KI-gestützte Untersuchung in der nicht-operativen Versorgung
                    symptomgebender Erkrankungen des Kniegelenks – ein multiprofessionelles Konzept
                    (KINEESIO)

Elke Schulze; Christoph Palm; Maximilian Kerschbaum; Roman Seidel; Lars Lehmann; Michael Koller; Andrea Pfingsten

doi:10.1055/a-2402-9982

MSK – Muskuloskelettale Physiotherapie, Table of Contents

MSK – Muskuloskelettale Physiotherapie 2024; 28(05): 312-321
DOI: 10.1055/a-2402-9982

Fachwissen

KI-gestützte Untersuchung in der nicht-operativen Versorgung symptomgebender Erkrankungen des Kniegelenks – ein multiprofessionelles Konzept (KINEESIO)

AI-Supported Examination in the Non-Surgical Treatment of Symptomatic Diseases of the Knee Joint – A Multiprofessional Concept (KINEESIO)

Elke Schulze

¹Angewandte Sozial- und Gesundheitswissenschaften, Fachbereich Physiotherapie, Ostbayerische Technische Hochschule Regensburg, Regensburg Center of Health Sciences and Technology (RCHST)

,

Christoph Palm

²Fakultät Informatik und Mathematik, Ostbayerische Technische Hochschule Regensburg, Regensburg Center of Health Sciences and Technology (RCHST)

,

Maximilian Kerschbaum

³Klinik und Poliklinik für Unfallchirurgie, Universitätsklinikum Regensburg (UKR)

,

Roman Seidel

⁴Professur Digital- und Schaltungstechnik, Fakultät für Elektrotechnik und Informationstechnik, Technische Universität Chemnitz

,

Lars Lehmann

⁴Professur Digital- und Schaltungstechnik, Fakultät für Elektrotechnik und Informationstechnik, Technische Universität Chemnitz

,

Michael Koller

⁵Zentrum für Klinische Studien, Universitätsklinikum Regensburg (UKR)

,

Andrea Pfingsten

⁶Professur für Physiotherapie, Ostbayerische Technische Hochschule Regensburg, Regensburg Center of Health Sciences and Technology (RCHST)

› Author Affiliations

Abstract

Zusammenfassung

Beschwerdebilder am Kniegelenk aufgrund muskuloskelettaler degenerativer oder verletzungsbedingter Erkrankungen sind häufig, nehmen im Alter zu und sind mit der steigenden Inanspruchnahme ärztlicher und therapeutischer Behandlungsmaßnahmen verbunden. Einer erfolgreichen Therapie gehen oft notwendige zeit- und ressourcenaufwendige Untersuchungen zur Erkennung und Differenzierung der patient*innenspezifischen Problematik voraus.

Im Zusammenhang mit der nicht-operativen Versorgung des Kniegelenks hat ein sektorübergreifendes multiprofessionelles Forschungsteam ein Konzept entwickelt, um künstliche neuronale Netze so zu trainieren, dass sie bei der ärztlichen und physiotherapeutischen Untersuchung unterstützend Einsatz finden können. Denn gerade in der Erfassung und Auswertung umfassender Datenmengen liegen große Potenziale in der Künstlichen Intelligenz (KI) im Gesundheitswesen.

Das Projekt KINEESIO trainiert und testet KI-gestützte Screening-Tools zur Untersuchung von Patient*innen mit Kniegelenkerkrankungen. Diese unterstützen die Abläufe zwischen Leistungserbringern und Patient*innen, tragen zu einer verbesserten Differenzierung individueller Beschwerdebilder bei und dienen Entscheidungsprozessen für eine adäquate Versorgung. Dadurch sollen Ressourcen im Gesundheitswesen geschont und eine qualitativ hochwertige Therapie ausreichend ermöglicht werden.

Abstract

Complaints regarding knee joints resulting from musculoskeletal degenerative or injury-related conditions are common, increase with age, and are associated with rising demand for treatments by physicians and therapists. The success of a therapeutic intervention is frequently contingent upon necessary time- and resource-intensive examinations to identify and differentiate the patient’s specific issues.

In connection with non-surgical knee joint treatment, a cross-sectoral, multidisciplinary research team has developed a concept for training artificial neural networks to assist in medical and physiotherapeutic examinations. After all, there is great potential for artificial intelligence (AI) in healthcare, particularly in collecting and analyzing large amounts of data.

The KINEESIO project is training and testing screening tools supported by artificial intelligence for the assessment of patients with knee joint disorders. These tools support the workflows between healthcare providers and patients, improve the differentiation of individual symptoms and serve the decision-making process towards appropriate care. This should conserve healthcare resources and enable sufficient high-quality treatment.

Schlüsselwörter

Kniegelenk - Diagnose - Physiotherapie - Künstliche Intelligenz

Keywords

knee joint - diagnosis - physiotherapy - artificial intelligence

Full Text

References

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