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Identifying Gait-Related Functional Outcomes in Post-Knee Surgery Patients Using Machine Learning: A Systematic Review

Author

Listed:
  • Christos Kokkotis

    (Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece)

  • Georgios Chalatsis

    (Department of Orthopedic Surgery, Faculty of Medicine, University of Thessaly, 41500 Larissa, Greece)

  • Serafeim Moustakidis

    (AIDEAS OÜ, 10117 Tallinn, Estonia)

  • Athanasios Siouras

    (AIDEAS OÜ, 10117 Tallinn, Estonia
    Department of Computer Science and Biomedical Informatics, School of Science, University of Thessaly, 35131 Lamia, Greece)

  • Vasileios Mitrousias

    (Department of Orthopedic Surgery, Faculty of Medicine, University of Thessaly, 41500 Larissa, Greece)

  • Dimitrios Tsaopoulos

    (Institute for Bio-Economy and Agri-Technology, Center for Research and Technology Hellas, 38333 Volos, Greece)

  • Dimitrios Patikas

    (School of Physical Education and Sports Science at Serres, Aristotle University of Thessaloniki, 62110 Serres, Greece)

  • Nikolaos Aggelousis

    (Department of Physical Education and Sport Science, Democritus University of Thrace, 69100 Komotini, Greece)

  • Michael Hantes

    (Department of Orthopedic Surgery, Faculty of Medicine, University of Thessaly, 41500 Larissa, Greece)

  • Giannis Giakas

    (Department of Physical Education and Sport Science, University of Thessaly, 42100 Trikala, Greece)

  • Dimitrios Katsavelis

    (Department of Exercise Science and Pre-Health Profession, Creighton University, Omaha, NE 68178, USA)

  • Themistoklis Tsatalas

    (Department of Physical Education and Sport Science, University of Thessaly, 42100 Trikala, Greece)

Abstract

Modern lifestyles require new tools for determining a person’s ability to return to daily activities after knee surgery. These quantitative instruments must feature high discrimination, be non-invasive, and be inexpensive. Machine learning is a revolutionary approach that has the potential to satisfy the aforementioned requirements and bridge the knowledge gap. The scope of this study is to summarize the results of a systematic literature review on the identification of gait-related changes and the determination of the functional recovery status of patients after knee surgery using advanced machine learning algorithms. The current systematic review was conducted using multiple databases in accordance with the PRISMA guidelines, including Scopus, PubMed, and Semantic Scholar. Six out of the 405 articles met our inclusion criteria and were directly related to the quantification of the recovery status using machine learning and gait data. The results were interpreted using appropriate metrics. The results demonstrated a recent increase in the use of sophisticated machine learning techniques that can provide robust decision-making support during personalized post-treatment interventions for knee-surgery patients.

Suggested Citation

  • Christos Kokkotis & Georgios Chalatsis & Serafeim Moustakidis & Athanasios Siouras & Vasileios Mitrousias & Dimitrios Tsaopoulos & Dimitrios Patikas & Nikolaos Aggelousis & Michael Hantes & Giannis Gi, 2022. "Identifying Gait-Related Functional Outcomes in Post-Knee Surgery Patients Using Machine Learning: A Systematic Review," IJERPH, MDPI, vol. 20(1), pages 1-12, December.
    • Handle: RePEc:gam:jijerp:v:20:y:2022:i:1:p:448-:d:1016825
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    References listed on IDEAS

    as
    1. David Moher & Alessandro Liberati & Jennifer Tetzlaff & Douglas G Altman & The PRISMA Group, 2009. "Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement," PLOS Medicine, Public Library of Science, vol. 6(7), pages 1-6, July.
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