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

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.

Tool for osteoarthritis risk prediction (TOARP) over 8 years using baseline clinical data, X‐ray, and MRI: Data from the osteoarthritis initiative

“…While previous studies have developed risk prediction models for OA(34–37), our study is different as it includes MR imaging and assessment of known risk factors for OA development over 8 years in subjects without, or with only mild, radiographic OA and no symptoms of OA at baseline. A variety of OA risk calculators have been developed which range in complexity: Some included only subject demographics, clinical factors, and risk factors without imaging(34), while others integrate biochemical markers and radiography-based KL scores in their OA prediction model(35).…”

“…While previous studies have developed risk prediction models for OA, [34][35][36][37] our study is different, as it includes MRI and assessment of known risk factors for OA development over 8 years in subjects without, or with only mild, radiographic OA and no symptoms of OA at baseline. A variety of OA risk calculators have been developed that Two key features of the models developed in this study are 1) individualized assessments and 2) inclusion of advanced MRI.…”

Automatic Detection of Knee Joints and Quantification of Knee Osteoarthritis Severity Using Convolutional Neural Networks

“…al. used artificial neural networks (ANN) and KNHANES V-1 data, and developed a scoring system to predict radiographic and symptomatic knee OA [20] risks. Shamir et.…”

“…Previous work has approached automatically assessing knee OA severity [14,17,20] as an image classification problem. In this work, we train CNNs from scratch to automatically quantify knee OA severity using X-ray images.…”

Prognostic Models for Knee Osteoarthritis: A Protocol for Systematic Review, Critical Appraisal and Meta-Analysis

“…Although the etiology of KOA has not been fully elucidated, a combination of risk factors is deemed to be related to this disease [4,5], which allows the establishment of KOA risk prediction models. The evolving understanding of pathophysiological aspect of KOA [2,3] is paralleled by improvements in prediction models, from only considering limited factors to a model combined clinical, genetic, biochemical and imaging information [6][7][8][9][10]. Prognostic models that showed moderate performance in evaluating KOA risk in general population may serve as a potential applicable tool for clinicians to stratify individuals by their risk level to provide suitable prevention strategy.…”