Automated Acetabular Defect Reconstruction and Analysis for Revision Total Hip Arthroplasty: A Computational Modeling...

Area of Science:

• Orthopedic surgery
• Biomedical engineering
• Computational modeling

Background:

• Revision total hip arthroplasty (rTHA) with large acetabular defects has high failure rates, often due to cup loosening.
• Current classification systems rely on planar radiographs, lacking 3D geometric detail crucial for complex defects.

Purpose of the Study:

• To develop an automated computational pipeline for rapid 3D reconstruction of acetabular bone defects in rTHA.
• To quantify defect geometry using volumetric and depth measurements and assess their correlation with existing classification systems.

Main Methods:

• Utilized artificial neural network segmentation of pelvic CT scans for defect identification.
• Employed statistical shape modeling for 3D defect reconstruction in 60 rTHA patients.
• Calculated absolute defect volume (ADV), relative defect volume (RDV), and defect depth (DD), stratifying by Paprosky classification.

Main Results:

• The automated pipeline achieved high accuracy, with a mean Dice coefficient of 0.827 and a mean relative volume error of 16.4% compared to manual models.
• Quantitative defect measures generally increased with Paprosky classification severity.
• Significant differences in defect metrics were primarily observed between higher-grade defects (3B) and lower/intermediate grades (2B-2C), indicating limitations in current grading for precise volumetric assessment.

Conclusions:

• The automated 3D modeling pipeline provides a rapid and validated method for characterizing acetabular bone defects in rTHA.
• Quantitative defect metrics derived from 3D models show limited distinctiveness across certain Paprosky grades, suggesting potential improvements needed in defect classification.
• These computational tools may aid in pre-operative surgical planning and advanced biomechanical modeling for rTHA.