ArticlePloS one2016
Simple Scoring System and Artificial Neural Network for Knee Osteoarthritis Risk Prediction: A Cross-Sectional Study.
Article in PloS one, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it, 85 citations in OpenAlex.
- Prevalence of knee osteoarthritis in the Chinese population, 2013-2023: a systematic review and meta-analysis.Journal of orthopaedic surgery and research · 2025Pooled it
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- Constructing machine learning-based risk prediction model for osteoarthritis in population aged 45 and above: NHANES 2011-2018.Scientific reports · 2025Article
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- A joint multi-modal learning method for early-stage knee osteoarthritis disease classification.Heliyon · 2023Article
- Prediction Models for Knee Osteoarthritis: Review of Current Models and Future Directions.The archives of bone and joint surgery · 2023Review
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- Automatic detection and classification of knee osteoarthritis using deep learning approach.La Radiologia medica · 2022Article
- Explainable machine learning for knee osteoarthritis diagnosis based on a novel fuzzy feature selection methodology.Physical and engineering sciences in medicine · 2022Article
- Design of an artificial neural network to predict mortality among COVID-19 patients.Informatics in medicine unlocked · 2022Article
- Developing an artificial neural network for detecting COVID-19 disease.Journal of education and health promotion · 2022Article
- The Application of Artificial Intelligence in Football Risk Prediction.Computational intelligence and neuroscience · 2022Article
- Externally validated models for first diagnosis and risk of progression of knee osteoarthritis.PloS one · 2022Observational
- Association between urinary metals and leukocyte telomere length involving an artificial neural network prediction: Findings based on NHANES 1999-2002.Frontiers in public health · 2022Article
- A siamese network with adaptive gated feature fusion for individual knee OA features grades prediction.Scientific reports · 2021Article
- Prognostic models for knee osteoarthritis: a protocol for systematic review, critical appraisal, and meta-analysis.Systematic reviews · 2021Article
- Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis.Frontiers in pharmacology · 2021Review
- Healthcare Data-Based Prediction Algorithm for Potential Knee Joint Injury of Football Players.Journal of healthcare engineering · 2021Article
- Long-term PMScientific reports · 2020Article
- Machine learning in knee osteoarthritis: A review.Osteoarthritis and cartilage open · 2020Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKnee 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.
methodsThe 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.
resultsThe 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).
conclusionsThe 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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