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

Christos Kokkotis, Georgios Chalatsis, Serafeim Moustakidis, Athanasios Siouras, Vasileios Mitrousias, Dimitrios Tsaopoulos, Dimitrios Patikas, Nikolaos Aggelousis, Michael Hantes, Giannis Giakas, Dimitrios Katsavelis and Themistoklis Tsatalas ()
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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
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

IJERPH, 2022, vol. 20, issue 1, 1-12

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.

Keywords: artificial intelligence; knee surgery; post-operative; walking; biomechanical data (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
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