Total knee arthroplasty (TKA) is a widely used surgical procedure to restore the knee joint after cartilage disorders as osteoarthritis. During this surgical procedure, the damaged cartilage layer is removed and the bones are cut in the correct alignment. The following step is the implantation of the TKA components in the knee. One of the major causes of failure after implantation of the TKA components in the knee is aseptic loosening. Since the fixation of the implant has an important role in this aseptic loosening, it is very important to test this beforehand. Implant fixation can be modelled and tested by doing finite element simulations and cadaveric experiments. During these experiments, the loading profiles of multiple activities will be applied to 3D models of the bone and the implant.
The aim of this project is to investigate the implant fixation of a PEEK total knee arthroplasty. This is done with the development of 3D models of the knee and a PEEK knee prosthesis. To create a proper model, the implant characteristics and patient variability need to be taken into account.
Researcher for this project: Corine Post.
Accurate fracture risk prediction is important, because it is difficult for clinicians to differentiate between high and low fracture risk lesions.
Patients with advanced cancer often develop bone metastases, and in approximately ten percent, these lesions occur in the femur. Femoral metastases may cause pain and can lead to pathological fractures, which severely affect the quality of life. Local treatment of patients with femoral metastases is based on the expected fracture risk: patients with a low fracture risk are treated conservatively with for example radiotherapy to decrease pain, whereas patients with a high fracture risk are considered for stabilizing surgery to prevent a fracture from occurring. Therefore, accurate fracture risk prediction is important. However, it is difficult for clinicians to differentiate between high and low fracture risk lesions, leading to considerable numbers of under and over treatment. To improve fracture risk prediction, we developed the BOne Strength score, or BOS score; a patient-specific finite element (FE) computer model that calculates bone strength based on patient-specific anatomy and bone quality, obtained from CT scans.
Previously, we demonstrated that the BOS score improved fracture risk prediction in patients, compared to current clinical guidelines. The goal of this project is to initiate clinical implementation of the BOS score by determining whether the BOS score aids in fracture risk prediction and treatment decision making for patients with bone metastases in the femur.
Researcher for this project: Florieke Eggermont.
Develop and implement a highly patient specific decision aid for patients with hip- or knee osteoarthritis
Hip- and knee osteoarthritis are leading causes of disability resulting in joint pain and stiffness. Total hip- and knee arthroplasty (THA and TKA) are a recommended intervention when conservative management appears to be ineffective and disability is significant. Due to this surgical intervention, quality of life rises while the functionality of the joints is ameliorated extensively. Although THA and TKA are considered safe, complications can interfere with a successful outcome or can even put patients in a life-threatening situation. Study findings show that comorbid diseases and medication use are known to influence complication risk and patient outcome.
Decision aids are commonly used to inform patients and to support the patient and physician to think about the level of experienced pain and discomfort, the personal circumstances and the intended type of surgery. These factors and the complication risk can influence which treatment fits best in the patients individual situation.
Until now, decision aids provide general information about treatment options and complication risk. Since one of the main challenges for physicians is to inform the individual patient by providing personal information about the complication risk, we aim to update the decision aid with a pre-operative risk estimation tool which takes the comorbid diseases and medication use of the individual patient into account.
The aim of this project is to develop and implement a decision aid for patients with hip- or knee osteoarthritis to facilitate shared decision making by providing predicted outcomes which are highly patient specific.
Researcher for this project: Lieke Sweerts.
Manual segmentation is a time-consuming task and it may be difficult to accurately segment every abnormality in the bone structure. The main goal of this study, therefore, is to automate the segmentation procedure using deep convolutional neural networks.
Researcher for this project: Ali Ataei.
Low back pain (LBP) is worldwide responsible for more years lived with disability than any other health condition. In the Netherlands, approximately 44% of the population experiences at least one episode of LBP in their lifetime, with one in five reporting persistent back pain lasting longer than three months (chronic low back pain, CLBP). CLBP often results in substantial limitations in functional activities and is responsible for high healthcare and socioeconomic costs. In the vast majority of patients with CLBP (85-90%), the aetiology is unknown and for medical specialists it is challenging to identify patients who would benefit from surgical or non-surgical interventions.
The Nijmegen Decision Tool for CLBP (NDT-CLBP) is a pre-diagnostic decision support tool that matches patients based on a questionnaires to the treatment that they are most likely to benefit from. Patients are referred to either spinal surgeon consultation or non-surgical consultation. In this project the NDT-CLBP will be further developed into a two-phased decision support tool following the patient journey: phase 1 decision support for consultation (currently in use) and phase 2 decision support for referral to a specific treatment based on the clinical diagnostic phase (NDT-CLBP 2.0). In the diagnostic phase, many patients with CLBP receive a lumbar spine MRI scan to detect degenerative changes of the spine. The goal of this project is to develop an AI-based image analysis algorithms that enable detailed quantitative routine analysis of these MRI scans.
The first step in creating such an algorithm is getting an automatic segmentation of the spine. In our first project we aim to create a convolutional neural network that automatically segments vertebrae and inter-vertebral discs on MRI scans. Anonymized clinical MRI scan from the RadboudUMC are manually annotated which will be used to train the network. Eventually we also aim to organize a segmentation challenge using this dataset.
To identify which MR image features are related to CLBP a narrative review is being written. In this review an overview of all possible image features is presented in five different catagories: discogenic, neurogenic, osseous, facetogenic and paraspinal. Per catagory an overview of all relevant literature is given. The result of this review will be a list of image features that have a high predictive value for CLBP which eventually will be used in the decision support tool.
Researcher for this project: Jasper van der Graaf.