Understanding the role of machine learning in predicting progression of osteoarthritis.
Journal
The bone & joint journal
ISSN: 2049-4408
Titre abrégé: Bone Joint J
Pays: England
ID NLM: 101599229
Informations de publication
Date de publication:
01 Nov 2024
01 Nov 2024
Historique:
medline:
1
11
2024
pubmed:
1
11
2024
entrez:
31
10
2024
Statut:
epublish
Résumé
Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice.
Identifiants
pubmed: 39481441
doi: 10.1302/0301-620X.106B11.BJJ-2024-0453.R1
pii: BJJ-2024-0453.R1
doi:
Types de publication
Journal Article
Systematic Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
1216-1222Subventions
Organisme : Louis and Valerie Freedman Studentship in Medical Sciences
Organisme : ORUK/Versus Arthritis
Organisme : Addenbrooke's Charitable Trust, Cambridge University Hospitals
Organisme : NIHR Academic Clinical Fellowship in Trauma and Orthopaedics
Organisme : Geoffrey Fisk Studentship
Organisme : NIHR Cambridge Biomedical Research Centre
Organisme : Versus Arthritis
Pays : United Kingdom
Organisme : UK Regenerative Medicine Platform
Organisme : AstraZeneca
Organisme : GlaxoSmithKline
Informations de copyright
© 2024 Castagno et al.
Déclaration de conflit d'intérêts
S. Castagno is supported by the Louis and Valerie Freedman Studentship in Medical Sciences from Trinity College Cambridge, the Orthopaedic Research UK (ORUK) / Versus Arthritis: AI in MSK Research Fellowship (G124606), the Addenbrooke’s Charitable Trust (ACT) Research Advisory Committee grant (G123290), and NIHR Academic Clinical Fellowship in Trauma and Orthopaedics ((ACF-2021-14-003)). B. Gompels is supported by the Geoffrey Fisk Studentship from Darwin College Cambridge. A. McCaskie and M. Birch are supported by the NIHR Cambridge Biomedical Research Centre (BRC) (NIHR203312) and receive funding from Versus Arthritis (grant 21156) and UKRMP (grant MR/R015635/1). M. Birch is also a member of the editorial board of The Bone & Joint Journal. M. van der Schaar reports funding from AstraZeneca and GSK, related to this study.
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