intensiv, Table of Contents intensiv 2024; 32(05): 261-273DOI: 10.1055/a-2345-5718 CNE Schwerpunkt KI auf Intensivstationen Virtual Reality und künstliche Intelligenz – (R)Evolution auf der Intensivstation David Kober Recommend Article Abstract Buy Article Dieser Schwerpunkt ergründet die faszinierenden Potenziale von Virtual Reality, Augmented Reality und künstlicher Intelligenz für die Zukunft der Intensivtherapie. Er beleuchtet nicht nur konkrete Anwendungsbeispiele, sondern auch ethische Überlegungen und Hürden im klinischen Einsatz dieser Technologien. Full Text References Literatur 1 Bruns K, Reichert R. Reader Neue Medien: Texte zur digitalen Kultur und Kommunikation. Bielefeld: transcript; 2015 2 Kind S, Ferdinand JP, Hungerland T. et al Virtual und Augmented Reality: Status quo, Herausforderungen und zukünftige Entwicklungen – TA-Vorstudie. Büro für Technikfolgen-Abschätzung beim Deutschen Bundestag (TAB) 2019 3 Raffler D.. Virtual Reality – Die Technik. Oktober 2016. Im Internet: t1p.de/xqri7; Stand: 21.06.2024 4 Muhanna M. Virtual reality and the CAVE: Taxonomy, interaction challenges and research directions. J King Saud Univ Sci 2015; 27: 344-61 5 Lindner S, Latoschik ME, Rittner H. Virtual Reality als Baustein in der Behandlung akuter und chronischer Schmerzen. AINS 2020; 55: 549-61 6 Kanschik D, Bruno RR, Wolff G. et al Virtual and augmented reality in intensive care medicine: A systematic review. Ann Intensive Care 2023; 13: 81 7 Rolland JP, Hua H. Head-mounted display systems. Encyclopedia of optical engineering. New York: Marcel Dekker; 2005 8 Hollmann V. Smarte Kontaktlinsen. Europäische Sicherheit & Technik 2022; 10: 92 9 Kellmeyer P, Biller-Andorno N, Meynen G. Ethical tensions of virtual reality treatment in vulnerable patients. Nature Medicine 2019; 25: 1185-8 10 Slater M. A note on presence terminology. Presence Connect 2003; 03: 3 11 Slater M. Place illusion and plausibility can lead to realistic behaviour in immersive virtual environments. Philos Trans R Soc Lond B Biol Sci 2009; 364: 3549-57 12 Witmer BG, Singer MJ. Measuring presence in virtual environments: A presence questionnaire. Presence 1998; 07 (03) 225-40 13 Levac D, Galvin J. When is virtual reality „therapy“?. Arch Phys Med Rehabil 2013; 94: 795-8 14 Laufer Y, Weiss PL. Virtual reality in the assessment and treatment of children with motor impairment: A systematic review. J Phsy Ther Educ 2011; 25: 59-71 15 Selzer M, Clarke S, Cohen LG. et al Textbook of neural repair and rehabilitation. 2. Aufl. Cambridge: Cambridge University Press; 2014 16 Diemer J. Das Potenzial der virtuellen Realität in der Verhaltenstherapie. Nervenheilkunde 2019; 38: 553-9 17 Yeung AWK, Tosevska A, Klager E. et al Virtual and augmented reality applications in medicine: Analysis of the scientific literature. J Med Internet Res 2021; 23: e25499 18 Estai M, Bunt S. Best teaching practices in anatomy education: A critical review. Ann Anat 2016; 208: 151-7 19 Cutler LR, Hayter M, Ryan T. A critical review and synthesis of qualitative research on patient experiences of critical illness. Intensive Crit Care Nurs 2013; 29: 147-57 20 Puel F, Minville V, Vardon-Bounes F. What place for virtual reality in the intensive care unit during medical procedures?. J Intensive Care 2021; 09: 30 21 Chanques G, Pohlman AS, Kress JP. et al Psychometric comparison of three behavioural scales for the assessment of pain in critically ill patients unable to self-report. Crit Care 2014; 18: R160 22 Chanques G, Ely EW, Garnier O. et al The 2014 updated version of the confusion assessment method for the intensive care unit compared to the 5th version of the diagnostic and statistical manual of mental disorders and other current methods used by intensivists. Ann Intensive Care 2018; 08: 33 23 Lin HC, Li JP, Sit JWH. et al Effects of music intervention on physiological stress response and anxiety level of mechanically ventilated patients in China: A randomised controlled trial. J Clin Nurs 2010; 19: 978-987 24 Merliot-Gailhoustet L, Raimbert C, Garnier O. et al Discomfort improvement for critically ill patients using electronic relaxation devices: Results of the cross-over randomized controlled trial E-CHOISIR (Electronic-CHOIce of a System for Intensive care Relaxation). Crit Care 2022; 26: 263 25 Gerber SM, Jeitziner M, Wyss P. et al Visuo-acoustic stimulation that helps you to relax: A virtual reality setup for patients in the intensive care unit. Sci Rep 2017; 07: 13228 26 Mosso-Vázquez JL, Gao K, Wiederhold BK. et al Virtual reality for pain management in cardiac surgery. Cyberpsychol Behav Soc Netw 2014; 17: 371-8 27 Esumi R, Yokochi A, Shimaoka M. et al Virtual reality as a non-pharmacologic analgesic for fasciotomy wound infections in acute compartment syndrome: A case report. J Med Case Rep 2020; 14: 46 28 Laghlam D, Naudin C, Coroyer L. et al Virtual reality vs. Kalinox® for management of pain in intensive care unit after cardiac surgery: A randomized study. Ann Intensive Care 2021; 11: 74 29 Scapin SQ, Echevarría-Guanilo ME, Fuculo PRB. et al Virtual reality in the treatment of burn patients: A systematic review. Burns 2018; 44: 1403-16 30 Naef AC, Jeitziner M, Gerber SM. et al Virtual reality stimulation to reduce the incidence of delirium in critically ill patients: Study protocol for a randomized clinical trial. Trials 2021; 22: 174 31 Appleton R, Kinsella J, Quasim T. The incidence of intensive care unit-acquired weakness syndromes: A systematic review. J Intensive Care Soc 2014; 16: 126-36 32 Klawitter F, Schaller S, Söhle M. et al Intensive care unit-acquired weakness. Die Anaesthesiologie 2022; 71: 618-25 33 Gomes TT, Schujmann DS, Fu C. Rehabilitation through virtual reality: Physical activity of patients admitted to the intensive care unit. Rev Bras Ter Intensiva 2019; 31: 456-63 34 Parke S, Hough CL, Bunnell AE. The feasibility and acceptability of virtual therapy environments for early ICU mobilization. PM & R 2020; 12: 1214-21 35 Jackson JC, Pandharipande PP, Girard TD. et al Depression, post-traumatic stress disorder, and functional disability in survivors of critical illness in the BRAIN-ICU study: A longitudinal cohort study. Lancet Respir Med 2014; 02: 369-79 36 Marra A, Pandharipande PP, Girard TD. et al Co-occurrence of post-intensive care syndrome problems among 406 survivors of critical illness. Crit Care Med 2018; 46: 1393-401 37 Vlake JH, Van Genderen ME, Schut AFC. et al Patients suffering from psychological impairments following critical illness are in need of information. J Intensive Care 2020; 08: 6 38 Vlake JH, Van Bommel J, Wils E. et al Intensive care unit – specific virtual reality for critically ill patients with COVID-19: Multicenter randomized controlled trial. J Medic Internet Res 2022; 24: e32368 39 Jung C, Wolff G, Wernly B. et al Virtual and augmented reality in cardiovascular care: State-of-the-art and future perspectives. JACC Cardiovasc Imaging 2022; 15: 519-32 40 Wolff G, Bruno RR, Reiter M. et al Virtual reality device training for extracorporeal membrane oxygenation. Crit Care 2020; 24: 390 41 Chiang DH, Huang CC, Cheng S. et al Immersive virtual reality (VR) training increases the self-efficacy of in-hospital healthcare providers and patient families regarding tracheostomy-related knowledge and care skills. Medicine 2022; 101: e28570 42 Yu M, Yang M, Ku B. et al Effects of virtual reality simulation program regarding high-risk neonatal infection control on nursing students. Asian Nurs Res 2021; 15: 189-96 43 Agasthya N, Penfil S, Slamon N. Virtual reality simulation for pediatric airway intubation readiness education. Cureus 2020; 12: e12059 44 Pashler H, McDaniel MA, Rohrer D. et al Learning styles: Concepts and evidence. Psychol Sci Public Interest 2008; 09: 105-19 45 Alismail A, Thomas JB, Daher N. et al Augmented reality glasses improve adherence to evidence-based intubation practice. Adv Med Educ Pract 2019; 10: 279-86 46 Huang C, Thomas JB, Alismail A. et al The use of augmented reality glasses in central line simulation: „See one, simulate many, do one competently, and teach everyone“. Adv Med Educ Pract 2018; 09: 357-63 47 Zackoff M, Cruse B, Sahay R. et al Development and implementation of augmented reality enhanced high-fidelity simulation for recognition of patient decompensation. Simul Healthc 2020; 16: 221-30 48 Bruno RR, Lin Y, Wolff G. et al Virtual reality-assisted conscious sedation during transcatheter aortic valve implantation: A randomised pilot study. Eurointervention 2020; 16: e1014-e1020 49 Markus L, Willems KE, Maruna CC. et al Virtual reality: Feasibility of implementation in a regional burn center. Burns 2009; 35: 967-9 50 Schmid A.. VR/AR und Datenschutz – eine technische Betrachtung der Komponenten. Juli 2023. Im Internet: t1p.de/v32yo; Stand: 21.06.2024 51 Martin L, Peine A. Was ist neu … Einsatz von künstlicher Intelligenz in der Intensivmedizin. Anaesthesist 2020; 70: 40-1 52 Riessen R, Kumpf O, Auer P. et al Functional requirements of patient data management systems in intensive care medicine. Med Klin Intensivmed Notfmed 2024; 119: 171-80 53 Rappold E, Juraszovich B. Pflegepersonal-Bedarfsprognose für Österreich. Bundesministerium für Soziales, Gesundheit, Pflege und Konsumentenschutz. 2019 Im Internet: t1p.de/y9cf; Stand: 21.06.2024 54 Yoon JH, Pinsky MR, Clermont G. Artificial intelligence in critical care medicine. Crit Care 2022; 26: 75 55 Lovejoy CA, Buch V, Maruthappu M. Artificial intelligence in the intensive care unit. Crit Care 2019; 23: 7 56 U. S. Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SaMD); April 2019. Im Internet: t1p.de/elve8; Stand: 21.06.2024 57 Europäisches Parlament. Was ist künstliche Intelligenz und wie wird sie genutzt?; Juni 2023. Im Internet: t1p.de/2s5jy; Stand: 21.06.2024 58 Kirste M, Schürholz M. Künstliche Intelligenz. Heidelberg: Springer; 2019 59 Seah J, Tang JSN, Kitchen A. et al Chest radiographs in congestive heart failure: Visualizing neural network learning. Radiology 2019; 290: 514-22 60 Horng S, Liao R, Wang X. et al Deep learning to quantify pulmonary edema in chest radiographs. Radiology 2021; 03: e190228 61 Monteiro M, Newcombe V, Mathieu F. et al Multiclass semantic segmentation and quantification of traumatic brain injury lesions on head CT using deep learning: An algorithm development and multicentre validation study. Lancet Digit Health 2020; 02: e314-e322 62 Dreizin D, Zhou Y, Fu S. et al A multiscale deep learning method for quantitative visualization of traumatic hemoperitoneum at CT: Assessment of feasibility and comparison with subjective categorical estimation. Radiology 2020; 02: e190220 63 McKenzie MS, Auriemma CL, Olenik JM. et al An observational study of decision making by medical intensivists. Crit Care Med 2015; 43: 1660-8 64 Sim I, Gorman PN, Greenes RA. et al Clinical decision support systems for the practice of evidence-based medicine. J Am Med Inform Assoc 2001; 08: 527-34 65 Collins GS, Reitsma JB, Altman DG. et al Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD). Circulation 2015; 131: 211-9 66 Kipnis É, Ramsingh D, Bhargava M. et al Monitoring in the intensive care. Crit Care Res Pract 2012; 2012: 1-20 67 Chen L, Ogundele O, Clermont G. et al Dynamic and personalized risk forecast in step-down units. Implications for monitoring paradigms. Ann Am Thorac Soc 2017; 14: 384-91 68 Yoon JH, Mu L, Chen L. et al Predicting tachycardia as a surrogate for instability in the intensive care unit. J Clin Monit Comput 2019; 33: 973-85 69 Yoon JH, Jeanselme V, Dubrawski A. et al Prediction of hypotension events with physiologic vital sign signatures in the intensive care unit. Criti Care 2020; 24: 661 70 Joosten A, Rinehart J, Van der Linden P. et al Computer-assisted individualized hemodynamic management reduces intraoperative hypotension in intermediate- and high-risk surgery: A randomized controlled trial. Anesthesiol 2021; 135: 258-72 71 Wijnberge M, Geerts BF, Hol L. et al Effect of a machine learning-derived early warning system for intraoperative hypotension vs standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery. JAMA 2020; 323: 1052-60 72 Bartkowiak B, Snyder A, Benjamin A. et al Validating the electronic cardiac arrest risk triage (eCART) score for risk stratification of surgical inpatients in the postoperative setting. Ann Surg 2019; 269: 1059-63 73 Nemati S, Holder A, Razmi F. et al An interpretable machine learning model for accurate prediction of sepsis in the ICU. Crit Care Med 2018; 46: 547-53 74 Kobayashi N, Shiga T, Ikumi S. et al Semi-automated tracking of pain in critical care patients using artificial intelligence: A retrospective observational study. Sci Rep 2021: 11 75 Banoei MM, Dinparastisaleh R, Zadeh AV. et al Machine-learning-based COVID-19 mortality prediction model and identification of patients at low and high risk of dying. Crit Care 2021; 25: 328 76 Raj R, Luostarinen T, Pursiainen E. et al Machine learning-based dynamic mortality prediction after traumatic brain injury. Sci Rep 2019: 9 77 Rau CS, Wu S, Chuang JF. et al Machine learning models of survival prediction in trauma patients. J Clin Med 2019; 08: 799 78 Kim SY, Kim S, Cho J. et al A deep learning model for real-time mortality prediction in critically ill children. Crit Care 2019; 23: 279 79 Azar AT, El-Metwally SM. Decision tree classifiers for automated medical diagnosis. Neural Comput Appl 2012; 23: 2387-403 80 Saqib M, Iftikhar M, Neha F. et al Artificial intelligence in critical illness and its impact on patient care: A comprehensive review. Front Med (Lausanne) 2023; 10: 1176192 81 Chen KW, Wang YC, Liu MH. et al Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care. Front Cardiovasc Med 2022; 09: 1001982 82 Nhat PTH, Háo NV, Tho PV. et al Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit. Crit Care 2023; 27: 257 83 Chen CW, Lin WC, Hsu CH. et al Detecting ineffective triggering in the expiratory phase in mechanically ventilated patients based on airway flow and pressure deflection: Feasibility of using a computer algorithm. Crit Care Med 2008; 36: 455-61 84 Blanch L, Sales B, Montanyà J. et al Validation of the Better Care® system to detect ineffective efforts during expiration in mechanically ventilated patients: A pilot study. Intensive Care Med 2012; 38: 772-80 85 Sottile PD, Albers DJ, Higgins C. et al The association between ventilator dyssynchrony, delivered tidal volume, and sedation using a novel automated ventilator dyssynchrony detection algorithm. Crit Care Med 2018; 46: e151-e157 86 Gutiérrez G. Artificial intelligence in the intensive care unit. Crit Care 2020; 24: 101 87 Prasad N, Cheng LF, Chivers C. et al A reinforcement learning approach to weaning of mechanical ventilation in intensive care units. April 2017. Im Internet: t1p.de/haufq; Stand: 21.06.2024 88 Parreco J, Hidalgo AE, Parks J. et al Using artificial intelligence to predict prolonged mechanical ventilation and tracheostomy placement. J Surg Res 2018; 228: 179-87 89 Krumm S, Dwertmann A. Künstliche Intelligenz. Heidelberg: Springer; 2019 90 Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nature Med 2019; 25: 44-56
