Developmental hip dysplasia (DDH) is a disease in which the hip joint fails to develop normally due to various causes before, during or after birth. The most important method used for the detection of DDH is hip ultrasonography. The stage of obtaining the hip US image varies because it depends on the operator and external influences. In this study, an artificial intelligence-based system has been developed to eliminate this variability. The developed system includes a 2-stage deep learning model. The main purpose of the system is to automatically determine whether the US images obtained by physicians are suitable for the calculation of alpha and beta angles required for diagnosis. The system uses the U-NET architecture in the first stage and the masked region-based convolutional neural network (MBT-ESA) architecture in the second stage. For the training, 540 images were taken from Selçuk University Faculty of Medicine hospital with the approval of the ethics committee. A total of 840 images were obtained for training with data augmentation. U-NET architecture training resulted in an accuracy of 0.93 and region-based convolutional neural network training with mask resulted in an accuracy of 0.96. The overall system accuracy was calculated as 0.96. The results obtained in this study suggest that by increasing the number of real-time tests and images, the inter-operator variability in the diagnosis of DDH can be eliminated. Translated with DeepL.com (free version).