The application of machine learning in early diagnosis of osteoarthritis: a narrative review.
The application of machine learning in early diagnosis of osteoarthritis: a narrative review.
复制标题
DOI：
10.1177/1759720x231158198
复制
发表时间：
2023
影响因子：
4.2
通讯作者：
Zhu, Zhaohua
中科院分区：
文献类型：
--
作者：
Xuan, Anran;Chen, Haowei;Chen, Tianyu;Li, Jia;Lu, Shilong;Fan, Tianxiang;Zeng, Dong;Wen, Zhibo;Ma, Jianhua;Hunter, David;Ding, Changhai;Zhu, Zhaohua
骨关节炎是世界范围内最常见的肌肉骨骼疾病，随着年龄的增长，发病率越来越高。它会导致关节疼痛和残疾，生活质量下降，并给社会的医疗服务带来巨大负担。但目前的主要诊断方法并不适用于骨性关节炎患者的早期诊断。在过去的几年里，机器学习(ML)在OA诊断中的应用急剧增加。因此，本文就ML在OA早期诊断中的应用研究进展进行综述，讨论ML方法的发展趋势和局限性，并提出在OA领域应用ML工具的未来研究重点。准确的基于ML的预测模型，加上成像技术，在临床特征出现之前对OA的早期变化敏感，有望解决目前的困境。融合多维信息的融合模型的诊断能力使未来可能对患者进行特定的早期诊断和预后评估。
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.