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Background: Ankle joint functional assessment tool (AJFAT) is gradually becoming a popular tool for diagnosing functional ankle instability (FAI). However, due to the lack of standard Chinese versions of AJFAT and reliability and validity tests, the use of AJFAT in the Chinese population is limited. This study aimed to translate and cross-culturally adapt the AJFAT from English into Chinese, and evaluate the reliability and validity of the Chinese version of AJFAT and to investigate its psychometric properties. Methods: The translation and cross-cultural adaptation of AJFAT was performed according to guidelines for cross-cultural adaptation of self-report measures. 126 participants with a history of ankle sprain completed the AJFAT-C twice within 14 days and completed the Cumberland ankle instability tool (CAIT-C) once. Test–retest reliability, internal consistency, ceiling and floor effects, convergent and structure validity and discriminative ability were investigated. Results: The test–retest reliability (ICC = 0.91, 95%CI = 0.87–0.94) and internal consistency (Cronbach's alpha = 0.87) of the AJFAT-C were excellent. No ceiling or floor effects were detected. A moderate correlation between the AJFAT-C and the CAIT-C suggested a moderate convergent validity. The AJFAT-C had a two-factor structure: 1. function of the unstable side of the ankle joint (9 items) and 2. symptoms of the unstable side of the ankle (2 items). The ideal cut-off point of the AJFAT-C was calculated as 26 points. Conclusion: The Chinese version of AJFAT can be considered as a valid and reliable ankle joint function evaluation tool that can be applied in clinical and research work. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]