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Artificial intelligence-driven preoperative CT 3D planning: a narrative review on improving the accuracy of
Yan Wang1, Tianlong Wang1, Shuren Wang2
1Graduate School, Heilongjiang University of Chinese Medicine, Harbin, China.
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:
AI-assisted planning consistently improved accuracy: mean angular errors for inclination and anteversion were below 3*, 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.