DOI QR코드

DOI QR Code

Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future

  • Kyung Ah Kim (Department of Brain and Cognitive Engineering, Korea University) ;
  • Hakseung Kim (Department of Brain and Cognitive Engineering, Korea University) ;
  • Eun Jin Ha (Department of Critical Care Medicine, Seoul National University Hospital) ;
  • Byung C. Yoon (Department of Radiology, Stanford University School of Medicine, VA Palo Alto Heath Care System) ;
  • Dong-Joo Kim (Department of Brain and Cognitive Engineering, Korea University)
  • 투고 : 2023.09.06
  • 심사 : 2024.01.04
  • 발행 : 2024.09.01

초록

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

키워드

과제정보

This work was supported by a National Research Foundation of Korea (NRF) Grant funded by the Korean government (Ministry of Science and ICT, MSIT) (No. 2022R1A2C1013205).

참고문헌

  1. Abe D, Inaji M, Hase T, Takahashi S, Sakai R, Ayabe F, et al. : A prehospital triage system to detect traumatic intracranial hemorrhage using machine learning algorithms. JAMA Netw Open 5 : e2216393, 2022
  2. Abujaber A, Fadlalla A, Gammoh D, Abdelrahman H, Mollazehi M, ElMenyar A : Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: machine learning approach. PLoS one 15 : e0235231, 2020
  3. Al-Mufti F, Smith B, Lander M, Damodara N, Nuoman R, El-Ghanem M, et al. : Novel minimally invasive multi-modality monitoring modalities in neurocritical care. J Neurol Sci 390 : 184-192, 2018
  4. Athaya T, Choi S : Evaluation of different machine learning models for photoplethysmogram signal artifact detection. 2020 International conference on information and communication technology convergence (ICTC); 2020 Oct 21-23; Jeju, Korea. New York : IEEE, c2020, pp1206-1208
  5. Au-Yeung WM, Sahani AK, Isselbacher EM, Armoundas AA : Reduction of false alarms in the intensive care unit using an optimized machine learning based approach. NPJ Digit Med 2 : 86, 2019
  6. Awaysheh A, Wilcke J, Elvinger F, Rees L, Fan W, Zimmerman KL : Review of medical decision support and machine-learning methods. Vet Pathol 56 : 512-525, 2019
  7. Azabou E, Navarro V, Kubis N, Gavaret M, Heming N, Cariou A, et al. : Value and mechanisms of EEG reactivity in the prognosis of patients with impaired consciousness: a systematic review. Crit Care 22 : 184, 2018
  8. Backhaus S : Traumatic Brain Injury (TBI) in Kreutzer JS, DeLuca J, Caplan B (eds) : Encyclopedia of Clinical Neuropsychology. New York : Springer New York, 2011, pp2550-2554
  9. Bakator M, Radosav D : Deep learning and medical diagnosis: a review of literature. Multimodal Technol Interact 2 : 47, 2018
  10. Beqiri E, Smielewski P, Robba C, Czosnyka M, Cabeleira MT, Tas J, et al. : Feasibility of individualised severe traumatic brain injury management using an automated assessment of optimal cerebral perfusion pressure: the COGiTATE phase II study protocol. BMJ Open 9 : e030727, 2019
  11. Bhavsar KA, Singla J, Al-Otaibi YD, Song OY, Zikria YB, Bashir AK : Medical diagnosis using machine learning: a statistical review. Comput Mater Contin 67 : 107-125, 2021
  12. Bonds BW, Yang S, Hu PF, Kalpakis K, Stansbury LG, Scalea TM, et al. : Predicting secondary insults after severe traumatic brain injury. J Trauma Acute Care Surg 79 : 85-90, 2015
  13. Briganti G : A clinician's guide to large language models. Future Medicine AI 1 : FMAI1, 2023
  14. Brossard C, Lemasson B, Attye A, De Busschere JA, Payen JF, Barbier EL, et al. : Contribution of CT-scan analysis by artificial intelligence to the clinical care of TBI patients. Front Neurol 12 : 666875, 2021
  15. Burgess S, Abu-Laban RB, Slavik RS, Vu EN, Zed PJ : A systematic review of randomized controlled trials comparing hypertonic sodium solutions and mannitol for traumatic brain injury: implications for emergency department management. Ann Pharmacother 50 : 291-300, 2016
  16. Carra G, Guiza F, Depreitere B, Meyfroidt G; CENTER-TBI High-Resolution ICU (HR ICU) Sub-Study Participants and Investigators : Prediction model for intracranial hypertension demonstrates robust performance during external validation on the CENTER-TBI dataset. Intensive Care Med 47 : 124-126, 2021
  17. Chen JW, Gombart ZJ, Rogers S, Gardiner SK, Cecil S, Bullock RM : Pupillary reactivity as an early indicator of increased intracranial pressure: the introduction of the Neurological Pupil index. Surg Neurol Int 2 : 82, 2011
  18. Chesnut RM, Temkin N, Carney N, Dikmen S, Rondina C, Videtta W, et al. : A trial of intracranial-pressure monitoring in traumatic brain injury. N Engl J Med 367 : 2471-2481, 2012
  19. Choi Y, Park JH, Hong KJ, Ro YS, Song KJ, Shin SD : Development and validation of a prehospital-stage prediction tool for traumatic brain injury: a multicentre retrospective cohort study in Korea. BMJ Open 12 : e055918, 2022
  20. Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, et al. : The 'Digital Twin' to enable the vision of precision cardiology. Eur Heart J 41 : 4556-4564, 2020
  21. Croatti A, Gabellini M, Montagna S, Ricci A : On the integration of agents and digital twins in healthcare. J Med Syst 44 : 161, 2020
  22. Cui W, Ge S, Shi Y, Wu X, Luo J, Lui H, et al. : Death after discharge: prognostic model of 1-year mortality in traumatic brain injury patients undergoing decompressive craniectomy. Chin Neurosurg J 7 : 24, 2021
  23. Cvach M : Monitor alarm fatigue: an integrative review. Biomed Instrum Technol 46 : 268-277, 2012
  24. DeJournett L, DeJournett J : In silico testing of an artificial-intelligencebased artificial pancreas designed for use in the intensive care unit setting. J Diabetes Sci Technol 10 : 1360-1371, 2016
  25. Drew BJ, Harris P, Zegre-Hemsey JK, Mammone T, Schindler D, Salas-Boni R, et al. : Insights into the problem of alarm fatigue with physiologic monitor devices: a comprehensive observational study of consecutive intensive care unit patients. PloS One 9 : e110274, 2014
  26. Dundar TT, Yurtsever I, Pehlivanoglu MK, Yildiz U, Eker A, Demir MA, et al. : Machine learning-based surgical planning for neurosurgery: artificial intelligent approaches to the cranium. Front Surg 9 : 863633, 2022
  27. Eddy DM, Schlessinger L : Validation of the Archimedes diabetes model. Diabetes Care 26 : 3102-3110, 2003
  28. Ellethy H, Chandra SS, Nasrallah FA : The detection of mild traumatic brain injury in paediatrics using artificial neural networks. Comput Biol Med 135 : 104614, 2021
  29. Emami H, Dong M, Nejad-Davarani SP, Glide-Hurst CK : Generating synthetic CTs from magnetic resonance images using generative adversarial networks. Med Phys 45 : 3627-3636, 2018
  30. Erol T, Mendi AF, Dogan D : The digital twin revolution in healthcare. 2020 4th international symposium on multidisciplinary studies and innovative technologies (ISMSIT); 2020 Oct 22-24; Istanbul, Turkey. New York : IEEE, c2020, pp1-7
  31. Evensen KB, Eide PK : Measuring intracranial pressure by invasive, less invasive or non-invasive means: limitations and avenues for improvement. Fluids Barriers CNS 17 : 34, 2020
  32. Farzaneh N, Williamson CA, Gryak J, Najarian K : A hierarchical expert-guided machine learning framework for clinical decision support systems: an application to traumatic brain injury prognostication. NPJ Digit Med 4 : 78, 2021
  33. Garg A, Mago V : Role of machine learning in medical research: a survey. Compu Sci Rev 40 : 100370, 2021
  34. Ghajar J : Traumatic brain injury. Lancet 356 : 923-929, 2000
  35. Glaser J, Vasquez M, Cardarelli C, Galvagno S Jr, Stein D, Murthi S, et al. : Through the looking glass: early non-invasive imaging in TBI predicts the need for interventions. Trauma Surg Acute Care Open 1 : e000019, 2016
  36. Gong EJ, Bang CS : Interpretation of medical images using artificial intelligence: current status and future perspectives. Korean J Gastroenterol 82 : 43-45, 2023
  37. Greenfield D : Artificial intelligence in medicine: applications, implications and limitations. Available at : https://sitn.hms.harvard.edu/flash/2019/artificial-intelligence-in-medicine-applications-implications-and-limitations/
  38. Guiza F, Depreitere B, Piper I, Van den Berghe G, Meyfroidt G : Novel methods to predict increased intracranial pressure during intensive care and long-term neurologic outcome after traumatic brain injury: development and validation in a multicenter dataset. Crit Care Med 41 : 554-564, 2013
  39. Guler I, Gokcil Z, Gulbandilar E : Evaluating of traumatic brain injuries using artificial neural networks. Expert Syst Appl 36 : 10424-10427, 2009
  40. Habibzadeh A, Khademolhosseini S, Kouhpayeh A, Niakan A, Asadi MA, Ghasemi H, et al. : Machine learning-based models to predict the need for neurosurgical intervention after moderate traumatic brain injury. Health Sci Rep 6 : e1666, 2023
  41. Hale AT, Stonko DP, Lim J, Guillamondegui OD, Shannon CN, Patel MB : Using an artificial neural network to predict traumatic brain injury. J Neurosurg Pediatr 23 : 219-226, 2018
  42. Hanko M, Grendar M, Snopko P, Opsenak R, Sutovsky J, Benco M, et al. : Random forest-based prediction of outcome and mortality in patients with traumatic brain injury undergoing primary decompressive craniectomy. World Neurosurg 148 : e450-e458, 2021
  43. Haveman ME, Van Putten MJAM, Hom HW, Eertman-Meyer CJ, Beishuizen A, Tjepkema-Cloostermans MC : Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography. Crit Care 23 : 401, 2019
  44. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL : Artificial intelligence in radiology. Nat Rev Cancer 18 : 500-510, 2018
  45. Hsu YC, Weng HH, Kuo CY, Chu TP, Tsai YH : Prediction of fall events during admission using eXtreme gradient boosting: a comparative validation study. Sci Rep 10 : 16777, 2020
  46. Huanxia W : A method for patient gait real-time monitoring based on powered exoskeleton and digital twin. Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 2022 Sep 16-18; Chongqing, China. Bellingham : SPIE, c2023, Vol 12566, pp734-743
  47. Hunter OF, Perry F, Salehi M, Bandurski H, Hubbard A, Ball CG, et al. : Science fiction or clinical reality: a review of the applications of artificial intelligence along the continuum of trauma care. World J Emerg Surg 18 : 16, 2023
  48. Imaduddin SM, Fanelli A, Vonberg FW, Tasker RC, Heldt T : Pseudo-Bayesian model-based noninvasive intracranial pressure estimation and tracking. IEEE Trans Biomed Eng 67 : 1604-1615, 2020
  49. Jahns FP, Miroz JP, Messerer M, Daniel RT, Taccone FS, Eckert P, et al. : Quantitative pupillometry for the monitoring of intracranial hypertension in patients with severe traumatic brain injury. Crit Care 23 : 155, 2019
  50. Jain S, Vyvere TV, Terzopoulos V, Sima DM, Roura E, Maas A, et al. : Automatic quantification of computed tomography features in acute traumatic brain injury. J Neurotrauma 36 : 1794-1803, 2019
  51. Jaishankar R, Fanelli A, Filippidis A, Vu T, Holsapple J, Heldt T : A spectral approach to model-based noninvasive intracranial pressure estimation. IEEE J Biomed Health Inform 24 : 2398-2406, 2020
  52. Jung MK, Ahn D, Park CM, Ha EJ, Roh TH, You NK, et al. : Prediction of serious intracranial hypertension from low-resolution neuromonitoring in traumatic brain injury: an explainable machine learning approach. IEEE J Biomed Health Inform 27 : 1903-1913, 2023
  53. Kashif FM, Verghese GC, Novak V, Czosnyka M, Heldt T : Model-based noninvasive estimation of intracranial pressure from cerebral blood flow velocity and arterial pressure. Sci Transl Med 4 : 129ra144, 2012
  54. Keshavamurthy KN, Leary OP, Merck LH, Kimia B, Collins S, Wright DW, et al. : Machine learning algorithm for automatic detection of CT-identifiable hyperdense lesions associated with traumatic brain injury. Medical Imaging 2017: Computer-Aided Diagnosis; 2017 Feb 11-16; Olando, FL. Bellingham : SPIE, c2017, Vol 10134, pp630-638
  55. Kim H, Lee SB, Son Y, Czosnyka M, Kim DJ : Hemodynamic instability and cardiovascular events after traumatic brain injury predict outcome after artifact removal with deep belief network analysis. J Neurosurg Anesthesiol 30 : 347-353, 2018
  56. Kim YJ : The impact of time to surgery on outcomes in patients with traumatic brain injury: a literature review. Int Emerg Nurs 22 : 214-219, 2014
  57. Kinoshita K : Traumatic brain injury: pathophysiology for neurocritical care. J Intensive Care 4 : 29, 2016
  58. Kovacs M, Peluso L, Njimi H, De Witte O, Gouvea Bogossian E, Quispe Cornejo A, et al. : Optimal cerebral perfusion pressure guided by brain oxygen pressure measurement. Front Neurol 12 : 732830, 2021
  59. Kristiansson H, Nissborg E, Bartek J Jr, Andresen M, Reinstrup P, Romner B : Measuring elevated intracranial pressure through noninvasive methods: a review of the literature. J Neurosurg Anesthesiol 25 : 372-385, 2013
  60. Lal A, Li G, Cubro E, Chalmers S, Li H, Herasevich V, et al. : Development and verification of a digital twin patient model to predict specific treatment response during the first 24 hours of sepsis. Crit Care Explor 2 : e0249, 2020
  61. Lameski P, Zdravevski E, Koceski S, Kulakov A, Trajkovik V : Suppression of intensive care unit false alarms based on the arterial blood pressure signal. IEEE Access 5 : 5829-5836, 2017
  62. Laubenbacher R, Sluka JP, Glazier JA : Using digital twins in viral infection. Science 371 : 1105-1106, 2021
  63. Lee HJ, Kim H, Kim YT, Won K, Czosnyka M, Kim DJ : Prediction of life-threatening intracranial hypertension during the acute phase of traumatic brain injury using machine learning. IEEE J Biomed Health Inform 25 : 3967-3976, 2021
  64. Lee SB, Kim H, Kim YT, Zeiler FA, Smielewski P, Czosnyka M, et al. : Artifact removal from neurophysiological signals: impact on intracranial and arterial pressure monitoring in traumatic brain injury. J Neurosurg 132 : 1952-1960, 2019
  65. Li H, Ma H, Yang B, Xu C, Cao L, Dong X, et al. : Automatic evaluation of mannitol dehydration treatments on controlling intracranial pressure using electrical impedance tomography. IEEE Sens J 20 : 4832-4839, 2020
  66. Li Q, Clifford GD : Signal quality and data fusion for false alarm reduction in the intensive care unit. J Electrocardiol 45 : 596-603, 2012
  67. Lin B, Chen Z, Li M, Lin H, Xu H, Zhu Y, et al. : Towards medical artificial general intelligence via knowledge-enhanced multimodal pretraining. Available at : https://doi.org/10.48550/arXiv.2304.14204
  68. Lin MY, Li CC, Lin PH, Wang JL, Chan MC, Wu CL, et al. : Explainable machine learning to predict successful weaning among patients requiring prolonged mechanical ventilation: a retrospective cohort study in Central Taiwan. Front Med (Lausanne) 8 : 663739, 2021
  69. Lyashevska O, Malone F, MacCarthy E, Fiehler J, Buhk JH, Morris L : Class imbalance in gradient boosting classification algorithms: application to experimental stroke data. Stat Methods Med Res 30 : 916-925, 2021
  70. Maas MB, Naidech AM, Batra A, Chou SH, Bleck TP : Comment on "Can quantitative pupillometry be used to screen for elevated intracranial pressure? A retrospective cohort study". Neurocrit Care 37 : 597-598, 2022
  71. Magoulas GD, Prentza A : Machine learning in medical applications in Paliouras G, Karkaletsis V, Spyropoulos CD (eds) : Machine Learning and Its Applications. Berlin : Springer, 2021, pp300-307
  72. Majdan M, Brazinova A, Rusnak M, Leitgeb J : Outcome prediction after traumatic brain injury: comparison of the performance of routinely used severity scores and multivariable prognostic models. J Neurosci Rural Pract 8 : 20-29, 2017
  73. Majdan M, Mauritz W, Brazinova A, Rusnak M, Leitgeb J, Janciak I, et al. : Severity and outcome of traumatic brain injuries (TBI) with different causes of injury. Brain Inj 25 : 797-805, 2011
  74. Makarenko S, Griesdale DE, Gooderham P, Sekhon MS : Multimodal neuromonitoring for traumatic brain injury: a shift towards individualized therapy. J Clin Neurosci 26 : 8-13, 2016
  75. Mariak Z, Swiercz M, Krejza J, Lewko J, Lyson T : Intracranial pressure processing with artificial neural networks: classification of signal properties. Acta Neurochir (Wien) 142 : 407-411; discussion 411-412, 2000
  76. Marshall GT, James RF, Landman MP, O'Neill PJ, Cotton BA, Hansen EN, et al. : Pentobarbital coma for refractory intra-cranial hypertension after severe traumatic brain injury: mortality predictions and one-year outcomes in 55 patients. J Trauma 69 : 275-283, 2010
  77. Matsushima K, Inaba K, Siboni S, Skiada D, Strumwasser AM, Magee GA, et al. : Emergent operation for isolated severe traumatic brain injury: does time matter? J Trauma Acute Care Surg 79 : 838-842, 2015
  78. McIntyre LA, Fergusson DA, Hebert PC, Moher D, Hutchison JS : Prolonged therapeutic hypothermia after traumatic brain injury in adults: a systematic review. JAMA 289 : 2992-2999, 2003
  79. McIver KG : The Application of High-Performance Computing to Create and Analyze Simulations of Human Injury. West Lafayette : Purdue University Graduate School, 2022
  80. Melinosky C, Yang S, Hu P, Li H, Miller CHT, Khan I, et al. : Continuous vital sign analysis to predict secondary neurological decline after traumatic brain injury. Front Neurol 9 : 761, 2018
  81. Meyfroidt G, Bouzat P, Casaer MP, Chesnut R, Hamada SR, Helbok R, et al. : Management of moderate to severe traumatic brain injury: an update for the intensivist. Intensive Care Med 48 : 649-666, 2022
  82. Mikola A, Ratsep I, Sarkela M, Lipping T : Prediction of outcome in traumatic brain injury patients using long-term qEEG features. 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC); 2015 Aug 25-29; Milan, Italy. New York : IEEE, c2015, pp1532-1535
  83. Miyagawa T, Sasaki M, Yamaura A : Intracranial pressure based decision making: prediction of suspected increased intracranial pressure with machine learning. PLoS One 15 : e0240845, 2020
  84. Moor M, Banerjee O, Abad ZSH, Krumholz HM, Leskovec J, Topol EJ, et al. : Foundation models for generalist medical artificial intelligence. Nature 616 : 259-265, 2023
  85. Moyer JD, Lee P, Bernard C, Henry L, Lang E, Cook F, et al. : Machine learning-based prediction of emergency neurosurgery within 24 h after moderate to severe traumatic brain injury. World J Emerg Surg 17 : 42, 2022
  86. Naharro-Abellan A, Lobo-Val-buena B, Gordo F : Clinical decision support systems: future or present in ICU. ICU Manag Pract 19 : 202-205, 2019
  87. Noh SH, Cho PG, Kim KN, Kim SH, Shin DA : Artificial intelligence for neurosurgery : current state and future directions. J Korean Neurosurg Soc 66 : 113-120, 2023
  88. Noor NSEM, Ibrahim H, Lah MHC, Abdullah JM : Improving outcome prediction for traumatic brain injury from imbalanced datasets using RUSBoosted trees on electroencephalography spectral power. IEEE Access 9 : 121608-121631, 2021
  89. Noraky J, Verghese GC, Searls DE, Lioutas VA, Sonni S, Thomas A, et al. : Noninvasive intracranial pressure determination in patients with subarachnoid hemorrhage. Acta Neurochir Suppl 122 : 65-68, 2016
  90. Osheroff JA, Teich J, Levick D, Saldana L, Velasco F, Sittig D, et al. : Improving outcomes with clinical decision support: an implementer's guide. Chicago : Himss Publishing, 2012
  91. Pansell J, Hack R, Rudberg P, Bell M, Cooray C : Can quantitative pupillometry be used to screen for elevated intracranial pressure? A retrospective cohort study. Neurocrit Care 37 : 531-537, 2022
  92. Pesapane F, Codari M, Sardanelli F : Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2 : 35, 2018
  93. Pimentel MA, Brennan T, Lehman LW, King NK, Ang BT, Feng M : Outcome prediction for patients with traumatic brain injury with dynamic features from intracranial pressure and arterial blood pressure signals: a Gaussian process approach. Acta Neurochir Suppl 122 : 85-91, 2016
  94. Popovic D, Khoo M, Lee S : Noninvasive monitoring of intracranial pressure. Recent Pat Biomed Eng 2 : 165-179, 2009
  95. Powers WJ : Intracerebral hemorrhage and head trauma: common effects and common mechanisms of injury. Stroke 41(10 Suppl) : S107-S110, 2010
  96. Raj R, Luostarinen T, Pursiainen E, Posti JP, Takala RSK, Bendel S, et al. : Machine learning-based dynamic mortality prediction after traumatic brain injury. Sci Rep 9 : 17672, 2019
  97. Rajaei F, Cheng S, Williamson CA, Wittrup E, Najarian K : AI-based decision support system for traumatic brain injury: a survey. Diagnostics (Basel) 13 : 1640, 2023
  98. Rajpurkar P, Lungren MP : The current and future state of AI interpretation of medical images. N Engl J Med 388 : 1981-1990, 2023
  99. Robba C, Asgari S, Gupta A, Badenes R, Sekhon M, Bequiri E, et al. : Lung injury is a predictor of cerebral hypoxia and mortality in traumatic brain injury. Front Neurol 11 : 771, 2020
  100. Robba C, Bacigaluppi S, Cardim D, Donnelly J, Bertuccio A, Czosnyka M : Non-invasive assessment of intracranial pressure. Acta Neurol Scand 134 : 4-21, 2016
  101. Robba C, Pozzebon S, Moro B, Vincent JL, Creteur J, Taccone FS : Multimodal non-invasive assessment of intracranial hypertension: an observational study. Crit Care 24 : 379, 2020
  102. Rohaut B, Eliseyev A, Claassen J : Uncovering consciousness in unresponsive ICU patients: technical, medical and ethical considerations. Crit Care 23 : 78, 2019
  103. Rosenberg JB, Shiloh AL, Savel RH, Eisen LA : Non-invasive methods of estimating intracranial pressure. Neurocrit Care 15 : 599-608, 2011
  104. Ryu JA, Jung W, Jung YJ, Kwon DY, Kang K, Choi H, et al. : Early prediction of neurological outcome after barbiturate coma therapy in patients undergoing brain tumor surgery. PLoS One 14 : e0215280, 2019
  105. Sadrawi M, Lin YT, Lin CH, Mathunjwa B, Hsin HT, Fan SZ, et al. : Noninvasive hemodynamics monitoring system based on electrocardiography via deep convolutional autoencoder. Sensors (Basel) 21 : 6264, 2021
  106. Sainbhi AS, Gomez A, Froese L, Slack T, Batson C, Stein KY, et al. : Noninvasive and minimally-invasive cerebral autoregulation assessment: a narrative review of techniques and implications for clinical research. Front Neurol 13 : 872731, 2022
  107. Scalzo F, Hamilton R, Asgari S, Kim S, Hu X : Intracranial hypertension prediction using extremely randomized decision trees. Med Eng Phys 34 : 1058-1065, 2012
  108. Schweingruber N, Mader MMD, Wiehe A, Roder F, Gottsche J, Kluge S, et al. : A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients. Brain 145 : 2910-2919, 2022
  109. Seelig JM, Becker DP, Miller JD, Greenberg RP, Ward JD, Choi SC : Traumatic acute subdural hematoma: major mortality reduction in comatose patients treated within four hours. N Engl J Med 304 : 1511-1518, 1981
  110. Shah RV, Grennan G, Zafar-Khan M, Alim F, Dey S, Ramanathan D, et al. : Personalized machine learning of depressed mood using wearables. Transl Psychiatry 11 : 338, 2021
  111. Sidey-Gibbons JAM, Sidey-Gibbons CJ : Machine learning in medicine: a practical introduction. BMC Med Res Methodol 19 : 64, 2019
  112. Smith M : Multimodality neuromonitoring in adult traumatic brain injury: a narrative review. Anesthesiology 128 : 401-415, 2018
  113. Son Y, Lee SB, Kim H, Song ES, Huh H, Czosnyka M, et al. : Automated artifact elimination of physiological signals using a deep belief network: an application for continuously measured arterial blood pressure waveforms. Inf Sci 456 : 145-158, 2018
  114. Staartjes VE, Stumpo V, Kernbach JM, Klukowska AM, Gadjradj PS, Schroder ML, et al. : Machine learning in neurosurgery: a global survey. Acta Neurochir (Wien) 162 : 3081-3091, 2020
  115. Stangler LA, Kouzani A, Bennet KE, Dumee L, Berk M, Worrell GA, et al. : Microdialysis and microperfusion electrodes in neurologic disease monitoring. Fluids Barriers CNS 18 : 52, 2021
  116. Stein SC, Georgoff P, Meghan S, Mirza KL, El Falaky OM : Relationship of aggressive monitoring and treatment to improved outcomes in severe traumatic brain injury. J Neurosurg 112 : 1105-1112, 2010
  117. Stevens AR, Su Z, Toman E, Belli A, Davies D : Optical pupillometry in traumatic brain injury: neurological pupil index and its relationship with intracranial pressure through significant event analysis. Brain Inj 33 : 1032-1038, 2019
  118. Steyerberg EW, Mushkudiani N, Perel P, Butcher I, Lu J, McHugh GS, et al. : Predicting outcome after traumatic brain injury: development and international validation of prognostic scores based on admission characteristics. PLoS Med 5 : e165; discussion e165, 2008
  119. Stocker RA : Intensive care in traumatic brain injury including multimodal monitoring and neuroprotection. Med Sci (Basel) 7 : 37, 2019
  120. Surendrakumar S, Rabelo TK, Campos ACP, Mollica A, Abrahao A, Lipsman N, et al. : Neuromodulation therapies in pre-clinical models of traumatic brain injury: systematic review and translational applications. J Neurotrauma 40 : 435-448, 2023
  121. Svedung Wettervik TM, Lewen A, Enblad P : Fine tuning of traumatic brain injury management in neurointensive care-indicative observations and future perspectives. Front Neurol 12 : 638132, 2021
  122. Tao F, Qi Q : Make more digital twins. Nature 573 : 490-491, 2019
  123. Thabtah F, Abdelhamid N, Peebles D : A machine learning autism classification based on logistic regression analysis. Health Inf Sci Syst 7 : 12, 2019
  124. Tierney KJ, Nayak NV, Prestigiacomo CJ, Sifri ZC : Neurosurgical intervention in patients with mild traumatic brain injury and its effect on neurological outcomes. J Neurosurg 124 : 538-545, 2016
  125. Tisdall MM, Smith M : Multimodal monitoring in traumatic brain injury: current status and future directions. Br J Anaesth 99 : 61-67, 2007
  126. Tsien CL, Fackler JC : Poor prognosis for existing monitors in the intensive care unit. Crit Care Med 25 : 614-619, 1997
  127. Tsien CL, Kohane IS, McIntosh N : Multiple signal integration by decision tree induction to detect artifacts in the neonatal intensive care unit. Artif Intell Med 19 : 189-202, 2000
  128. Tu KC, Eric Nyam TT, Wang CC, Chen NC, Chen KT, Chen CJ, et al. : A computer-assisted system for early mortality risk prediction in patients with traumatic brain injury using artificial intelligence algorithms in emergency room triage. Brain Sci 12 : 612, 2022
  129. Tunthanathip T, Oearsakul T : Application of machine learning to predict the outcome of pediatric traumatic brain injury. Chin J Traumatol 24 : 350-355, 2021
  130. van de Sande D, van Genderen ME, Huiskens J, Gommers D, van Bommel J : Moving from bytes to bedside: a systematic review on the use of artificial intelligence in the intensive care unit. Intensive Care Med 47 : 750-760, 2021
  131. Vilela GH, Cabella B, Mascarenhas S, Czosnyka M, Smielewski P, Dias C, et al. : Validation of a new minimally invasive intracranial pressure monitoring method by direct comparison with an invasive technique. Acta Neurochir Suppl 122 : 97-100, 2016
  132. Wang X, Gao Y, Lin J, Rangwala H, Mittu R : A machine learning approach to false alarm detection for critical arrhythmia alarms. 2015 IEEE 14th international conference on machine learning and applications (ICMLA); 2015 Dec 9-11; Miami, FL. New York : IEEE, 2015, pp202-207
  133. Wang Y, Huang C, Tian R, Yang X : Target temperature management and therapeutic hypothermia in sever neuroprotection for traumatic brain injury: clinic value and effect on oxidative stress. Medicine (Baltimore) 102 : e32921, 2023
  134. Wang Z, Wang H, Becker R, Rufo J, Yang S, Mace BE, et al. : Acoustofluidic separation enables early diagnosis of traumatic brain injury based on circulating exosomes. Microsyst Nanoeng 7 : 20, 2021
  135. Whalen S, Schreiber J, Noble WS, Pollard KS : Navigating the pitfalls of applying machine learning in genomics. Nat Rev Genet 23 : 169-181, 2022
  136. Ye G, Balasubramanian V, Li JK, Kaya M : Machine learning-based continuous intracranial pressure prediction for traumatic injury patients. IEEE J Transl Eng Health Med 10 : 4901008, 2022
  137. Yokobori S, Hosein K, Burks S, Sharma I, Gajavelli S, Bullock R : Biomarkers for the clinical differential diagnosis in traumatic brain injury--a systematic review. CNS Neurosci Ther 19 : 556-565, 2013
  138. Young AMH, Guilfoyle MR, Donnelly J, Smielewski P, Agarwal S, Czosnyka M, et al. : Multimodality neuromonitoring in severe pediatric traumatic brain injury. Pediatr Res 83 : 41-49, 2018
  139. Yu R, Wang S, Xu J, Wang Q, He X, Li J, et al. : Machine learning approaches-driven for mortality prediction for patients undergoing craniotomy in ICU. Brain Inj 35 : 1658-1664, 2021
  140. Zeiler FA, Iturria-Medina Y, Thelin EP, Gomez A, Shankar JJ, Ko JH, et al. : Integrative neuroinformatics for precision prognostication and personalized therapeutics in moderate and severe traumatic brain injury. Front Neurol 12 : 729184, 2021
  141. Zhang X, Medow JE, Iskandar BJ, Wang F, Shokoueinejad M, Koueik J, et al. : Invasive and noninvasive means of measuring intracranial pressure: a review. Physiol Meas 38 : R143-R182, 2017
  142. Zhang X, Yan C, Gao C, Malin BA, Chen Y : Predicting missing values in medical data via XGBoost regression. J Healthc Inform Res 4 : 383-394, 2020