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Nervenheilkunde 2023; 42(09): 612-620
DOI: 10.1055/a-2133-1575
Schwerpunkt

Kann KI Parkinson?

Die Beschreibung dopasensitiver Beweglichkeit mit Hilfe maschinellen LernensAI and Parkinson?How to describe dopa-sensitive movement using machine learning
Urban M. Fietzek
1   Neurologische Klinik und Poliklinik LMU-Klinikum München
2   Deutsches Zentrum für Neurodegenerative Erkrankungen, Standort München
4   Schön Klinik München Schwabing, Abt. Neurologie und klinische Neurophysiologie, München
,
Moritz Messner
3   Medizinische Fakultät, Ludwig-Maximilians-Universität München
,
Johannes Levin
1   Neurologische Klinik und Poliklinik LMU-Klinikum München
2   Deutsches Zentrum für Neurodegenerative Erkrankungen, Standort München
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ZUSAMMENFASSUNG

Die Zunahme mobilitätseinschränkender Erkrankungen wie Morbus Parkinson führt zu einer zunehmend stärkeren Belastung der Gesundheits- und Pflegesysteme. Fortschritte in der Mikroelektronik und der digitalen Datenverarbeitung ermöglichen im Sport- und Freizeitbereich seit geraumer Zeit die nicht invasive und ungestörte Erfassung von Bewegungsdaten über lange Zeiträume. Im medizinischen Bereich für die Bewegungsstörungen verspricht diese Technologie, sowohl die Forschungsansätze als auch die klinische Versorgung zu verbessern. Eine kontinuierliche Überwachung von Symptomen könnte das Erkennen von Parkinsonsymptomen an sich ermöglichen, ein Therapieansprechen detektieren oder die Indikation für Interventionen oder eine Therapieeskalation durch eine objektive Datengrundlage unterstützen.

Konkret stellt sich uns in diesem Beitrag die Frage, auf welchem Stand wir uns bei der Beschreibung von dopasensitiven Parkinsonsymptomen mit Sensoren befinden. Dabei werden wir nicht nur die vielfältigen Möglichkeiten, sondern auch die Herausforderungen diskutieren, die sich mit dieser neuen Technologie ergeben und die eine breitere Anwendung bislang verhindert haben. Wir beenden unseren Beitrag mit einem Ausblick, der Empfehlungen zur Überwindung dieser Herausforderungen gibt.

ABSTRACT

The rise of mobility-impairing diseases such as Parkinson’s disease (PD) is putting an increasingly heavy burden on health and care systems. Advances in microelectronics and digital data processing have long enabled the non-invasive and uninterrupted collection of movement data over long periods of time in the sports and recreation fields. In the medical field for movement disorders, this technology promises to improve both research approaches and clinical care. Continuous monitoring of symptoms could enable detection of Parkinson’s symptoms per se, detect therapy response, or support indications for intervention or therapy escalation based on objective data.

Specifically, in this paper we will address the question of where we stand in terms of describing Dopa-sensitive Parkinson’s symptoms with sensors. In doing so, we will discuss not only the many possibilities but also the challenges that arise with this new technology and that have impeded its widespread application so far. We will end our paper with an outlook that provides recommendations on how to overcome these challenges.

Schlüsselwörter

Parkinson - maschinelles Lernen - Inertialsensoren - Bewegungsanalyse

Key words

Parkinson’s disease - sensor - machine learning - inertial measurement units


Publication History

Article published online:
04 September 2023

© 2023. Thieme. All rights reserved.

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