Xuan Anran, Chen Haowei, Chen Tianyu, Li Jia, Lu Shilong, Fan Tianxiang, Zeng Dong, Wen Zhibo, Ma Jianhua, Hunter David, Ding Changhai, Zhu Zhaohua
The Second Clinical Medical School, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Clinical Research Centre, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Ther Adv Musculoskelet Dis. 2023 Mar 14;15:1759720X231158198. doi: 10.1177/1759720X231158198. eCollection 2023.
Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.
骨关节炎（OA）是全球最常见的肌肉骨骼疾病，随着人口老龄化，其患病率不断上升。它会导致关节疼痛和残疾，降低生活质量，并给社会医疗服务带来巨大负担。然而，目前的主要诊断方法并不适用于早期诊断骨关节炎患者。在过去几年中，机器学习（ML）在骨关节炎诊断中的应用急剧增加。因此，在这篇综述文章中，我们描述了机器学习在骨关节炎早期诊断中的应用研究进展，讨论了机器学习方法的当前趋势和局限性，并提出了未来在骨关节炎领域应用这些工具的研究重点。基于机器学习的准确预测模型与对骨关节炎临床特征出现之前的早期变化敏感的成像技术相结合，有望解决当前的困境。结合多维信息的融合模型的诊断能力使未来对骨关节炎患者进行个性化早期诊断和预后评估成为可能。
