Predicting knee osteoarthritis severity: comparative modeling based on patient's data and plain X-ray images
This work addresses knee osteoarthritis severity prediction for medical diagnosis, but it is incremental as it applies existing methods to a specific domain without major breakthroughs.
The study tackled predicting knee osteoarthritis severity by comparing models using patient data and X-ray images, finding that CNN, Elastic Net, and Random Forest models achieved root mean squared errors of 0.77, 0.97, and 0.94, respectively, with comparable results between image and data-based approaches.
Knee osteoarthritis (KOA) is a disease that impairs knee function and causes pain. A radiologist reviews knee X-ray images and grades the severity level of the impairments according to the Kellgren and Lawrence grading scheme; a five-point ordinal scale (0--4). In this study, we used Elastic Net (EN) and Random Forests (RF) to build predictive models using patient assessment data (i.e. signs and symptoms of both knees and medication use) and a convolution neural network (CNN) trained using X-ray images only. Linear mixed effect models (LMM) were used to model the within subject correlation between the two knees. The root mean squared error for the CNN, EN, and RF models was 0.77, 0.97, and 0.94 respectively. The LMM shows similar overall prediction accuracy as the EN regression but correctly accounted for the hierarchical structure of the data resulting in more reliable inference. Useful explanatory variables were identified that could be used for patient monitoring before X-ray imaging. Our analyses suggest that the models trained for predicting the KOA severity levels achieve comparable results when modeling X-ray images and patient data. The subjectivity in the KL grade is still a primary concern.