Clinical Application of Artificial Intelligence Preoperative Planning System Combined with Expert Database Retrieval...

Accurate preoperative planning in revision hip arthroplasty is crucial for achieving successful outcomes. To enhance the intuitive evaluation of acetabular bone defect severity and leverage previous successful experience in revision hip arthroplasty, this study proposes a novel approach based on expert surgical case database retrieval and is initially implemented in clinical application. In this study, five patients who required revision hip arthroplasty were preoperatively planned to employ the expert case database surgical planning system.The patient's imaging data was entered into the system and matched with cases in the expert case database. Based on the expert's surgical experience, a revision surgery plan was recommended. If no suitable case was found, the model and position of the prosthesis were planned based on patient-specific reconstruction results. A total of five patients were enrolled in this study, four males and one female, with a mean age of 50.6 years. The diagnosis was aseptic prosthesis loosening after hip arthroplasty. The mean operative time was 123.2 min, and the mean intraoperative hemorrhage was 672 mL. No intraoperative complications, such as vascular or nerve injury, were observed. In Case 2, for instance, the application of this innovative planning scheme enabled the surgeon to delineate the revision surgery plan for this patient in the preoperative period, thereby reducing the operative time and intraoperative hemorrhage. Furthermore, patients could be apprised of the outcomes of analogous cases in advance. Leveraging a big data analysis approach through our comprehensive case database enables automated identification of matching expert treatment plans throughout the entire process. This particularly benefits inexperienced orthopedic surgeons by providing accurate guidance on surgical strategies to assist them in selecting appropriate prosthetic sizes and mounting positions. Additionally, the matching results can offer patients visualizations depicting predicted postoperative outcomes.