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.
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.
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.
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