The custom total knee replacement: A bespoke solution

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

The aim of this study was to develop an automated pipeline capable of designing custom total knee replacement implants from CT scans. The developed pipeline firstly utilised a series of machine learning methods including classification, object detection, and image segmentation models, to extract geometrical information from inputted DICOM files. Statistical shape models then used the information to create femur and tibia 3D surface model predictions which were ultimately used by computer aided design scripts to generate customised implant designs. The developed pipeline was trained and tested using CT scan images, along with segmented 3D models, obtained for 98 Korean Asian subjects. The performance of the pipeline was tested computationally by virtually fitting outputted implant designs with 'ground truth' 3D models for each test subject's bones. This demonstrated the pipeline was capable of repeatably producing highly accurate designs, and its performance was not impacted by subject sex, height, age, or knee side. In conclusion, a robust, accurate and automatic, CT-based total knee replacement customisation pipeline was shown to be feasible and could afford significant time and cost advantages over conventional methods. The pipeline framework could also be adapted to enable customisation of other medical implants.

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

Purpose: The aim of this study was to outline a fully automatic tool capable of reliably predicting the most suitable total knee replacement implant sizes for patients, using bi-planar X-ray images. By eliminating the need for manual templating or guiding software tools via the adoption of convolutional neural networks, time and resource requirements for pre-operative assessment and surgery could be reduced, the risk of human error minimized, and patients could see improved outcomes.

Methods: The tool utilizes a machine learning-based 2D—3D pipeline to generate accurate predictions of subjects’ distal femur and proximal tibia bones from X-ray images. It then virtually fits different implant models and sizes to the 3D predictions, calculates the implant to bone root-mean-squared error and maximum over/under hang for each, and advises the best option for the patient. The tool was tested on 78, predominantly White subjects (45 female/33 male), using generic femur component and tibia plate designs scaled to sizes obtained for five commercially available products. The predictions were then compared to the ground truth best options, determined using subjects’ MRI data.

Results: The tool achieved average femur component size prediction accuracies across the five implant models of 77.95% in terms of global fit (root-mean-squared error), and 71.79% for minimizing over/underhang. These increased to 99.74% and 99.49% with ±1 size permitted. For tibia plates, the average prediction accuracies were 80.51% and 72.82% respectively. These increased to 99.74% and 98.98% for ±1 size. Better prediction accuracies were obtained for implant models with fewer size options, however such models more frequently resulted in a poor fit.

Conclusion: A fully automatic tool was developed and found to enable higher prediction accuracies than generally reported for manual templating techniques, as well as similar computational methods.

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

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.

A computational design of experiments based method for evaluation of off-the-shelf total knee replacement implants

A methodology to explore the design space of off-the-shelf total knee replacement implant designs is outlined. Generic femur component and tibia plate designs were scaled to thousands of sizes and virtually fitted to 244 test subjects. Various implant designs and sizing requirements between genders and ethnicities were evaluated. 5 sizes optimised via the methodology produced a good global fit for most subjects. However, clinically significant over/underhang was present in 19% of subjects for tibia plates and 25% for femur components, reducing to 11/20% with 8 sizes. The analysis highlighted subtly better fit performance was obtained using sizes with unequal spacing.

Biomechanical and Clinical Effect of Patient-Specific or Customized Knee Implants: A Review

(1) Background: Although knee arthroplasty or knee replacement is already an effective clinical treatment, it continues to undergo clinical and biomechanical improvements. For an increasing number of conditions, prosthesis based on an individual patient's anatomy is a promising treatment. The aims of this review were to evaluate the clinical and biomechanical efficacy of patient-specific knee prosthesis, explore its future direction, and summarize any published comparative studies. (2) Methods: We searched the PubMed, MEDLINE, Embase, and Scopus databases for articles published prior to February 1, 2020, with the keywords "customized knee prosthesis" and "patient-specific knee prosthesis". We excluded patient-specific instrument techniques. (3) Results: Fifty-seven articles met the inclusion criteria. In general, clinical improvement was greater with a patient-specific knee prosthesis than with a conventional knee prosthesis. In addition, patient-specific prosthesis showed improved biomechanical effect than conventional prosthesis. However, in one study, patient-specific unicompartmental knee arthroplasty showed a relatively high rate of aseptic loosening, particularly femoral component loosening, in the short- to medium-term follow-up. (4) Conclusions: A patient-specific prosthesis provides a more accurate resection and fit of components, yields significant postoperative improvements, and exhibits a high level of patient satisfaction over the short to medium term compared with a conventional prosthesis. However, the tibial insert design of the current patient-specific knee prosthesis does not follow the tibial plateau curvature.