Der Einsatz von Künstlicher Intelligenz bei Alzheimer-Krankheit – Personalisierte Diagnostik und Therapie

Zusammenfassung

Am Beispiel der Demenz bei Alzheimer-Krankheit wird aufgezeigt, welche Chancen, aber auch Risiken neuere methodische Ansätze der Künstlichen Intelligenz (KI) (Artificial Intelligence, AI) für die Diagnostik und Therapie der Alzheimer-Demenz (AD) bieten. Daneben wird KI im Kontext einer ethisch-philosophischen Technologiekritik beleuchtet.

Abstract

Using the example of dementia in Alzheimer’s disease, it is shown which opportunities but also risks are posed by newer methodological approaches of artificial intelligence (AI) for the diagnosis and treatment of Alzheimer’s dementia (AD). In addition, AI is examined in the context of an ethical-philosophical critique of technology.

Schlüsselwörter

Alzheimer-Demenz - Präzisionsmedizin - personalisierte Medizin - Künstliche Intelligenz - angewandte Ethik

Keywords

Alzheimer’s dementia - precision medicine - personalized medicine - artificial intelligence - applied ethics

Publikationsverlauf

Artikel online veröffentlicht:
02. März 2021

• Literatur

• 1 Livingston G, Sommerlad A, Orgeta V. et al. Dementia prevention, intervention, and care. Lancet 2017; 390: 2673-2734
• 2 Cullen B, O’Neill B, Evans JJ. et al. A review of screening tests for cognitive impairment. J Neurol Neurosurg Psychiatr 2007; 78: 790-799
• 3 Jessen F, Amariglio RE, Buckley RF. et al. The characterisation of subjective cognitive decline. Lancet Neurol 2020; 19: 271-278
• 4 Klünemann HH, Kastner I, Kaczmarek V. DemTect – einfach, schnell und trotzdem zuverlässig, Frühdiagnostik bei Demenz. NeuroTransmitter 2011; 9: 44-46
• 5 Wiltfang J, Lewczuk P, Otto M. Biomarker bei Demenzen und anderen neurodegenerativen Erkrankungen. Nervenarzt 2016; 87: 1305-1309
• 6 Shahpasand-Kroner H, Klafki HW, Bauer C. et al. A two-step immunoassay for the simultaneous assessment of Aβ38, Aβ40 and Aβ42 in human blood plasma supports the Aβ42/Aβ40 ratio as a promising biomarker candidate of Alzheimer’s disease. Alzheimers Res Ther 2018; 10: 121
• 7 Vicario-Orri E, Opazo CM, Muñoz FJ. The pathophysiology of axonal transport in Alzheimer’s disease. J Alzheimers Dis 2015; 430: 1097-1113
• 8 van Dyck CH. Anti-Amyloid-β Monoclonal Antibodies for Alzheimer’s Disease: Pitfalls and Promise. Biol Psychiatry 2018; 83: 311-319
• 9 Ballard C, Aarsland D, Cummings J. et al. Drug repositioning and repurposing for Alzheimer disease. Nat Rev Neurol 2020; 12: 661-673
• 10 Mishra R, Li B. The Application of Artificial Intelligence in the Genetic Study of Alzheimer’s Disease. Aging Dis 2020; 11: 1567-1584
• 11 Noorbakhsh-Sabet N, Zand R, Zhang Y. et al. Artificial Intelligence Transforms the Future of Health Care. Am J Med 2019; 132: 795-801
• 12 Miller RA. Medical diagnostic decision support systems – past, present, and future: a threaded bibliography and brief commentary. J Am Med Inform Assoc 1994; 1: 8-27
• 13 Paranjpe MD, Taubes A, Sirota M. Insights into Computational Drug Repurposing for Neurodegenerative Disease. Trends Pharmacol Sci 2019; 40: 565-576
• 14 Neuner SM, Tcw J, Goate AM. Genetic architecture of Alzheimer’s disease. Neurobiol Dis 2020; 143: 104976
• 15 Bartels C, Belz M, Vogelgsang J. et al. To Be Continued? Long-Term Treatment Effects of Antidepressant Drug Classes and Individual Antidepressants on the Risk of Developing Dementia: A German Case-Control Study. J Clin Psychiatry 2020; 81: 19m13205
• 16 Bartels C, Wagner M, Wolfsgruber S. et al. Alzheimer’s Disease Neuroimaging Initiative. Impact of SSRI Therapy on Risk of Conversion from Mild Cognitive Impairment to Alzheimer’s Dementia in Individuals With Previous Depression. Am J Psychiatry 2018; 175: 232-241
• 17 Reddy OC, van der Werf YD. The Sleeping Brain: Harnessing the Power of the Glymphatic System through Lifestyle Choices. Brain Sci 2020; 10: 868
• 18 Ding Y, Sohn JH, Kawczynski MG. et al. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using (18) F-FDG PET of the Brain. Radiology 2019; 290: 456-464
• 19 de la Fuente Garcia S, Ritchie CW, Luz S. Artificial Intelligence, Speech, and Language Processing Approaches to Monitoring Alzheimer’s Disease: A Systematic Review. J Alzheimers Dis 2020; 78: 1547-1574
• 20 Ashton NJ, Nevado-Holgado AJ, Barber IS. et al. A plasma protein classifier for predicting amyloid burden for preclinical Alzheimer’s disease. Sci Adv 2019; 5: eaau7220
• 21 Vogelgsang J, Wiltfang J, Klafki HW. Validation of a Chemiluminescence Immunoassay for Measuring Amyloid-β in Human Blood Plasma. Methods Mol Biol 2018; 1750: 111-124
• 22 Barthélemy NR, Li Y, Joseph-Mathurin N. et al. Dominantly Inherited Alzheimer Network. A soluble phosphorylated tau signature links tau, amyloid and the evolution of stages of dominantly inherited Alzheimer’s disease. Nat Med 2020; 26: 398-407
• 23 Mattsson-Carlgren N, Janelidze S, Palmqvist S. et al. Longitudinal plasma p-tau217 is increased in early stages of Alzheimer’s disease. Brain 2020; 143: 3234-3241
• 24 Ovod V, Ramsey KN, Mawuenyega KG. et al. Amyloid β concentrations and stable isotope labeling kinetics of human plasma specific to central nervous system amyloidosis. Alzheimers Dement 2017; 8: 841-849
• 25 Nakamura A, Kaneko N, Villemagne VL. et al. High performance plasma amyloid-β biomarkers for Alzheimer’s disease. Nature 2018; 554: 249-254
• 26 Pearson K. On lines and planes of closest fit to a system of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science 1901; 2: 559-572
• 27 Tanevski J, Nguyen Th, Truong B. et al. Predicting cells position from single-cell transcriptomics. Life Sci Alliance 2020; 3: e202000867
• 28 Rajewsky N, Almouzni G, Gorski SA. et al. LifeTime and improving European healthcare through cell-based interceptive medicine. Nature 2020; 587: 377-386
• 29 Priebe S. Wo ist der Fortschritt?. Psychiat Prax 2012; 39: 55-56