A probabilistic approach for modelling bone fracture healing under Ilizarov circular fixator

Influence of implant base material on secondary bone healing: an in silico study

The implant material at the fracture site influences fracture healing not only from biological perspective but also from mechanical perspective. Biodegradable implants such as magnesium (Mg) based alloys have shown faster secondary bone healing properties as compared to bioinert implants such as titanium (Ti). The general reasoning behind this is the benefit of Mg from biocompatibility perspectives. We studied the effect of Ti and Mg as base materials for implants from mechanical perspectives, where we focused on the displacements at the fracture site of the tibia and their influence on the stimulus for bone healing. We found out that in comparison to Ti, Mg implants have minimal stress shielding problem, only which led to better mechanical stimulus at the fracture site.

A probabilistic approach for assessing the mechanical performance of intertrochanteric fracture stabilized with proximal femoral nail antirotation

Maintaining post-operative mechanical stability is crucial for successfully healing intertrochanteric fractures treated with the Proximal Femoral Nail Antirotation (PFNA) system. This stability is primarily dependent on the bone mineral density (BMD) and strain on the fracture. Current PFNA failure analyses often overlook the uncertainties related to BMD and body weight (BW). Therefore, this study aimed to develop a probabilistic model using finite element modeling and engineering reliability analysis to assess the post-operative performance of PFNA under various physiological loading conditions. The model predictions were validated through a series of experimental test. The results revealed a negative nonlinear relationship between the BMD and compressive strain. Conversely, the BW was positively and linearly correlated with the compressive strain. Importantly, the compressive strain was more sensitive to BW than to BMD when the BMD exceeded 0.6 g/cm3. Potential trabecular bone compression failure is also indicated if BMD is equal to or below 0.15 g/cm3 and BW increases to approximately 2.5 times the normal or higher. This study emphasizes that variations in the BMD significantly affect the probability of failure of a PFNA system. Thus, careful planning of post-operative physical therapy is essential. For patients aged > 50 years restrictions on high-intensity activities are advised, while limiting strenuous movements is recommended for those aged > 65 years.

Development of numerical model-based machine learning algorithms for different healing stages of distal radius fracture healing

BACKGROUND AND OBJECTIVES

Early therapeutic exercises are vital for the healing of distal radius fractures (DRFs) treated with the volar locking plate. However, current development of rehabilitation plans using computational simulation is normally time-consuming and requires high computational power. Thus, there is a clear need for developing machine learning (ML) based algorithms that are easy for end-users to implement in daily clinical practice. The purpose of the present study is to develop optimal ML algorithms for designing effective DRF physiotherapy programs at different stages of healing.

METHOD

First, a three-dimensional computational model for the healing of DRF was developed by integrating mechano-regulated cell differentiation, tissue formation and angiogenesis. The model is capable of predicting time-dependant healing outcomes based on different physiologically relevant loading conditions, fracture geometries, gap sizes, and healing time. After being validated using available clinical data, the developed computational model was implemented to generate a total of 3600 clinical data for training the ML models. Finally, the optimal ML algorithm for each healing stage was identified.

RESULTS

The selection of the optimal ML algorithm depends on the healing stage. The results from this study show that cubic support vector machine (SVM) has the best performance in predicting the healing outcomes at the early stage of healing, while trilayered ANN outperforms other ML algorithms in the late stage of healing. The outcomes from the developed optimal ML algorithms indicate that Smith fractures with medium gap sizes could enhance the healing of DRF by inducing larger cartilaginous callus, while Colles fractures with large gap sizes may lead to delayed healing by bringing excessive fibrous tissues.

CONCLUSIONS

ML represents a promising approach for developing efficient and effective patient-specific rehabilitation strategies. However, ML algorithms at different healing stages need to be carefully chosen before being implemented in clinical applications.

Influence of muscle loading on early-stage bone fracture healing

Designing weight-bearing exercises for patients with lower-limb bone fractures is challenging and requires a systematic approach that accounts for patient-specific loading conditions. However, 'trial-and-error' approaches are commonplace in clinical settings due to the lack of a fundamental understanding of the effect of weight-bearing exercises on the bone healing process. Whilst computational modelling has the potential to assist clinicians in designing effective patient-specific weight-bearing exercises, current models do not explicitly account for the effects of muscle loading, which could play an important role in mediating the mechanical microenvironment of a fracture site. We combined a fracture healing model involving a tibial fracture stabilised with a locking compression plate (LCP) with a detailed musculoskeletal model of the lower limb to determine interfragmentary strains in the vicinity of the fracture site during both full weight-bearing (100% body weight) and partial weight-bearing (50% body weight) standing. We found that muscle loading significantly altered model predictions of interfragmentary strains. For a fractured bone with a standard LCP configuration (bone-plate distance = 2 mm, working length = 30 mm) subject to full weight-bearing, the predicted strains at the near and far cortices were 23% and 11% higher when muscle loading was included compared to the case when muscle loading was omitted. The knee and ankle muscles accounted for 38% of the contact force exerted at the knee joint during quiet standing and contributed significantly to the strains calculated at the fracture site. Thus, models of bone fracture healing ought to account explicitly for the effects of muscle loading. Furthermore, the study indicated that LCP configuration parameters play a crucial role in influencing the fracture site microenvironment. The results highlighted the dominance of working length over bone-plate distance in controlling the flexibility of fracture sites stabilised with LCP devices.

Computational Modelling for Managing Pathways to Cartilage Failure

Over several decades the perception and therefore description of articular cartilage changed substantially. It has transitioned from being described as a relatively inert tissue with limited repair capacity, to a tissue undergoing continuous maintenance and even adaption, through a range of complex regulatory processes. Even from the narrower lens of biomechanics, the engagement with articular cartilage has changed from it being an interesting, slippery material found in the hostile mechanical environment between opposing long bones, to an intriguing example of mechanobiology in action. The progress revealing this complexity, where physics, chemistry, material science and biology are merging, has been described with increasingly sophisticated computational models. Here we describe how these computational models of cartilage as an integrated system can be combined with the approach of structural reliability analysis. That is, causal, deterministic models placed in the framework of the probabilistic approach of structural reliability analysis could be used to understand, predict, and mitigate the risk of cartilage failure or pathology. At the heart of this approach is seeing cartilage overuse and disease processes as a 'material failure', resulting in failure to perform its function, which is largely mechanical. One can then describe pathways to failure, for example, how homeostatic repair processes can be overwhelmed leading to a compromised tissue. To illustrate this 'pathways to failure' approach, we use the interplay between cartilage consolidation and lubrication to analyse the increase in expected wear rates associated with cartilage defects or meniscectomy.

Optimal time-dependent levels of weight-bearing for bone fracture healing under Ilizarov circular fixators

It is known that weight-bearing exercises under Ilizarov circular fixators (ICF) could enhance bone fracture healing by mechano-regulation. However, interfragmentary movements at the fracture site induced by weight-bearing may inhibit angiogenesis and ultimately delay the healing process. To tackle this challenge, a computational model is presented in this study which considers the spatial and temporal changes in mechanical properties of fracture callus to predict optimal levels of weight-bearing during fracture healing under ICF. The study takes sheep fractures as example and shows that the developed model has the capability of predicting patient specific, time-dependent optimal levels of weight-bearing which enhances mechano-regulation mediated healing without hindering the angiogenesis process. The results demonstrate that allowable level of weight-bearing and timings depend on fracture gap size. For normal body weights (BW) and moderate fracture gap sizes (e.g. 3 mm), weight-bearing with 30% BW could start by week 4 post-operation and gradually increase to 100% BW by week 11. In contrast, for relatively large fracture gap sizes (i.e. 6 mm), weight-bearing is recommended to commence in later stages of healing (e.g. week 11 post-operation). Furthermore, increasing ICF stiffness (e.g. using half pins instead of pretension wires) can increase the level of weight-bearing significantly in the early stages up to a certain time point (e.g. week 8 post-operation) beyond which no noticeable benefits could be achieved. The findings of this study have potential applications in designing post-operative weight bearing exercises.