Improving the predictive performance with semantic segmentation of joint space regions. We compared two types of neural network models with or without the semantic segmentation of joint space regions. We benchmarked their performance in predicting joint space narrowing using a, Pearson’s correlation and b, root mean square error. We also benchmarked their performance in bone erosion prediction using c, Pearson’s correlation and d, root mean square error. In each comparison, we performed 10-fold cross-validation experiments. The one-sided paired Wilcoxon signed-rank tests were used to determine the statistical significance.