Comprehensive Summary
This literature review demonstrates how artificial intelligence (AI) can assist surgeons during total hip arthroplasty (THA) by providing real-time assessments of acetabular component angles using intraoperative radiographs. By utilizing proper positioning of the acetabular components, it ensures long-term implant stability and reduces complications like joint instability and early wear. To develop this AI model, researchers analyzed 268 hips and expanded their dataset to 536 preoperative and intraoperative anteroposterior pelvic radiographs. Key bone landmarks were manually marked and were used to train a machine learning model to estimate anteversion and inclination angles intraoperatively. The AI’s predictions were cross-examined with computed tomography (CT)-based navigation data. Results showcased that the model performed well with mean absolute errors (MAE) of 2.19° (R2 = 0.850) for anteversion and 1.18° (R2 = 0.805) for inclination in the internal test set. The external test set demonstrated slightly lower accuracy but still showed strong performance, with an MAE of 2.78° (R2 = 0.789) for anteversion and 1.56° (R2 = 0.744) for inclination.
Outcomes and Implications
This AI-driven approach has the potential to improve surgical precision in THA by providing real-time guidance during procedures. By minimizing reliance on manual estimation, the model enhances accuracy and consistency, which could lead to fewer postoperative complications and better patient outcomes. The strong external validation results indicate that this technology could be widely applicable in real-world clinical settings. If integrated into surgical workflows, it could assist surgeons in making more informed decisions, ultimately improving implant positioning and reducing the likelihood of revision surgeries. Future research should focus on refining the model’s real-time application and ensuring it performs consistently across diverse patient populations before full clinical implementation.