On model deployment, ideally deep learning models should be able learn continuously from new data, but data privacy concerns in medical imaging do not allow for ready sharing of training data. Retraining with incremental data generally leads to catastrophic forgetting. In this study, we evaluated the performance of a knee plane prescription model by retraining with incremental data from a new site. Increasing the number of incoming training data sets and transfer learning significantly improved test performance. We suggest that partial retraining and distributed learning frameworks may be more suitable for retraining of incremental data.