Next-Generation Computer Vision in Veterinary Medicine: A Study on Canine Ophthalmology

Abstract

Taking into account the achievements of state-of-the-art computer vision methods in recent years, the aim of this research was to examine the extent to which their application can help in the detection of symptoms of eye diseases in dogs and the diagnosis of ophthalmological conditions in order to provide owners with preliminary information about the disease of their pets and speed up making diagnoses to veterinarians. In the research, clinical data of canine eye diseases including at least one of the 4 symptoms of the disease was collected and a set was formed to train the segmentation model, which was expanded with synthesized data generated using the LoRA Stable Diffusion model verified by an ophthalmologist. An extended segmentation model based on U-Net architecture with ResNet34 backbone was fine-tuned on the prepared set and compared to zero-training GPT-4o and Grounding SAM. The results show that the fine-tuned U-Net model gives the best segmentation results of eye disease symptoms of 97% base of pixel accuracy metric and significantly outperforms other tested methods. The segmentation masks are used as part of the prompts for GPT-4 and GPT-4o to generate diagnoses of diseases having the specified symptoms. The generated diagnostic results were evaluated using text evaluation metrics and that the most accurate diagnosis according to the Bert score of 84% is achieved using GPT-4o in combination with the U-Net segmentation mask. The article proposes a pipeline that gives the best results and solutions to be considered for other diagnostic procedures in ophthalmology and veterinary medicine.