The following Vanderbilt-based study proffers a deep-learning solution that has been configured to automate the localization of critical pelvic anatomical features in conjunction with the acetabular component position and clinically applicable values (namely, leg length, offset, and cup positions on radiographs and fluoroscopic images for THA: Total Hip Arthroplasty). The model in question was calibrated through a dataset of 161 THAs by way of a single annotator. Furthermore, it was evaluated through inter-rater reliability (IRR) assessment along with permutation testing. The efficacy of the model was benchmarked against human annotators across various anatomical landmarks, with particular scrutiny on its ability to identify bony and cup landmarks on radiographs and fluoroscopic images. A prime feature of the model is its ability to support swift manual modifications by surgeons. This, in turn, ensures its therapeutic precision. As evidenced, in the generality of instances, the model matched or outperformed human annotators. Specifically in the IRR assessment and permutation testing, the model was shown to perform better than human annotators in several landmark categories. Be that as it may, however, it did still exhibit suboptimal levels in some areas, likely attributable to certain anatomical complexities supplementary to the reduced radiocontrast of certain landmarks. Turning to the box-plot analysis, we saw that the discrepancies were chiefly governed by a small subset of outliers. Nonetheless, such minimal discrepancies did not materially affect clinically pertinent measurements, with the model’s outputs for designated variables like trans-ischial lines being in line with those of human annotators. As a final case in point, the model displayed high computational efficiency, analyzing approximately 1,300 images per minute. As such, it exhibits promising potential for real-time intraoperative application.