Enhancing Canine Musculoskeletal Diagnoses: Leveraging Synthetic Image Data for Pre-Training AI-Models on Visual Documentations

The examination of the musculoskeletal system in dogs is a challenging task in veterinary practice. The careful diagnosis as well as the evaluation of very complex findings is getting increasingly important. Therefore, a novel method has been developed that enables efficient documentation of a dog's condition through a visual representation. However, since the visual documentation is new, there is no existing training data. The objective of this work is therefore to mitigate the impact of data scarcity in order to develop an AI-based diagnostic support system that can provide veterinarians with accurate predictions. To this end, the potential of synthetic data that mimics realistic visual documentations of diseases for pre-training AI models is investigated. Specifically, this work explores whether pre-training an AI model with synthetic data can improve the overall accuracy of canine musculoskeletal diagnoses. We propose a method for generating synthetic image data that mimics realistic visual documentations. Initially, a basic dataset containing three distinct classes is generated, followed by the creation of a more sophisticated dataset containing 36 different classes. Both datasets are used for the pre-training of an AI model, adapting it to the domain of visual documentations. Subsequently, an evaluation dataset is created, consisting of 250 manually created visual documentations for five different diseases. This dataset, along with a subset containing 25 examples, serves as the basis for evaluating the efficacy of pre-training an AI model on synthetic data. The obtained results on the evaluation dataset containing 25 examples demonstrate a significant enhancement of approximately 10% in diagnosis accuracy when utilizing generated synthetic images that mimic real-world visual documentations. However, these results do not hold true for the larger evaluation dataset containing 250 examples, indicating that the advantages of using synthetic data for pre-training an AI model emerge primarily when dealing with few examples of visual documentations for a given disease. This implies that the use of synthetic data may not be necessary for diseases with many visual documentation examples. Overall, this work provides valuable insights into mitigating the limitations imposed by limited training data through the strategic use of generated synthetic data, presenting an approach applicable beyond the canine musculoskeletal assessment domain.