Clinical Application of Artificial Intelligence Preoperative Planning System Combined with Expert Database Retrieval in Complex Revision Hip Surgery

Summary

This study proposes a novel artificial intelligence preoperative planning approach based on expert surgical case database retrieval in revision hip arthroplasty. Additionally, the technique was initially employed in five patients, exhibiting a reduction in operative time and intraoperative hemorrhage.

Abstract

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.

Introduction

The increasing prevalence of primary total hip arthroplasty (THA) has led to a corresponding rise in the necessity for revision arthroplasty due to a number of factors, including aseptic loosening, infection, recurrent dislocation, and periprosthetic fracture¹. Compared to primary hip arthroplasty, revision hip surgery is a more technically complex and clinically challenging procedure, with higher mortality rates², higher healthcare costs³, and greater complication risks⁴.

In revision hip arthroplasty, the reconstruction of acetabular bone loss and the selection of prosthesis are paramount in determining the success of the surgery. The orthopedic surgeon needs to assess the residual bone stock and the altered anatomy, aiming for adequate initial stability of the newly implanted acetabular cup¹. Consequently, precise preoperative planning is crucial to guide available treatment options.

Currently, orthopedic surgeons are responsible for conducting a comprehensive assessment and planning of revision arthroplasty based on preoperative imaging findings and their own surgical experience. Nevertheless, this will present a significant challenge for the inexperienced surgeon.

With the development of artificial intelligence (AI) technology, it has been increasingly used in orthopedic surgery, primarily for image segmentation, diagnosis, and classification of pathologies and implants⁵. Meanwhile, AI is beginning to achieve initial success in assisting primary THA⁶. However, intelligent preoperative planning for revision hip arthroplasty remains a blank slate. AI has a promising future in hip revision surgery, particularly in the assessment of bone defects. These defects are unique to each patient, and while they exhibit certain patterns, the traditional Paprosky classification method lacks the precision required to fully characterize them. Nevertheless, AI is capable of extracting more detailed information from image data, offering a promising avenue for enhancing the accuracy and precision of bone defect assessment. We developed a novel AI-assisted preoperative planning system to guide orthopedic surgeons' decisions about revision arthroplasty based on expert surgical case database retrieval.

We first established a novel method for acetabular bone defect reconstruction, quantifying and typing acetabular bone defects. Subsequently, we constructed a hip revision case database by collecting clinical and imaging data on 200 hip revision surgical cases from a senior national expert. The database consists of preoperative computed tomography (CT), preoperative X-Ray, postoperative X-ray, and patient demographics. We can match cases in the database based on the current bone defect characteristics of patients scheduled for surgery and find the most similar case scenarios to provide the surgeon with a preoperative reference. This approach allows the surgeon to have a preoperative idea of the acetabular revision protocol, reducing the intraoperative trial and error time.

Protocol

The study received permission from the Ethics Committee of Luoyang Orthopedic-Traumatological Hospital of Henan Province. Additionally, this study was based on imaging data and would not harm the volunteers or disclose their information. Therefore, by national legislation and institutional requirements, there was no need for participants or their legal guardians/next of kin to sign an informed consent form.

1. Image import

1. Import the original CT data of the patient's bilateral total hip. Open the medical image processing software, select Import Data and click Local Data to import the original CT data of the patient. Figure 1 shows the image import interface.

2. Recovery of acetabular bone defect on the affected side

1. Determine whether the patient has a unilateral or bilateral lesion. Select the Measurement tool and use the mouse to draw a rectangular box on the image, directly measuring the HU (Hounsfield Unit, HU) value of the hip bone area. Consider metal present when the HU value exceeds 1000 and designate that area as the affected hip joint.

3. Image segmentation and reconstruction

1. Select the image data of the patient to plan. Click the Automatic Segmentation option, the CT value of 400 is selected by default for hip bone coarse segmentation.
2. Select the Edit button to manually adjust the segmentation results of each part of each layer. Then, select the 3D Reconstruction option to complete the 3D reconstruction calculation of the patient's site. The reconstructed 3D model mainly includes the left/right hip bones and the left/right femur regions. As shown in Figure 2, different parts are displayed with different colors.

4. Acetabular bone defect partition and defect amount calculation

1. For patients with unilateral hip surgery, select the 3D Model of the Healthy Side. Use the mirror function to align it with the anatomical structure of the affected side. This will complete the reconstruction of the hip joint restoration model.
2. For patients with bilateral hip surgery, open the standard model library and select the Hip Model that is most similar to the patient's anatomy as the restoration model.
3. Click the Partition option to divide the 3D model of the hip defect (affected side) reconstructed in step 2 and the 3D model of the hip restored in step 3. Then, click the Difference Operation option to perform the difference operation on the two partitioned hip bone models to calculate the acetabular defect. The hip bone model will be divided into different regions according to Qin's method⁷, as shown in Figure 3.
   NOTE: Due to the focus on 3-Point fixation (rami ischii, rami ossis pubis, and anterosuperior part of the acetabulum) of the acetabular cup within the boundaries of the acetabular wall, the acetabular was divided into 3 equal parts every 120°, defined as the cranial roof, anterior column, and posterior column, excluding the medial wall. To guide surgical procedures, fabricate the sub-layers based on the acetabulum diameter. It was determined that the inner layer must lie between 54 mm and 62 mm and the outer layer must lie between 62 mm and 70 mm for men. The inner layer lie between 50 mm and 58 mm and the outer layer lie between 58 mm and 66 mm for women.

5. Expert hip revision database search - acetabular defect planning

1. Open the expert case base and use the calculated acetabular defect volume as described above as input for retrieval. Select the measurement tools to assess the relevant planning parameters on the preoperative and postoperative X-ray films of the retrieved cases and obtain the planning data.
2. Open the expert case base and enter the type and amount of hip defect for automatic retrieval. The corresponding cases' information in the database will be obtained, including preoperative and postoperative images of similar cases and the doctor's planning parameters (implant size, position, etc.).
3. The search results will be presented in order of similarity from highest to lowest. Let the doctor select the most similar cases. The complete procedure is shown in Figure 4.
4. If a similar case cannot be found in the above database, adopt the result of the model reconstruction from step 2, using the center of the intact acetabulum as the center of the acetabular cup. By default, the ante version angle of the acetabular cup is 20°, and the abduction angle is 40° (this rule is clinically defined).
   NOTE: After planning, the surgeon is able to anticipate the surgical procedure and prepare the prosthesis and supporting instruments with greater precision. This approach eliminates the possibility of encountering a situation where the correct prosthesis is not available during the operation. Moreover, the recommended solution is employed directly during the operation, obviating the need for alternative solutions, detours, or protracted trial and error processes.

Results

Currently, we applied this method in five cases of patients who underwent revision hip arthroplasty, including four men and one woman. The ages ranged from 42 to 67 years. They were diagnosed as aseptic prosthesis loosening after hip arthroplasty and classified based on the Paprosky classification⁸. The mean operative time for the five patients was 123.2 min, with a mean intraoperative blood loss of 672 mL. The operative time is the overall time, including femoral stem prosthesis revision. The det...

Discussion

Due to significant anatomical damage, the intricate soft tissue condition after hip arthroplasty, and the presence of severe metal artifacts often associated with metal implants, it is frequently necessary for experienced medical professionals to utilize 3D reconstruction to comprehensively analyze imaging results and clinical manifestations in order to evaluate specific bone defects in patients and subsequently plan suitable acetabular prostheses^9,10. However, e...

Materials

Name	Company	Catalog Number	Comments
PyCharm	JetBrains	243.21565.199	The Python IDE for data science and web development