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Big Data und maschinelles Lernen bei Prävention und Rehabilitation

Big data and deep learning in preventive and rehabilitation medicine

  • Leitthema
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Zusammenfassung

Die Digitalisierung der Medizin hat zur Beschleunigung der Abläufe und nahezu universellen Verfügbarkeit von Informationen geführt. Verkürzte stationäre Verweildauern erfordern nun auch intelligente und praxistaugliche Systeme in der Rehabilitation und Prävention. Hierzu gehören u. a. die Optimierung von Bewegungsanalysen durch innovative Techniken, z. B. Detektoren mit Druckmessfolien, portable Monitorsysteme, robotergestützte Assistenzsysteme sowie professionelle Präventionsprogramme, die auf reliablen Daten beruhen. Nicht zuletzt bedarf es allerdings auch klinischer Strukturen, um die Möglichkeiten künstlicher Intelligenz (AI) und maschinellen Lernens in der Rehabilitation optimal zu nutzen. Einen Beitrag hierzu können inter- und transdisziplinäre Behandlungsteams wie z. B. das RehaBoard liefern. Durch die zunehmende Einführung von AI in der rehabilitativen Orthopädie und Unfallchirurgie mit der Unterstützung sektorenübergreifender Behandlungspfade hat diese im Gegensatz zu anderen kostenintensiven Bereichen wie beispielsweise der Onkologie, sehr gute Chancen, bei einem Wettbewerb um den größten Patientennutzen („value-based competition“) vergleichsweise gut abzuschneiden.

Abstract

The digitalization in medicine has led to almost universal availability of information to different healthcare professionals and accelerated clinical pathways. Fast-track concepts and short hospital stays require intelligent and practicable systems in preventive and rehabilitation medicine. This includes optimization of movement analysis by innovative tools such as detectors sensing skin movements, portable feedback systems for monitoring, robot-assisted devices, and prevention programs based on reliable data. Finally, clinical structures are needed to exploit the maximal potential of artificial intelligence (AI) and deep learning. One example is the establishment of inter- and transdisciplinary professional teams such as a RehaBoard. In contrast to other cost-intensive disciplines such as oncology, the introduction of AI into rehabilitation orthopedics and trauma surgery with the support of cross-sectoral cooperation has great potential for performing well in patient benefit-orientated competition (value-based competition).

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Abbreviations

AI:

„Artificial intelligence“

DL:

„Deep learning“

FIFA:

Fédération Internationale de Football Association

RAGT:

„Robot-assisted gait training“

SGB:

Sozialgesetzbuch

VKB:

Vorderes Kreuzband

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Author information

Authors and Affiliations

  1. Klinik für Orthopädie und Unfallchirurgie, Universitätsklinikum Essen, Hufelandstraße 55, 45274, Essen, Deutschland

    M. Jäger & C. Mayer

  2. Klinik für Neurologie, Universitätsklinikum Düsseldorf, Düsseldorf, Deutschland

    H. Hefter

  3. Neurologie, MediClin Fachklinik Rhein/Ruhr, Essen, Deutschland

    M. Siebler

  4. Lehrstuhl für Mechanik und Robotik, Universität Duisburg-Essen, Duisburg, Deutschland

    A. Kecskeméthy

Authors
  1. M. Jäger
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  2. C. Mayer
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  3. H. Hefter
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  4. M. Siebler
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  5. A. Kecskeméthy
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Corresponding author

Correspondence to M. Jäger.

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Interessenkonflikt

M. Jäger, C. Mayer, H. Hefter, M. Siebler und A. Kecskeméthy geben an, dass kein Interessenkonflikt besteht.

Dieser Beitrag beinhaltet keine von den Autoren durchgeführten Studien an Menschen oder Tieren.

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Jäger, M., Mayer, C., Hefter, H. et al. Big Data und maschinelles Lernen bei Prävention und Rehabilitation. Orthopäde 47, 826–833 (2018). https://doi.org/10.1007/s00132-018-3603-y

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  • DOI: https://doi.org/10.1007/s00132-018-3603-y

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