Aims 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. Methods 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. Results 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. Conclusion 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.

中文翻译：

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了解机器学习在预测骨关节炎进展中的作用。


目标机器学习 （ML） 是人工智能的一个分支，它使用算法从数据中学习并做出预测，为实现更加个性化和量身定制的手术治疗提供了途径。这种方法与骨关节炎 （OA） 等常见关节疾病特别相关。与关节置换术提供出色效果的终末期疾病相比，OA 的早期阶段目前缺乏阻止或逆转进展的有效疗法。如果要制定及时的干预措施，以加强患者护理并优化临床试验的设计，准确预测 OA 进展至关重要。方法 根据 PRISMA 指南进行系统评价。我们于2024年5月5日检索了MEDLINE和Embase，以查找利用ML预测OA进展的研究。独立筛选标题和摘要，然后对符合纳入标准的研究进行全文评价。提取并综合关键信息进行分析，包括数据类型（如临床、放射学或生化）、OA 进展的定义、ML 算法、验证方法和结果测量。结果 在最初确定的 1,160 项研究中，纳入了 39 项。大多数研究 （85%） 是在 2020 年至 2024 年间发表的，其中 82% 使用公开可用的数据集，主要是骨关节炎倡议。ML 方法主要受到监督，OA 进展的定义存在显著差异：大多数研究侧重于结构变化 （59%），而较少研究涉及疼痛进展或两者兼而有之。44% 的研究使用了深度学习，而 5% 的研究使用了自动化 ML。评估指标缺乏标准化，外部验证有限。 54% 的研究主要使用 SHapley 加法解释来探讨可解释性。结论我们的系统评价证明了 ML 模型预测 OA 进展的可行性，但也揭示了目前限制其临床适用性的关键局限性。未来的优先事项应包括数据源多样化、结果测量标准化、实施严格的验证以及集成更复杂的算法。这种从预测建模到可操作临床工具的范式转变有可能改变骨科实践中的患者护理和疾病管理。