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.

Applying machine learning methods to enable automatic customisation of knee replacement implants from CT data

“…Furthermore, the SSM base shapes were ‘idealised’ and smoothed so outputted models were suitable for use in designing custom implants. This approach is consistent with Burge et al 12 , 13 .…”

“…), to facilitate quality outputs, precise calibration and tightly controlled positioning/alignment were required. Furthermore, slightly poorer performance was reported for the X-ray-based pipeline compared to other 3D imaging-based methods, particularly for geometrically complicated femur components 13 .…”

A computational tool for automatic selection of total knee replacement implant size using X-ray images

“…The accuracy of the 2D—3D process was evaluated by Burge et al (2022b) by comparing the tool’s bone surface estimations with subjects’ 3D ground truth models. A mean RMSE of 1.09 mm (SD 0.18 mm) for the femur and 0.98 mm (SD 0.15 mm) for the tibia across the 78 subjects were reported.…”

“…The performance of the tool could be further improved by controlling the alignment and quality of the inputted X-ray images to ensure compatibility with the 2D—3D pipeline ( Burge et al, 2022b ). Due to the datasets available, the testing of the tool was limited to predominantly White Americans without severe arthritis degeneration, whilst the modules of the tool were principally trained using Asian Korean data.…”

“…Like in Zheng et al (2018) and Massé and Ghate (2021) , the size prediction tool initially utilizes inputted AP and lateral X-ray images to generate 3D estimations of patients’ femur and tibia bones before component size predictions can be made. To achieve this automatically, the 2D—3D pipeline developed in Burge et al (2022a) , Burge et al (2022b) was utilized. The key aspects of the workflow, built using Python 3, are summarized below and illustrated in Figure 1 .…”