Comprehensive Summary
This literature review explores the development of an artificial intelligence based surgical support model with the goal of estimating acetabular component angles during total hip arthroplasty through intraoperative radiographs. Precise positioning of the acetabular component is necessary in achieving successful surgical outcomes and ensuring the long-term success of hip replacements. Researchers trained the AI model by annotating bone landmarks on intraoperative radiographs and calculating distances and angles between these landmarks. The accuracy of the model was verified by comparing its estimations to angles derived from an intraoperative computed tomography–based navigation system, serving as the benchmark. The developed AI model achieved a record breaking predictive accuracy, with mean absolute errors of 2.78 for anteversion and 1.56 for inclination in external validation, indicating the AI model’s reliability and effectiveness.
Outcomes and Implications
Accurate positioning of the acetabular component during total hip arthroplasty is essential for successful surgical outcomes, which directly affects patient recovery, mobility, and long-term implant success. The significance of this literature review lies in its ability to provide a reliable, objective alternative to manual measurements, which are susceptible to variability and human error. Clinically, the AI model represents a meaningful advancement toward improving surgical precision, potentially reducing operative errors, complications, and enhancing patient outcomes.