## 4 Discussion
### Other Machine Learning Tools Used for Fracture Risk Assessment and Their Limitations  
Machine learning technologies are extensively used to evaluate the risk of bone fractures and use advanced algorithms to improve the precision and dependability of predictions. Much research has shown the potential of random forest classifiers. However, it is worth noting that a wide range of other machine learning approaches are also being used in the field of bone health and are providing useful insights and capabilities. Unfortunately, most of these tools can only practically be used for human medicine, not for animals. The aforementioned tools encompass the following. The utilization of Cox regression models and an artificial neural network (ANN)-DeepSurv model provides a diverse set of approaches to evaluating prospective fracture chances in individuals diagnosed with osteopenia and osteoporosis. This showcases the adaptability and comprehensiveness of machine learning applications within this particular domain [3]. An additional tool that employs machine learning models to diagnose and categorize osteoporosis by detecting fractures from images and predicting fracture risk via the use of various data sources provides a comprehensive approach to tackling osteoporosis and related bone health issues [36]. Support vector machines (SVMs), gradient-boosting machines (GBMs), neural networks (NNETs), and regularized discriminant analysis (RDA) are being used to predict new fractures following treatment for osteoporortic vertebral compression fractures. The analysis underscores the wide range of machine learning algorithms that can be applied [37]. The use of artificial intelligence in medical diagnostics has been applied to the prediction of fragility fractures using MRI data. This integration of artificial intelligence enhances diagnostic accuracy and risk assessment, highlighting the unique application of AI in medical diagnostics [38]. Some researchers have explored the possibility of automation and machine learning in facilitating speedy and accurate evaluations of fracture detection, bone mineral density prediction, and fracture risk evaluation through the use of plain radiographs [39]. The combination of advanced imaging methods and deep learning has been seen in the use of deep learning models and high-resolution peripheral quantitative computed tomography (HR-pQCT) for the full investigation of bone health, with a specific emphasis on analyzing bone micro-architecture and fracture risk [40]. Random forest (RF) classifiers were chosen due to their notable attributes, including high resilience, fast handling of intricate datasets, and remarkable levels of accuracy, specificity, and sensitivity. RF specializes in handling complex data and can be used for the evaluation of canine fracture risk. It is important to note that our study, being retrospective, did not include data on the breed conformation, such as skeletal slenderness. This is a notable limitation, as breeds with different skeletal builds may have varying susceptibilities to fractures. For instance, slender breeds like Italian Greyhounds might differ significantly from more robust breeds like German Shepherds, even though they are within the same size category. Additionally, we recognize that lifespan variations among breeds could influence fracture risk profiles, with some breeds having shorter lifespans while others may live significantly longer. Future studies should aim to incorporate these factors to provide a more nuanced understanding of fracture risks across different canine breeds. While many of our findings align with established knowledge in veterinary epidemiology and preventive medicine, this study underscores the practical application of machine learning in a clinical setting. By leveraging this approach, we demonstrate its potential as a foundational tool that could evolve into a more comprehensive IT solution.