title: Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study.
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.
authors: Department of Ophthalmology, Yonsei University College of Medicine, Seoul, Republic of Korea.
authors: Tae Keun
authors: TK
authors: Yoo
authors: Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea.|Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Republic of Korea.
authors: Deok Won
authors: DW
authors: Kim
authors: Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea.|Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Republic of Korea.
authors: Soo Beom
authors: SB
authors: Choi
authors: Department of Anaesthesiology and Pain Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
authors: Ein
authors: E
authors: Oh
authors: Department of Medical Engineering, Yonsei University College of Medicine, Seoul, Republic of Korea.|Department of Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
authors: Jee Soo
authors: JS
authors: Park
mesh_terms: D000328:Adult; D000368:Aged; D003430:Cross-Sectional Studies; D059026:Diagnostic Self Evaluation; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D009749:Nutrition Surveys; D020370:Osteoarthritis, Knee / Q000175:diagnosis / Q000453:epidemiology / Q000209:etiology*; D011237:Predictive Value of Tests; D056910:Republic of Korea / Q000453:epidemiology; D012307:Risk Factors