Performance and Sensitivity Analysis of an Automated X-Ray Based Total Knee Replacement Mass-Customization Pipeline

Abstract

A proof-of-concept, fully automated, mass-customization pipeline for knee replacement surgery is outlined. The pipeline aims to address the limitations of currently available customization solutions by removing the need for 3D imaging and manual design, minimizing lead times, and reducing costs, whilst enabling improved patient outcomes. The dataflow of the pipeline and methods for assessing performance are detailed. A digitally reconstructed radiograph method was adopted in the analysis to remove errors stemming from poor X-ray alignment and calibration, and to enable the influence of specific attributes to be evaluated. A sensitivity study was performed to quantify the impact of X-ray alignment and calibration. The analysis found better results were achieved for the tibia over the femur in terms of clinically significant component over/under-hang (9% vs 18%). The pipeline was sensitive to subject ethnicity, but this was likely due to limited diversity in the training data. Arthritis severity was found to impact performance, suggesting further work is required to confirm suitability for use with more severe cases. X-ray alignment and dimensional calibration were shown to be paramount for accurate results. The pipeline performance was demonstrated to be superior to results reported for off-the-shelf implants, but customization solutions based on 3D imaging could afford marginally better results. In summary, the study validated the pipeline for a broad range of subjects, highlighted its potential advantages over both off-the-shelf and other customization alternatives, and outlined the potential challenges of adopting such a tool.