Movement Biomechanics, Institute of Sport Sciences, Humboldt-Universität zu Berlin, Unter Den Linden 6, 10099, Berlin, Germany.
Department of Kinesiology, Iowa State University, Ames, 50011, IA, USA.
BMC Musculoskelet Disord. 2023 Feb 1;24(1):86. doi: 10.1186/s12891-023-06195-2.
To determine the current evidence on artificial neural network (ANN) in prognostic studies of musculoskeletal diseases (MSD) and to assess the accuracy of ANN in predicting the prognosis of patients with MSD. The scoping review was reported under the Preferred Items for Systematic Reviews and the Meta-Analyses extension for Scope Reviews (PRISMA-ScR). Cochrane Library, Embase, Pubmed, and Web of science core collection were searched from inception to January 2023. Studies were eligible if they used ANN to make predictions about MSD prognosis. Variables, model prediction accuracy, and disease type used in the ANN model were extracted and charted, then presented as a table along with narrative synthesis. Eighteen Studies were included in this scoping review, with 16 different types of musculoskeletal diseases. The accuracy of the ANN model predictions ranged from 0.542 to 0.947. ANN models were more accurate compared to traditional logistic regression models. This scoping review suggests that ANN can predict the prognosis of musculoskeletal diseases, which has the potential to be applied to different types of MSD.
确定人工神经网络（ANN）在肌肉骨骼疾病（MSD）预后研究中的现有证据，并评估 ANN 在预测 MSD 患者预后方面的准确性。本 scoping 综述按照系统评价和荟萃分析扩展的首选项目（PRISMA-ScR）进行报告。从成立到 2023 年 1 月，检索了 Cochrane Library、Embase、Pubmed 和 Web of Science 核心合集。如果研究使用 ANN 对 MSD 预后进行预测，则符合纳入标准。提取并绘制了 ANN 模型中使用的变量、模型预测准确性和疾病类型，并以表格形式呈现，同时进行叙述性综合。本 scoping 综述纳入了 18 项研究，涉及 16 种不同类型的肌肉骨骼疾病。ANN 模型预测的准确性范围为 0.542 至 0.947。与传统的逻辑回归模型相比，ANN 模型的预测更为准确。本 scoping 综述表明，ANN 可以预测肌肉骨骼疾病的预后，这有可能应用于不同类型的 MSD。
