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Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study

Authors
 Tae Keun Yoo  ;  Deok Won Kim  ;  Soo Beom Choi  ;  Ein Oh  ;  Jee Soo Park 
Citation
 PLoS One, Vol.11(2) : e0148724, 2016 
Journal Title
 PLoS One 
Issue Date
2016
MeSH
Adult ; Aged ; Cross-Sectional Studies ; Diagnostic Self Evaluation ; Early Diagnosis ; Female ; Humans ; Male ; Middle Aged ; Neural Networks (Computer)* ; Nutrition Surveys ; Osteoarthritis, Knee/diagnosis ; Osteoarthritis, Knee/epidemiology ; Osteoarthritis, Knee/etiology* ; Predictive Value of Tests ; Republic of Korea/epidemiology ; Risk Factors
Abstract
BACKGROUND: Knee osteoarthritis (OA) is the most common joint disease of adults worldwide. Since the treatments for advanced radiographic knee OA are limited, clinicians face a significant challenge of identifying patients who are at high risk of OA in a timely and appropriate way. Therefore, we developed a simple self-assessment scoring system and an improved artificial neural network (ANN) model for knee OA. METHODS: The Fifth Korea National Health and Nutrition Examination Surveys (KNHANES V-1) data were used to develop a scoring system and ANN for radiographic knee OA. A logistic regression analysis was used to determine the predictors of the scoring system. The ANN was constructed using 1777 participants and validated internally on 888 participants in the KNHANES V-1. The predictors of the scoring system were selected as the inputs of the ANN. External validation was performed using 4731 participants in the Osteoarthritis Initiative (OAI). Area under the curve (AUC) of the receiver operating characteristic was calculated to compare the prediction models. RESULTS: The scoring system and ANN were built using the independent predictors including sex, age, body mass index, educational status, hypertension, moderate physical activity, and knee pain. In the internal validation, both scoring system and ANN predicted radiographic knee OA (AUC 0.73 versus 0.81, p<0.001) and symptomatic knee OA (AUC 0.88 versus 0.94, p<0.001) with good discriminative ability. In the external validation, both scoring system and ANN showed lower discriminative ability in predicting radiographic knee OA (AUC 0.62 versus 0.67, p<0.001) and symptomatic knee OA (AUC 0.70 versus 0.76, p<0.001). CONCLUSIONS: The self-assessment scoring system may be useful for identifying the adults at high risk for knee OA. The performance of the scoring system is improved significantly by the ANN. We provided an ANN calculator to simply predict the knee OA risk.
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DOI
10.1371/journal.pone.0148724
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Kim, Deok Won(김덕원)
Park, Jee Soo(박지수) ORCID logo https://orcid.org/0000-0001-9976-6599
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URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/146328
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