Introduction: Tibial fracture healing complications occur frequently with reported non-union rates up to 23%. Preoperative patient-specific finite element (FE) modelling of fracture fixation may help to minimize these complications. However, developing such models requires labour-intensive work including (manual) segmentation of bones from medical images, making them unpractical for clinical applications. This study aims to establish a semi-automated workflow for the development of three-dimensional (3D) patient-specific FE models of long bone fractures based on two-dimensional (2D) X-ray images and patient characteristics.

Methods: A statistical shape model (SSM) of the tibia was developed based on computed tomography (CT) scans of subjects without tibial fractures. Using this model, shape parameters were correlated to patient characteristics, including gender, age, weight, and height of the subjects, using multilinear regression. Thereafter, strategies were developed to (1) fit the SSM of the tibia to a previously unseen fractured tibia based on two orthogonal X-rays and patient characteristics to estimate its intact 3D shape, and to (2) automatically model the fracture lines as detected on the X-rays in the intact tibia model. Using the automatically created geometries of the fractured tibia, FE models of the stabilized fracture were developed in Abaqus/CAE and used to investigate strains within the callus under post-operative loading conditions. The workflow was tested on one patient and the strains obtained from the FE models within the fracture region were compared to strains reported in the literature.

Results: An SSM of the tibia was successfully developed based on CT scans of 25 subjects (15 male, age = 60 ± 5.5; 10 female, age = 51 ± 7.1). The first five shape modes captured 90% of the total shape variation in the studied population. Significant correlations were found between the first shape mode, which described shape changes in the tibial length, and patient gender, age, weight, and height. SSM-to-patient fitting was achieved with a mean error of 0.81 mm and a maximum error of 4.22 mm. FE analysis of the stabilized fracture predicted inter-fragmentary compressive strains between 0 and 10% with a median value of 2%. Increasing the fixation working length by 13 mm, led to a 10-fold increase in the predicted median compressive strains.

Discussion and Conclusions: A workflow for the semi-automated generation of FE models of tibia fractures was successfully established. Patient-specific FE analysis results predicted strains within the fracture in a range reported for optimal bone formation. Additionally, predicted strains were highly dependent on the fixation configuration and material, most notably the fixation working length. Future work should focus on fully automating the suggested workflow and on the validation of the results. Ultimately, such a workflow could be used to formulate individualized treatment recommendations during the early pre- and post-operative phase in tibial fracture management to prevent non-union development.

Open Wedge High Tibial Osteotomy (OWHTO) is an extensively used, effective treatment option for medical conditions such as medial knee osteoarthritis and varus malalignment. The current developments in the 3D printing industry facilitated using 3D printed patient-specific guides (PSSGs), making OWHTO a desirable treatment option. Although the PSSG improves the outcomes (e.g., high accuracy, lower radiation) of the OWHTO, lateral hinge fractures and posterior soft tissue injuries of the tibia are two main risks still present. Therefore, this study aims to facilitate an optimal 3D printed PSSG design with a lateral hinge protecting K-wire and a soft tissue protecting sleeve for OWHTO that can prevent or reduce these two critical risks. For this purpose, first, 3D models were constructed from pre-operative CT images of the cadaver specimens. Afterward, a monopolar osteotomy cut was created at a location where the orthopedic surgeon Tom Piscaer decided on the 3D-constructed models to develop Finite Element (FE) analysis. Using the FE analysis and 3D constructed models, different intact lateral hinge lengths (5 mm, 6 mm, 7 mm, 8 mm, 9 mm, and 10 mm) without apical drill holes were analyzed for relative comparison between six different simulations. The intact hinge length was chosen between 10 mm and 5 mm according to the information in the literature. Von Mises stress was used as an outcome measure. The decrease in distribution and magnitude for the von Mises stress at the lateral side of the tibia and the lateral hinge was used to point out the overall impact of the hinge length reduction. As the intact hinge length decreased, the von Mises stress on the lateral hinge generally decreased. According to the results from FE analysis using a high-quality mesh, leaving a 6 mm intact hinge was determined to be the optimum case among other cases. It reduced the stress around the hinge, maintained sufficient cortical and cancellous bone, had a higher margin of error, and likely reduced the fraction risk. The results of this FE analysis were used to develop the hinge K-wire location on the PSSG. Several PSSG models with soft tissue-protecting sleeves were created. Among these designs, the second and third prototypes (3 prototypes in total) were tested on the cadaver specimens. After the tests, post-operative CT images of the cadavers were taken. From these images, 3D models of the tibia were constructed. Pre- and post-operative 3D models were registered onto each other for measuring the deviation between the planned and obtained osteotomy. This was used to analyze the accurate placement of the PSSG and its effect on the sleeve function. While the translation deviations ranged from 5.2 mm, 2.05 mm, and 7.81 mm, the rotational deviations ranged from 7.04°, 1.55°, and 9.16° for cases 1, 2, and 3, respectively. Two PSSG designs passed the acceptable region for translational malpositioning (lower than 5 mm). Although the malpositioning of the PSSG affected the sleeve position slightly, the cadaver test results showed that the sleeve still protected the tibia's posterior side from soft tissue injuries by stopping the cutting saw at the cutting trajectory due to its lengthy coverage at the posterior side of the tibia. Since the cadavers did not have the necessary soft tissues, it wasn't easy to give a concrete answer. Still, according to the results, the sleeve is expected to ensure soft tissue protection. Using the PSSG with a hinge-protecting K-wire and a soft tissue-protecting sleeve will likely provide lower radiation exposure, shorter operation time, fewer complication risks and lateral hinge fractures, less posterior soft tissue damage, and increased healing.

Background: In the field of orthoses for the treatment of forearm injuries, there is a growing clinical demand for more precise and individualized health monitoring technologies. The integration of additive manufacturing techniques with wearable electronics has gained significant interest in this field, emerging as a prominent treatment option that aims to address this demand and gradually replacing conventional rehabilitation approaches. While the medical benefits of orthoses are well-established, a significant challenge persists in ensuring patient adherence to suggested usage guidelines.

Objectives: This thesis aims to develop a systematic workflow for providing personalized feedback to patients, with the objective of 1) improving their functional recovery throughout the rehabilitation process, and 2) facilitating patient adherence to their prescribed treatment plan.

Methods: A general workflow for the utilization of wearable electronics inside an orthotic device is proposed. This workflow accounts for the physical interactions between the user and the orthotic device itself. Particularly, an elastomer material was used to fabricate the hand brace with six commercially available force-sensitive sensors and a microcontroller for data processing and integration, into the proposed workflow. Each element of this workflow is discussed with respect to the role that it plays in the treatment of a forearm injury, along with how the data of each sensor can be used as inputs to the designed platform that aims to monitor the patient compliance to the treatment plan. Through an accessible web interface, users can conveniently track their treatment progress, receive real-time feedback on brace usage and monitor wear duration. Concurrently, clinicians can gain access to valuable monitoring capabilities, facilitating data-informed adjustments for personalized care. To validate the effectiveness of the designed 3D-printed hand brace, finite element simulations and experiments were conducted to evaluate the immobilization of the hand brace and study pressure distribution, by applying the brace on a participant’s hand.

Results: Simulation results were compared to those of experiments and demonstrate positive outcomes, affirming the brace's ability to provide support for forearm fractures with 85% immobilization for the hand rotation under wrist movements. The maximum average pressure values observed during experiments were less than 0.3 MPa for wrist flexion and 0.2 MPa for wrist extension.

Conclusion: The proposed method outlines a multistep process that lays the foundation for future advancements in orthopedic brace design and personalized healthcare. It offers an objective means of monitoring patient adherence to forearm treatment methods and allows a trained healthcare worker to 3D-print a brace based on the initial medical diagnosis and subsequently modify the treatment method considering the data captured via platform during the user’s daily activities.

That mechanical properties of the extracellular environment can influence cell behaviour has already been established. Recent studies indicate that human mesenchymal stem cells are affected by the surface curvature of the underlying substrate. However, how meso-scale substrate curvature affects cell behaviour is still not clear. This study utilised the finite element method to simulate a prestressed human mesenchymal stem cell conforming and attaching to a flat control substrate, concave hemispherical and concave cylindrical substrates with curvature radii from 300 µm to 75 µm. The cell model comprises the actin cortex, cytoskeleton, and nucleus modelled with a hyperelastic material definition and 30 linear elastic stress fibres prestressed with a force of 10 nN. The effects of surface curvature on cellular traction forces, cell height, nuclear aspect ratio and actin cortex was studied. The vertical traction forces were observed to be 70% and 40% lower for the hemispherical and cylindrical substrates of the highest curvatures compared to the flat control substrate, respectively. Cellular traction forces towards the cell periphery were roughly 10-20% higher than the more central cellular traction forces independent of substrate curvature. Stresses in the actin cortex were observed to increase by 290% and 220% from the flat control substrate to the hemispherical and cylindrical substrates of the highest curvatures, respectively. These results indicate that the cell is more sensitive to hemispherical substrates than cylindrical substrates. The results also support in vitro observations where hMSCs are seen to span hemispherical substrates and avoid continuous contact.

Background: Advancements in the field of artificial intelligence have lead to the incorporation of automated algorithms in the analysis of medical images and data. Deep learning algorithms have been applied in muscu- loskeletal research to improve the understanding of osteoarthritis and to assist in disease detection and prognosis. The majority of the developed methods examine and process X-ray images and clinical data (age, gender etc.), with a small minority using MRI as inputs. Objective: The current master thesis project aims to investigate the influence of MRI scans on the early detection of knee osteoarthritis through the use of deep learning architectures, and to develop a semi-automatic method for knee region of interest extraction for creating the MRI input of detection algorithms. Methods: The MRI scans used in this project were acquired from the publicly available database of the Os- teoarthritis Initiative. In total 593 dual echo steady state and intermediate-weighted turbo spin-echo sequences were included. The extraction of the knee joint included several processing steps. Initially, a U-Net model was trained on 507 annotated dual echo steady state MRIs for the segmentation of bone and cartilage tissue, which was followed by the registration of the output masks to intermediate-weighted turbo spin-echo sequences in order to create the joint labels for the desired MRI scans. Final step for the region of interest construction included the search of bone coordinates and the creation of the knee joint region of interest. The detection of early osteoarthri- tis progression from knee MRI scans was tested through three different deep learning architectures, a residual network (ResNet), a densely connected convolutional network (DenseNet) and a convolutional variational au- toencoder (CVAE). Furthermore, the probability output of the ResNet and DenseNet as well as the feature vector of the CVAE were coupled with clinical data (age, gender, bone mass index) and used as input to a Logistic Regression Classifier, in order to investigate the influence of osteoarthritis related features to the detection task. The U-Net segmentation method was evaluated using Dice similarity coefficient and Intersection of Union while the detection algorithms using the area under the receiver’s characteristic curve (AUC) and the precision-recall curve (PR-AUC) metrics, with two different input data configurations, only MRI and a combination of MRI and clinical data. Results: The U-Net algorithm for bone and cartilage segmentation showed adequate results, since Dice simi- larity coefficient and Intersection of Union reached mean values higher than 0.99 and 0.88. Regarding the early detection of knee osteoarthritis incidence, ResNet and DenseNet showed similar results, with both methods hav- ing an AUC value ranging from 0.5033 to 0.6269, when only MRI scans were examined. In the case of MRI and clinical data combination, the more complicated deep learning architecture (DenseNet) achieved the highest AUC at 0.6556. The best performing model was CVAE with the largest number of latent space features (1000) achieving an AUC of 0.6699 when combined with clinical data and an AUC of 0.6689 when used alone as input to the logistic regression classifier. All three deep learning algorithms yielded higher performance metrics when clinical data where combined with models’ outputs. Conclusion: The tested deep learning algorithms showed a potential in the challenging task of early detection of knee osteoarthritis through MRI scans, even though they did not reach the same level of performance metrics. The region-of-interest creation had promising results for the implementation of U-Net method for bone tissue labelling.

Bone defects are a common problem in healthcare today. When the damage is too extensive, the human body is not able to heal itself. Different solutions are available, but they all have limitations. Therefore, the search for a solution to overcome these limitations is important. In this theses a way to create a scaffold out of seperate crumpled elements is explored. Mechanical and morphological properties are examined.

To better understand and predict osteoarthritis, researchers are developing so-called coupled modelling workflows. Coupled workflows convert data from gait analysis studies to subject-specific tissue mechanical response estimations through the use of musculoskeletal and finite element models. The tissue mechanical response inside the joint is thought to play an integral role in the onset and progression of osteoarthritis. To study this, the design of coupled workflow was proposed. This design contained subject-specific gait data which was processed by a musculoskeletal model with a single degree of freedom knee joint. Musculoskeletal output was transferred through an adjusted generic finite element model of the knee to calculate maximum principal stress and shear strain values in the tibial cartilage of the knee. Proper marker placement is crucial for making an accurate assessment of the patient’s function in gait analysis studies. It has been claimed that marker misplacement is the main cause of measurement variability in gait analysis studies. It however remains unclear how potential marker misplacement propagates to the coupled modelling workflow results. To investigate this, in addition to the design of a coupled workflow, a sensitivity analysis was performed. With this sensitivity analysis we tried to answer the following question: How does marker placement of knee joint markers in gait analysis influence the tissue mechanical response calculated by a coupled workflow? For the sensitivity analysis, knee joint marker placements were virtually perturbed along anterior-posterior, proximal-distal and medial-lateral direction, to mimic marker misplacement. Corresponding knee biomechanics were estimated from the perturbed input data in the coupled workflow. Peak maximum principal stress values varied by up to 0.60MPa and peak shear strain varied by up to 0.08% as a result of perturbed knee marker placement. For cumulative stress levels, broader relative ranges were found. Moreover, the results showed that marker placement along the anterior-posterior direction had the greatest influence on corresponding tissue mechanical response estimations. In future studies a standard error of measurement margin is proposed in the assessment of coupled modelling worfklow results. In conclusion, the proposed workfow was relatively easy to build and provided similar tissue mechanical response result to those as reported by more complex models which were more computationally intensive. This implies that in the future, coupled modelling processes may very well be incorporated into the clinical decision-making process for musculoskeletal disorders like osteoarthritis.

Restoration of the normal mandibular form and function is attempted with reconstructive surgery. The current standard procedure to restore continuity defects of the mandible involves free tissue transfer with an autologous bone flap. Even though the success rates are high, several critical drawbacks have been associated with this procedure, including severe donor site morbidity, long hospital stay and re­covery process, need for high surgical expertise, insufficient bone graft height, and mechanical failure of the plating system. A systematic approach for the designing and testing of patient­-specific implants used as an alternative to the free flap procedure appears to be still lacking. The goal of this thesis project was to develop a semi­automatic workflow for designing patient­ specific mandibular reconstruction implants and assess the effect of topology optimization on the biome­chanical performance of the implant using the finite element analysis (FEA) and experimental validation. Using the proposed workflow, a fully porous implant (lattice implant) and a topology optimized implant (TO implant) were designed according to the anatomy of a synthetic mandible analog subjected to lateral resection, and were additively manufactured using selective laser melting. Both implants were designed in the shape of a cage to allow for the insertion of a bone graft and the integration of dental implants. The mechanical performance of the reconstructed mandibles was predicted with computa­tional FEA and evaluated through quasi­-static and cyclic experimental testing. Digital image correlation was used to validate the finite element model through principal strain field comparison. An excellent fit between the implants and mandibles was established, indicating the capability of the workflow to develop customized implants with accurate dimensions. The results obtained with FEA were in agreement with the DIC measurements and quasi-­static testing results. No significant differences (P < 0.01) in mean stiffness, mean ultimate load, and mean ultimate displacement were found between the non-­implanted control mandibles, lattice­-implanted mandibles, and TO-­implanted mandibles during quasi-­static testing. No implant failure was observed during static nor cyclic testing at loads substantially higher than the average maximum biting force of healthy individuals, indicating the high resistance of the implant designs to mechanical failure. Yet, the lattice implant would likely be preferred over the TO implant for clinical application due to its lower weight (16.5%), higher porosity (17.4%), and shorter workflow time (633.3%). The workflow proposed in this study may offer surgeons and medical engineers the tools to sys­tematically design and evaluate patient­-specific reconstruction implants. This would result in more cost­-effective and time­-effective pre-­surgical planning and result in implant designs that can minimize morbidity and maximize aesthetic and functional outcomes.