Abstract
Computed tomography (CT) is the imaging method of choice for evaluating the canine nasal cavity, being invaluable in determining disease extent, guiding sampling, and planning treatment. While predictions of pathology type can be made, there is significant overlap between CT changes noted in neoplastic, inflammatory, and infectious nasal disease. Recent years have seen remarkable advancement in computer-aided detection systems in human medicine, with machine and deep learning techniques being successfully applied for the identification and accurate classification of intranasal pathology. This study aimed to develop a neural network pipeline for differentiating nasal pathology in dogs using CT studies of the head. A total of 80 CT studies were recruited for training and testing purposes. Studies falling into one of the three groups (normal nasal anatomy, fungal rhinitis, and intranasal neoplasia) were manually segmented and used to train a suite of neural networks. Standard accuracy metrics assessed performance during training and testing. The machine learning algorithm showed reasonable accuracy (86%) in classifying the diagnosis from an isolated scan slice but high accuracy (99%) when aggregating over slices taken from a full scan. These results suggest that machine learning programmes can accurately discriminate between intranasal pathologies based on canine computed tomography.