Artificial intelligence-driven preoperative CT 3D planning: a narrative review on improving the accuracy of acetabular cup angle and size in total hip arthroplasty

Introduction: Total hip arthroplasty (THA) is a well-established treatment for end-stage hip disorders, yet its success heavily depends on precise acetabular cup positioning and sizing. Conventional planning, based on manual CT interpretation, is time-consuming, operator-dependent, and lacks standardisation, limiting its ability to achieve consistent surgical accuracy. Methods: This narrative review systematically searched PubMed, Web of Science, Cochrane Library, and IEEE Xplore for peer-reviewed studies published between January 2015 and June 2025. We included original research evaluating AI-driven preoperative CT 3D planning for THA, with quantitative outcomes on cup angle or size accuracy. Data were extracted and assessed for methodological quality using standard tools. Results: , size matching accuracy within ±1 size ranged from 80% to 85%, and planning time was reduced by 57% to 70% compared with manual templating. These findings were reproducible across different deep-learning architectures and patient cohorts. Discussion: Although AI planning shows clear benefits in accuracy and efficiency, several challenges remain-including limited generalisability to complex anatomies, susceptibility to image artefacts, and insufficient integration with intraoperative execution. Future research should prioritise multi-centre validation, dynamic functional planning, and seamless clinical workflow integration to translate technological potential into improved patient outcomes.