Back to Search Start Over

Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting

Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting

Authors :
In-Woon Baek
Seung Min Jung
Yune-Jung Park
Kyung-Su Park
Ki-Jo Kim
Source :
Arthritis Research & Therapy, Vol 25, Iss 1, Pp 1-11 (2023)
Publication Year :
2023
Publisher :
BMC, 2023.

Abstract

Abstract Background Predicting radiographic progression in axial spondyloarthritis (axSpA) remains limited because of the complex interaction between multiple associated factors and individual variability in real-world settings. Hence, we tested the feasibility of artificial neural network (ANN) models to predict radiographic progression in axSpA. Methods In total, 555 patients with axSpA were split into training and testing datasets at a 3:1 ratio. A generalized linear model (GLM) and ANN models were fitted based on the baseline clinical characteristics and treatment-dependent variables for the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) of the radiographs at follow-up time points. The mSASSS prediction was evaluated, and explainable machine learning methods were used to provide insights into the model outcome or prediction. Results The R 2 values of the fitted models were in the range of 0.90–0.95 and ANN with an input of mSASSS as the number of each score performed better (root mean squared error (RMSE) = 2.83) than GLM or input of mSASSS as a total score (RMSE = 2.99–3.57). The ANN also effectively captured complex interactions among variables and their contributions to the transition of mSASSS over time in the fitted models. Structural changes constituting the mSASSS scoring systems were the most important contributing factors, and no detectable structural abnormalities at baseline were the most significant factors suppressing mSASSS change. Conclusions Clinical and radiographic data-driven ANN allows precise mSASSS prediction in real-world settings. Correct evaluation and prediction of spinal structural changes could be beneficial for monitoring patients with axSpA and developing a treatment plan.

Details

Language :
English
ISSN :
14786362
Volume :
25
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Arthritis Research & Therapy
Publication Type :
Academic Journal
Accession number :
edsdoj.6a72376ebba140e3aa31949aadef9381
Document Type :
article
Full Text :
https://doi.org/10.1186/s13075-023-03050-6