BACKGROUND: Novel methods for annotating antero-posterior pelvis radiographs and fluoroscopic images with deep-learning models have recently been developed. However, their clinical use has been limited. Therefore, the purpose of this study was to develop a deep learning model that could annotate clinically relevant pelvic landmarks on both radiographic and fluoroscopic images and automate total hip arthroplasty (THA)-relevant measurements. METHODS: A deep learning model was developed using imaging from 161 primary THAs. A combination of preoperative and postoperative antero-posterior pelvis radiographs and intraoperative fluoroscopic images were annotated. A landmark detection model was then designed to annotate pelvis radiographs and fluoroscopic images. The algorithm was used to automate the measurement of pelvic tilt, leg length, offset, acetabular component abduction, and inclination. RESULTS: Our novel deep learning model annotated pelvic landmarks as well, if not better, than trained humans at 16 of 20, four of four, and five of eight landmarks for bony landmarks on radiographs, implant landmarks on radiographs, and bony landmarks on fluoroscopy, respectively. Measurements of cup inclination and anteversion, pelvic tilt, offset, and leg length were successfully calculated. CONCLUSIONS: We have developed a novel deep-learning model that can automate the identification of clinically relevant pelvic landmarks in real time and provide THA-relevant measurements that are equivalent to those of trained humans. We believe the model could be rapidly incorporated into clinical practice for both surgical and research applications.