Infant Development Dysplasia of the Hip (DDH) is a disorder where the hip joint does not form properly. The Graf method and The Femoral Head Coverage (FHC) method are ultrasound-based screening techniques which use anatomical landmarks to classify disease severity and guide treatment. Deep learning has been used for either Graf Classification or for FHC, yet there has been only minimal investigation into combining the two. No work to-date has detected FHC using landmarks alone. This paper develops a model which predicts both Graf Classification and FHC from landmarks only. In this method, Recall (Precision) improved when combining methods compared to FHC and Graf methods alone. Two external datasets were used to evaluate model performance under domain shift. Improvements are needed to generalise the model to new datasets. Since the model encompasses both techniques, it gains a clinical understanding of automated methods for DDH screening and improves clinical use.