PhD defense - Adriaan Lambrechts
Automation of preoperative planning for total knee arthroplasty using machine learning
Start: 12/06/2024, 17:30
Location: Aula van de Tweede Hoofdwet - Thermotechnisch Instituut
Abstract:
Patients suffering from end stage osteoarthritis commonly have to cope with pain, joint instability, reduced range of motion, and joint stiffness. Total knee arthroplasty (TKA) is a surgical procedure aimed to treat patients suffering from end stage osteoarthritis. In the last decade several technological advancements have helped surgeons in increasing their efficiency and attempt to improve patient outcome. 3D preoperative planning allows surgeons to be better prepared for the surgery by planning the required implant types, sizes and their position in advance. These preoperative plans can be used in combination with patient specific instruments, navigation systems, robotics platforms or augmented reality to transfer the plan to the surgery. In the process, these systems collect large amounts of data.
The purpose of this thesis is to investigate ways to improve the preoperative planning process based on retrospectively collected preoperative plans using machine learning (ML). Therefore, we will investigate automatic knee MRI segmentation, the variability in preoperative planning styles, and surgeon specific preoperative planning. Manual segmentation of knee MRI scans is time consuming, taking up to 6 hours per scan, and is prone to inter-observer variability. As a result, several methods have been proposed over the last decade to automate the segmentation procedure based on atlas approaches, shape models, and recently deep learning. Two deep learning approaches were compared based on 3D and 2.5D convolutional neural networks. Our methods outperform state-of-the-art methods in terms of bone segmentation. The 3D and 2.5D methods were compared to a shape model based automatic segmentation based on 3000 scans and yielded a 22.9% and 35.4% reduction in manual segmentation time, respectively. As a result, the cost associated with preoperative planning can be decreased, facilitating the adoption of 3D based preoperative planning.
For decades, mechanical alignment has been the gold standard in knee alignment targeted during TKA. However, in recent years, new scientific and clinical insights have led to new planning philosophies. We investigated the variation in preoperative planning styles between 42 surgeons through the use of machine learning. A machine learning method was proposed that predicted the surgeon who planned a case based on the selected implant sizes, their position and orientation. Our method was able to predict with 84% accuracy which surgeon planned a case. Furthermore, our method was able to find surgeons with similar planning style. Based on these results, we concluded that preoperative planning should happen in a way that is both patient and surgeon specific.
Several studies in the literature have demonstrated the need to make changes to the proposed implant size, position or orientation in manufacturer’s preoperative plans. Therefore, a machine learning method was proposed that predicts the preoperative planning parameters in a surgeon and patient specific manner. The ML generated preoperative plans required 39.7% less corrections compared to manufacturer’s default preoperative plans. The femoral and tibial implant sizes in the default plan were correct in 68.4% and 73.1% of cases respectively, while in the ML plan they were correct in 82.2% and 85.0% of cases. These ML generated preoperative plans can enhance the efficiency and user experience of the surgeon.
Determining the required implant size preoperatively has clinical and logistical advantages. Several methods in the literature have investigated predicting femoral implant size based on demographic data with an accuracy up to 56%. We proposed a novel method to predict femoral implant size based on the 3D mesh of the femoral bone. Our method, the hypergraph regularized group lasso significantly outperformed previously reported methods with an accuracy of 70.1%. This can be beneficial in increasing the efficiency of hospital logistics, by reducing instrument and implant sterilization cost, and operating room setup time. In this thesis state-of-the-art machine learning approaches were investigated to improve the efficiency of the preoperative planning process. Improved medical image segmentation can reduce lead times and the cost of personalized total knee arthroplasty. Machine learning methods can capture surgical planning styles of experienced surgeons. Towards the future the biggest opportunity of machine learning in total knee arthroplasty lies in optimizing the patient reported outcome. If more data, such as data related to patient outcome, is collected on a larger scale, these models can help establish surgical plans that attempt to maximize the patient’s post-operative outcome.
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The defence will also be livestreamed through the following link (PIN: 533559)
PDF: PDF
Organized by: Adriaan Lambrechts