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

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 pr...
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Autor*in:	Baek, In-Woon [verfasserIn] Jung, Seung Min Park, Yune-Jung Park, Kyung-Su Kim, Ki-Jo

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Axial spondyloarthritis Radiographic progression Artificial neural network Quantitative prediction Real-world setting

Anmerkung:	© The Author(s) 2023

Übergeordnetes Werk:	Enthalten in: Arthritis Research & Therapy - London : BioMed Central, 1999, 25(2023), 1 vom: 20. Apr.
Übergeordnetes Werk:	volume:25 ; year:2023 ; number:1 ; day:20 ; month:04

Links:	Volltext

DOI / URN:	10.1186/s13075-023-03050-6

Katalog-ID:	SPR050128337

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520			\|a 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 R2 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.
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allfields	10.1186/s13075-023-03050-6 doi (DE-627)SPR050128337 (SPR)s13075-023-03050-6-e DE-627 ger DE-627 rakwb eng Baek, In-Woon verfasserin aut Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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 R2 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. Axial spondyloarthritis (dpeaa)DE-He213 Radiographic progression (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Quantitative prediction (dpeaa)DE-He213 Real-world setting (dpeaa)DE-He213 Jung, Seung Min aut Park, Yune-Jung aut Park, Kyung-Su aut Kim, Ki-Jo aut Enthalten in Arthritis Research & Therapy London : BioMed Central, 1999 25(2023), 1 vom: 20. Apr. (DE-627)326646418 (DE-600)2041668-4 1478-6354 nnns volume:25 year:2023 number:1 day:20 month:04 https://dx.doi.org/10.1186/s13075-023-03050-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2023 1 20 04
spelling	10.1186/s13075-023-03050-6 doi (DE-627)SPR050128337 (SPR)s13075-023-03050-6-e DE-627 ger DE-627 rakwb eng Baek, In-Woon verfasserin aut Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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 R2 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. Axial spondyloarthritis (dpeaa)DE-He213 Radiographic progression (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Quantitative prediction (dpeaa)DE-He213 Real-world setting (dpeaa)DE-He213 Jung, Seung Min aut Park, Yune-Jung aut Park, Kyung-Su aut Kim, Ki-Jo aut Enthalten in Arthritis Research & Therapy London : BioMed Central, 1999 25(2023), 1 vom: 20. Apr. (DE-627)326646418 (DE-600)2041668-4 1478-6354 nnns volume:25 year:2023 number:1 day:20 month:04 https://dx.doi.org/10.1186/s13075-023-03050-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2023 1 20 04
allfields_unstemmed	10.1186/s13075-023-03050-6 doi (DE-627)SPR050128337 (SPR)s13075-023-03050-6-e DE-627 ger DE-627 rakwb eng Baek, In-Woon verfasserin aut Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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 R2 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. Axial spondyloarthritis (dpeaa)DE-He213 Radiographic progression (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Quantitative prediction (dpeaa)DE-He213 Real-world setting (dpeaa)DE-He213 Jung, Seung Min aut Park, Yune-Jung aut Park, Kyung-Su aut Kim, Ki-Jo aut Enthalten in Arthritis Research & Therapy London : BioMed Central, 1999 25(2023), 1 vom: 20. Apr. (DE-627)326646418 (DE-600)2041668-4 1478-6354 nnns volume:25 year:2023 number:1 day:20 month:04 https://dx.doi.org/10.1186/s13075-023-03050-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2023 1 20 04
allfieldsGer	10.1186/s13075-023-03050-6 doi (DE-627)SPR050128337 (SPR)s13075-023-03050-6-e DE-627 ger DE-627 rakwb eng Baek, In-Woon verfasserin aut Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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 R2 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. Axial spondyloarthritis (dpeaa)DE-He213 Radiographic progression (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Quantitative prediction (dpeaa)DE-He213 Real-world setting (dpeaa)DE-He213 Jung, Seung Min aut Park, Yune-Jung aut Park, Kyung-Su aut Kim, Ki-Jo aut Enthalten in Arthritis Research & Therapy London : BioMed Central, 1999 25(2023), 1 vom: 20. Apr. (DE-627)326646418 (DE-600)2041668-4 1478-6354 nnns volume:25 year:2023 number:1 day:20 month:04 https://dx.doi.org/10.1186/s13075-023-03050-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2023 1 20 04
allfieldsSound	10.1186/s13075-023-03050-6 doi (DE-627)SPR050128337 (SPR)s13075-023-03050-6-e DE-627 ger DE-627 rakwb eng Baek, In-Woon verfasserin aut Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2023 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 R2 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. Axial spondyloarthritis (dpeaa)DE-He213 Radiographic progression (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Quantitative prediction (dpeaa)DE-He213 Real-world setting (dpeaa)DE-He213 Jung, Seung Min aut Park, Yune-Jung aut Park, Kyung-Su aut Kim, Ki-Jo aut Enthalten in Arthritis Research & Therapy London : BioMed Central, 1999 25(2023), 1 vom: 20. Apr. (DE-627)326646418 (DE-600)2041668-4 1478-6354 nnns volume:25 year:2023 number:1 day:20 month:04 https://dx.doi.org/10.1186/s13075-023-03050-6 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 25 2023 1 20 04
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author	Baek, In-Woon
spellingShingle	Baek, In-Woon misc Axial spondyloarthritis misc Radiographic progression misc Artificial neural network misc Quantitative prediction misc Real-world setting Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting
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topic_title	Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting Axial spondyloarthritis (dpeaa)DE-He213 Radiographic progression (dpeaa)DE-He213 Artificial neural network (dpeaa)DE-He213 Quantitative prediction (dpeaa)DE-He213 Real-world setting (dpeaa)DE-He213
topic	misc Axial spondyloarthritis misc Radiographic progression misc Artificial neural network misc Quantitative prediction misc Real-world setting
topic_unstemmed	misc Axial spondyloarthritis misc Radiographic progression misc Artificial neural network misc Quantitative prediction misc Real-world setting
topic_browse	misc Axial spondyloarthritis misc Radiographic progression misc Artificial neural network misc Quantitative prediction misc Real-world setting
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title	Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting
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title_full	Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting
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author_browse	Baek, In-Woon Jung, Seung Min Park, Yune-Jung Park, Kyung-Su Kim, Ki-Jo
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title_sort	quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting
title_auth	Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting
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 R2 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. © The Author(s) 2023
abstractGer	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 R2 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. © The Author(s) 2023
abstract_unstemmed	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 R2 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. © The Author(s) 2023
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title_short	Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting
url	https://dx.doi.org/10.1186/s13075-023-03050-6
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author2	Jung, Seung Min Park, Yune-Jung Park, Kyung-Su Kim, Ki-Jo
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Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting

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