Principal Component Analysis of Knee Joint Differences Between Bilateral and Unilateral Total Knee Replacement Patients During Level Walking

Long-term gait analysis in patients after total knee arthroplasty: A systematic review and meta-analysis

BACKGROUND

Gait abnormalities have been described in patients after total knee arthroplasty (TKA), leading to the development of inter-joint coordination abnormalities and increased risk of falling. Such impairments have been reported to persist in the long-term, although the majority of studies assessed gait pattern especially in the first months after TKA.

RESEARCH QUESTION

What are the long-term gait impairments in patients after TKA compared to healthy age-matched subjects?

METHODS

A systematic search was conducted on MEDLINE/PubMed, EMBASE, CENTRAL and Scopus databases. Observational studies or randomized controlled trials investigating gait spatial-temporal, kinematic and kinetics parameters in a time-window longer than 6 months in patients with TKA compared to healthy age-matched subjects were included. Methodological quality was assessed using the modified Downs and Black (D&B) checklist and participants' characteristics, surgical procedures details and outcome measures were extracted. Pooled or un-pooled findings were categorized into "6 months - 1 year" and "more than 1 year" timepoint categories.

RESULTS

Twenty-eight studies (976 patients) were included. Overall quality was fair with a mean modified D&B score of 63.5 %. Reduced speed, stride length, cadence and longer stance phase were found in patients when compared to healthy individuals at "6 months - 1 year" follow-up. Spatial-temporal parameters deficits were also found at more than 1 year after TKA, where lower single-limb support and longer double-limb support durations were detected. These impairments occurred in concomitance with decreased knee range of motion along the sagittal and frontal planes and altered kinetic parameters. Hip kinematic and kinetic long-term impairments were also detected after TKA.

SIGNIFICANCE

These findings highlighted long-term gait pattern alterations in patients with TKA compared to age-matched healthy subjects. Future studies should identify interventions able to reduce long-term gait pattern alterations and improve function in patients after TKA.

Machine learning/artificial intelligence in sports medicine: state of the art and future directions

Machine learning (ML) is changing the way health care is practiced and recent applications of these novel statistical techniques have started to impact orthopaedic sports medicine. Machine learning enables the analysis of large volumes of data to establish complex relationships between "input" and "output" variables. These relationships may be more complex than could be established through traditional statistical analysis and can lead to the ability to predict the "output" with high levels of accuracy. Supervised learning is the most common ML approach for healthcare data and recent studies have developed algorithms to predict patient-specific outcome after surgical procedures such as hip arthroscopy and anterior cruciate ligament reconstruction. Deep learning is a higher-level ML approach that facilitates the processing and interpretation of complex datasets through artificial neural networks that are inspired by the way the human brain processes information. In orthopaedic sports medicine, deep learning has primarily been used for automatic image (computer vision) and text (natural language processing) interpretation. While applications in orthopaedic sports medicine have been increasing exponentially, one significant barrier to widespread adoption of ML remains clinician unfamiliarity with the associated methods and concepts. The goal of this review is to introduce these concepts, review current machine learning models in orthopaedic sport medicine, and discuss future opportunities for innovation within the specialty.

Unsupervised machine learning methods and emerging applications in healthcare

Unsupervised machine learning methods are important analytical tools that can facilitate the analysis and interpretation of high-dimensional data. Unsupervised machine learning methods identify latent patterns and hidden structures in high-dimensional data and can help simplify complex datasets. This article provides an overview of key unsupervised machine learning techniques including K-means clustering, hierarchical clustering, principal component analysis, and factor analysis. With a deeper understanding of these analytical tools, unsupervised machine learning methods can be incorporated into health sciences research to identify novel risk factors, improve prevention strategies, and facilitate delivery of personalized therapies and targeted patient care.Level of evidence: I.