Predicting Knee Osteoarthritis Severity from Radiographic Predictors: Data from the Osteoarthritis Initiative.
Authors
Affiliations (6)
Affiliations (6)
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland. [email protected].
- Diagnostic Imaging Centre, Kuopio University Hospital, The Wellbeing Services County of North Savo, Kuopio, Finland. [email protected].
- Department of Technical Physics, University of Eastern Finland, Kuopio, Finland.
- Science Service Center, Kuopio University Hospital, The Wellbeing Services County of North Savo, Kuopio, Finland.
- AI Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland.
- Diagnostic Imaging Centre, Kuopio University Hospital, The Wellbeing Services County of North Savo, Kuopio, Finland.
Abstract
In knee osteoarthritis (KOA) treatment, preventive measures to reduce its onset risk are a key factor. Among individuals with radiographically healthy knees, however, future knee joint integrity and condition cannot be predicted by clinically applicable methods. We investigated if knee joint morphology derived from widely accessible and cost-effective radiographs could be helpful in predicting future knee joint integrity and condition. We combined knee joint morphology with known risk predictors such as age, height, and weight. Baseline data were utilized as predictors, and the maximal severity of KOA after 8 years served as a target variable. The three KOA categories in this study were based on Kellgren-Lawrence grading: healthy, moderate, and severe. We employed a two-stage machine learning model that utilized two random forest algorithms. We trained three models: the subject demographics (SD) model utilized only SD; the image model utilized only knee joint morphology from radiographs; the merged model utilized combined predictors. The training data comprised an 8-year follow-up of 1222 knees from 683 individuals. The SD- model obtained a weighted F1 score (WF1) of 77.2% and a balanced accuracy (BA) of 65.6%. The Image-model performance metrics were lowest, with a WF1 of 76.5% and BA of 63.8%. The top-performing merged model achieved a WF1 score of 78.3% and a BA of 68.2%. Our two-stage prediction model provided improved results based on performance metrics, suggesting potential for application in clinical settings.