  • 88 Huang L, Chen X, Liu W, Shih PC, Bao J. Automatic surgery and anesthesia emergence duration prediction using artificial neural networks. J Healthc Eng 2022; 2022: 2921775
  • 89 Hetherington J, Lessoway V, Gunka V, Abolmaesumi P, Rohling R. SLIDE: automatic spine level identification system using a deep convolutional neural network. Int J CARS 2017; 12 (07) 1189-1198
  • 90 Miyaguchi N, Takeuchi K, Kashima H, Morita M, Morimatsu H. Predicting anesthetic infusion events using machine learning. Sci Rep 2021; 11 (01) 23648
  • 91 Enarvi S, Amoia S, Del-Agua Teba M. et al. Generating Medical Reports from Patient-Doctor Conversations using Sequence-to-Sequence Models. Associatio n for Computational Linguistics; 2020: 22-30
  • 92 Jiao Y, Xue B, Lu C, Avidan MS, Kannampallil T. Continuous real-time prediction of surgical case duration using a modular artificial neural network. Br J Anaesth 2022; 128 (05) 829-837
  • 93 He Y, Peng S, Chen M, Yang Z, Chen Y. A transformer-based prediction method for depth of anesthesia during target-controlled infusion of propofol and remifentanil. IEEE Trans Neural Syst Rehabil Eng 2023; 31: 3363-3374
  • 94 Dresp B, Liu R, Wandeto J. Surgical task expertise detected by a self-organizing neural network map. 2021
  • 95 Cascella M, Scarpati G, Bignami EG. et al. Utilizing an artificial intelligence framework (conditional generative adversarial network) to enhance telemedicine strategies for cancer pain management. J Anesth Analg Crit Care 2023; 3 (01) 19