Background & Aims
Machine learning (ML) efficiently processes large datasets, showing promise in enhancing clinical practice within physical therapy. It enables precise tailoring of treatments, potentially improving patient outcomes and personalizing care. The aim of this study was to provide an overview of research on the applications of machine learning in the clinical context of physical therapy.
Methods
A scoping review was performed in PubMed, EMBASE, PEDro, Cochrane, Web of Science, and Scopus. We concentrated on primary studies that specifically employed machine learning techniques within clinical contexts of physical therapy. Data were extracted regarding methods, data types, performance metrics, and model availability.
Results
Fourteen studies (1,230 participants) were included. The majority were published after 2018 (n=11). Six studies were in the musculoskeletal physiotherapy domain, four in neurological, and two in pediatric. The models varied, with artificial neural networks (n=5) and convolutional neural networks (n=4) being the most common. Only one study reported on model availability. In the current review, we found several clinical applications for ML-based tools, including diagnosis (n=1), prognosis (n=1), image and video analysis (n=3), clinical decision support (n=3), treatment outcomes prediction(n=3), and patient monitoring (n=2).
Conclusions
This scope review mapped the emerging landscape of machine learning applications in physical therapy. Despite the growing interest, the field still lacks high-quality studies on validation, model availability, and acceptability to progress from research to clinical practice.
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Presenting Author
Matheus Bartholazzi Lugão de Carvalho
Poster Authors
Matheus Bartholazzi
Physical therapy Student
Instituto Federal do Rio de Janeiro
Lead Author
Topics
- Other