Use of artificial neural networks in the prognosis of musculoskeletal diseases—a scoping review

• Published in: BMC musculoskeletal disorders Vol. 24; no. 1; pp. 86 - 11
• Main Authors: Qiu, Fanji, Li, Jinfeng, Zhang, Rongrong, Legerlotz, Kirsten
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 01.02.2023; BioMed Central; BMC
• Subjects: Accuracy; Analysis; Artificial intelligence; Care and treatment; Diagnosis; Health aspects; Humans; Logistic Models; Machine learning; Medical prognosis; Methods; Musculoskeletal diseases; Musculoskeletal Diseases - diagnosis; Neural networks; Neural Networks, Computer; Patients; Prediction; Predictions; Prognosis; Regression analysis; Subject heading schemes; China; United States > US; Musculoskeletal diseases; Machine learning; Prediction
• ISSN: 1471-2474; 1471-2474
• 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.