Künstliche Intelligenz in der Neurologie

ZUSAMMENFASSUNG

Künstliche Intelligenz (KI) ist spätestens seit der Veröffentlichung von ChatGPT in aller Munde. Die Grundlage eines jeden KI-Modells ist die Analyse von Daten. In der Neurologie sind aufgrund der Digitalisierung ausreichend große Datenmengen vorhanden, um mittels KI analysiert werden zu können. Dieser Artikel soll einen Überblick über KI-Modelle sowie aktuelle Forschungen und Anwendungen in der Neurologie geben. Mögliche Probleme in der Integration der KI in den klinischen Alltag werden beleuchtet und ein Ausblick auf die Zukunft wird versucht.

ABSTRACT

Ever since the publication of ChatGPT everyone is talking about Artificial intelligence (AI). Every AI-algorithm is based on the analysis of data. In neurology, digitalization has created sufficiently large amounts of data to be analyzed. This article aims to provide an overview of AI models , as well as current research and applications of AI in the field of neurology. It will also briefly highlight potential problems in the integration of AI into clinical practice and provide an outlook for the future.

Schlüsselwörter

Künstliche Intelligenz - Neurologie - Daten - Analyse - Herausforderungen

Key words

Artificial intelligence - neurology - data - analysis - challenges

Publication History

Article published online:
04 September 2023

• Literatur

• 1 Turing AM. Computing Machinery and Intelligence. Mind 1950; 49: 433-460
• 2 Amaro Junior E. Artificial intelligence and Big Data in neurology. Arq Neuropsiquiatr 2022; 80: 342-347
• 3 Europäisches Parlament. Was ist künstliche Intelligenz und wie wird sie genutzt? 2021. https://www.europarl.europa.eu/news/de/headlines/society/20200827STO85804/was-ist-kunstliche-intelligenz-und-wie-wird-sie-genutzt Stand: 12.06.2023
• 4 Hauser WA, Beghi E. First seizure definitions and worldwide incidence and mortality. Epilepsia 2008; 49: 8-12
• 5 Elger CE, Berkenfeld R.. S1-Leitlinie Erster epileptischer Anfall und Epilepsien im Erwachsenenalter Leitlinien für Diagnostik und Therapie in der Neurologie. Berlin. 2017
• 6 Baldin E, Hauser WA, Buchhalter JR. et al Yield of epileptiform electroencephalogram abnormalities in incident unprovoked seizures: A population-based study. Epilepsia 2014; 55: 1389-1398
• 7 Gleichgerrcht E, Munsell BC, Alhusaini S. et al Artificial intelligence for classification of temporal lobe epilepsy with ROI-level MRI data: A worldwide ENIGMA-Epilepsy study. Neuroimage Clin 2021; 31: 102765
• 8 Hong S-J, Bernhardt BC, Schrader DS. et al Whole-brain MRI phenotyping in dysplasia-related frontal lobe epilepsy. Neurology 2016; 86: 643-650
• 9 Jin B, Krishnan B, Adler S. et al Automated detection of focal cortical dysplasia type II with surface-based magnetic resonance imaging postprocessing and machine learning. Epilepsia 2018; 59: 982-992
• 10 Jae Sung Lee, Dong Soo Lee, Seok-Ki Kim. et al Localization of epileptogenic zones in F-18 FDG brain PET of patients with temporal lobe epilepsy using artificial neural network. IEEE Trans Med Imaging 2000; 19: 347-355
• 11 Kwan P, Arzimanoglou A, Berg AT. et al Definition of drug resistant epilepsy: Consensus proposal by the ad hoc Task Force of the ILAE Commission on Therapeutic Strategies. Epilepsia 2009; 51: 1069-1077
• 12 An S, Malhotra K, Dilley C. et al Predicting drug-resistant epilepsy — A machine learning approach based on administrative claims data. Epilepsy & Behavior 2018; 89: 118-125
• 13 Devinsky O, Dilley C, Ozery-Flato M. et al Changing the approach to treatment choice in epilepsy using big data. Epilepsy & Behavior 2016; 56: 32-37
• 14 Kural MA, Jing J, Fürbass F. et al Accurate identification of EEG recordings with interictal epileptiform discharges using a hybrid approach: Artificial intelligence supervised by human experts. Epilepsia 2022; 63: 1064-1073
• 15 Jing J, Sun H, Kim JA. et al Development of Expert-Level Automated Detection of Epileptiform Discharges During Electroencephalogram Interpretation. JAMA Neurol 2020; 77: 103
• 16 Koren J, Hafner S, Feigl M. et al Systematic analysis and comparison of commercial seizure-detection software. Epilepsia 2021; 62: 426-438
• 17 Tveit J, Aurlien H, Plis S. et al Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence. JAMA Neurol 2023
• 18 Elger CE, Hoppe C. Diagnostic challenges in epilepsy: seizure under-reporting and seizure detection. Lancet Neurol 2018; 17: 279-288
• 19 Zambrana-Vinaroz D, Vicente-Samper JM, Manrique-Cordoba J. et al Wearable Epileptic Seizure Prediction System Based on Machine Learning Techniques Using ECG, PPG and EEG Signals. Sensors 2022: 22
• 20 Meisel C, El Atrache R, Jackson M. et al Machine learning from wristband sensor data for wearable, noninvasive seizure forecasting. Epilepsia 2020; 61: 2653-2666
• 21 Beniczky S, Karoly P, Nurse E. et al Machine learning and wearable devices of the future. Epilepsia 2021: 62
• 22 NightWatch. Kostenerstattung im Einzelfall Stand 2023.. https://nightwatchepilepsy.com/de/kostenerstattung/ Stand: 13.06.2023
• 23 Skarpaas TL, Jarosiewicz B, Morrell MJ. Brain-responsive neurostimulation for epilepsy (RNS® System). Epilepsy Res 2019; 153: 68-70
• 24 Abedi V, Goyal N, Tsivgoulis G. et al Novel Screening Tool for Stroke Using Artificial Neural Network. Stroke 2017; 48: 1678-1681
• 25 Soun JE, Chow DS, Nagamine M. et al Artificial Intelligence and Acute Stroke Imaging. American Journal of Neuroradiology 2021; 42: 2-11
• 26 Chan KL, Leng X, Zhang W. et al Early Identification of High-Risk TIA or Minor Stroke Using Artificial Neural Network. Front Neurol 2019: 10
• 27 Shafaat O, Bernstock JD, Shafaat A. et al Leveraging artificial intelligence in ischemic stroke imaging. Journal of Neuroradiology 2022; 49: 343-351
• 28 Krishnamurthi R, Hale L, Barker-Collo S. et al Mobile technology for primary stroke prevention a proof-of-concept pilot randomized controlled trial. Stroke 2019; 50: 196-198
• 29 García L, Tomás J, Parra L. et al An m-health application for cerebral stroke detection and monitoring using cloud services. Int J Inf Manage 2019; 45: 319-327
• 30 Hyland SL, Faltys M, Hüser M. et al Early prediction of circulatory failure in the intensive care unit using machine learning. Nat Med 2020; 26: 364-373
• 31 Savin I, Ershova K, Kurdyumova N. et al Healthcare-associated ventriculitis and meningitis in a neuro-ICU: Incidence and risk factors selected by machine learning approach. J Crit Care 2018; 45: 95-104
• 32 Jonkman AH, Rauseo M, Carteaux G. et al Proportional modes of ventilation: technology to assist physiology. Intensive Care Med 2020; 46: 2301-2313
• 33 Šeho L, Šutković H, Tabak V. et al Using Artificial Intelligence in Diagnostics of Meningitis. IFAC-PapersOnLine 2022; 55: 56-61
• 34 D’Angelo G, Pilla R, Tascini C. et al A proposal for distinguishing between bacterial and viral meningitis using genetic programming and decision trees. Soft comput 2019; 23: 11775-11791
• 35 Korolev S, Safiullin A, Belyaev M. et al Residual and plain convolutional neural networks for 3 D brain MRI classification. In: 2017 IEEE 14th International Symposium on Biomedical Imaging. IEEE 2017: 835-838
• 36 Bo L, Zhang Z, Jiang Z. et al Differentiation of Brain Abscess From Cystic Glioma Using Conventional MRI Based on Deep Transfer Learning Features and Hand-Crafted Radiomics Features. Front Med 2021: 8
• 37 Li R, Wang X, Lawler K. et al Applications of artificial intelligence to aid early detection of dementia: A scoping review on current capabilities and future directions. J Biomed Inform 2022: 127
• 38 Tăuţan AM, Ionescu B, Santarnecchi E. Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques. Artif Intell Med 2021: 117
• 39 Wu J, Li Y, Yin L. et al Automated assessment of balance: A neural network approach based on large-scale balance function data. Front Public Health 2022; 10: 882811
• 40 Mauldin T, Canby M, Metsis V. et al SmartFall: A Smartwatch-Based Fall Detection System Using Deep Learning. Sensors 2018; 18: 3363
• 41 Iizuka T, Fukasawa M, Kameyama M. Deep-learning-based imaging-classification identified cingulate island sign in dementia with Lewy bodies. Sci Rep 2019: 9
• 42 Chang C-H, Lin C-H, Lane H-Y. Machine Learning and Novel Biomarkers for the Diagnosis of Alzheimer’s Disease. Int J Mol Sci 2021: 22
• 43 Watson NF, Fernandez CR. Artificial Intelligence and Sleep: Advancing Sleep Medicine. Sleep Med Rev 2021: 59
• 44 Thiesse L, Staner L, Fuchs G. et al Performance of Somno-Art Software compared to polysomnography interscorer variability: A multi-center study. Sleep Med 2022; 96: 14-19
• 45 Bandyopadhyay A, Goldstein C. Clinical applications of artificial intelligence in sleep medicine: a sleep clinician’s perspective. Sleep and Breathing 2023; 27: 39-55
• 46 Veauthier C, Ryczewski J, Mansow-Model S. et al Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing. Sci Rep 2019: 9
• 47 Rudie JD, Rauschecker AM, Bryan RN. et al Emerging Applications of Artificial Intelligence in Neuro-Oncology. Radiology 2019; 290: 607-618
• 48 Clark K, Vendt B, Smith K. et al The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository. J Digit Imaging 2013; 26: 1045-1057
• 49 Rathore S, Akbari H, Doshi J. Radiomic signature of infiltration in peritumoral edema predicts subsequent recurrence in glioblastoma: implications for personalized radiotherapy planning. Journal of Medical Imaging 2018; 05: 1
• 50 Ortiz-Ramón R, Larroza A, Ruiz-España S. et al Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol 2018; 28: 4514-4523
• 51 Boyle AJ, Gaudet VC, Black SE. et al Artificial intelligence for molecular neuroimaging. Ann Transl Med 2021; 09: 822-822
• 52 Capper D, Jones DTW, Sill M. et al DNA methylation-based classification of central nervous system tumours. Nature 2018; 555: 469-474
• 53 Galldiks N, Zadeh G, Lohmann P. Artificial Intelligence, Radiomics, and Deep Learning in Neuro-Oncology. Neurooncol Adv 2020; 02: iv1-iv2
• 54 Aslam N, Khan IU, Bashamakh A. et al Multiple Sclerosis Diagnosis Using Machine Learning and Deep Learning: Challenges and Opportunities. Sensors 2022; 22: 7856
• 55 Xiang Y, Zeng C, Liu B. et al Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3 D Multi-Sequence MRI. Journal of Magnetic Resonance Imaging 2022; 55: 1082-1092
• 56 Kim H, Lee Y, Kim Y-H. et al Deep Learning-Based Method to Differentiate Neuromyelitis Optica Spectrum Disorder From Multiple Sclerosis. Front Neurol 2020: 11
• 57 Martynova E, Goyal M, Johri S. et al Serum and Cerebrospinal Fluid Cytokine Biomarkers for Diagnosis of Multiple Sclerosis. Mediators Inflamm 2020; 2020: 1-10
• 58 Kenney RC, Liu M, Hasanaj L. et al The Role of Optical Coherence Tomography Criteria and Machine Learning in Multiple Sclerosis and Optic Neuritis Diagnosis. Neurology 2022; 99: e1100-e1112
• 59 Voigt I, Inojosa H, Dillenseger A. et al Digital Twins for Multiple Sclerosis. Front Immunol 2021: 12
• 60 Ellertsson S, Loftsson H, Sigurdsson EL. Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy. Scand J Prim Health Care 2021; 39: 448-458
• 61 Sanchez-Sanchez PA, García-González JR, Rúa Ascar JM. Automatic migraine classification using artificial neural networks. F1000Res 2020; 09: 618
• 62 Chong CD, Gaw N, Fu Y. et al Migraine classification using magnetic resonance imaging resting-state functional connectivity data. Cephalalgia 2017; 37: 828-844
• 63 Parrales Bravo F, Del Barrio García AA, Gallego MM. et al Prediction of patient’s response to OnabotulinumtoxinA treatment for migraine. Heliyon 2019; 05: e01043
• 64 Ferroni P, Zanzotto FM, Scarpato N. et al Machine learning approach to predict medication overuse in migraine patients. Comput Struct Biotechnol J 2020; 18: 1487-1496
• 65 Formeister EJ, Baum RT, Sharon JD. Supervised machine learning models for classifying common causes of dizziness. Am J Otolaryngol 2022; 43: 103402
• 66 Yu F, Wu P, Deng H. et al A Questionnaire-Based Ensemble Learning Model to Predict the Diagnosis of Vertigo: Model Development and Validation Study. J Med Internet Res 2022; 24: e34126
• 67 Krafczyk S, Tietze S, Swoboda W. et al Artificial neural network: A new diagnostic posturographic tool for disorders of stance. Clinical Neurophysiology 2006; 117: 1692-1698
• 68 Ahmadi SA, Vivar G, Frei J. et al Towards computerized diagnosis of neurological stance disorders: data mining and machine learning of posturography and sway. J Neurol 2019; 266: 108-117
• 69 Ahmadi S-A, Vivar G, Navab N. et al Modern machine-learning can support diagnostic differentiation of central and peripheral acute vestibular disorders. J Neurol 2020; 267: 143-152
• 70 van Bonn SM, Behrendt SP, Pawar BL. et al Smartphone-based nystagmus diagnostics: development of an innovative app for the targeted detection of vertigo. European Archives of Oto-Rhino-Laryngology 2022; 279: 5565-5571
• 71 Groezinger M, Huppert D, Strobl R. et al Development and validation of a classification algorithm to diagnose and differentiate spontaneous episodic vertigo syndromes: results from the DizzyReg patient registry. J Neurol 2020; 267: 160-167
• 72 Deutscher Bundestag. Bericht der Enquete-Kommission Künstliche Intelligenz – Gesellschaftliche Verantwortung und wirtschaftliche, soziale und ökologische Potenziale. Berlin; 2020
• 73 Europäisches Parlament. KI-Gesetz: erste Regulierung der künstlichen Intelligenz. KI-Gesetz: erste Regulierung der künstlichen Intelligenz 2023. https://www.europarl.europa.eu/news/de/headlines/society/20230601STO93804/ki-gesetz-erste-regulierung-der-kunstlichen-intelligenz Stand: 20.06.2023
• 74 Brune G. KI -Update 1:Künstliche Intelligenz in der Medizin. Was denkt Deutschland über Künstliche Intelligenz? Eine Umfrageserie von AIHAMBURG. 2021 https://ai.hamburg/ki-update-kunstliche-intelligenz-in-der-medizin/#:~:text=Wenn%20Patienten%20und%20Patientinnen%20erfahren%2C%20dass%20%C3%84rztin%20oder,Bei%2061%20%25%20wird%20das%20Vertrauen%20nicht%20beeinflusst Stand: 20.06.2023
• 75 Adadi A, Berrada M. Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI). IEEE Access 2018; 06: 52138-52160
• 76 Bærøe K, Miyata-Sturm A, Henden E. How to achieve trustworthy artificial intelligence for health. Bull World Health Organ 2020; 98: 257-262
• 77 Simon DA, Shachar C, Cohen IG. At-home Diagnostics and Diagnostic Excellence. JAMA 2022; 327: 523
• 78 Ilan Y. Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes. Front Digit Health 2020: 2
• 79 Pedersen M, Verspoor K, Jenkinson M. et al Artificial intelligence for clinical decision support in neurology. Brain Commun 2020: 2