CNN model-based image classification for canine brain MRI abnormalities

Diagnostic imaging represents one of the most promising clinical applications of artificial intelligence (AI) algorithms. The purpose of this study was to evaluate the customised convolutional neural network (CNN) SepNetDense for distinguishing between normal and abnormal canine brain magnetic resonance images (MRIs), with the aim of enhancing diagnostic efficiency and assisting radiologists in identifying abnormal images.The dataset comprised T1-weighted (T1w) pre- and post-contrast sequences in transverse, sagittal, and dorsal planes from 550 dogs, collected from four universities. Dogs were included if they had a complete clinical diagnosis confirmed either through histopathology or in accordance with current clinical consensus. Patients were randomly divided into a training set (n = 444), a validation set (n = 53), and a test set (n = 53). Each MRI was labelled on a slice-by-slice basis as normal or abnormal. The model was trained on 205 normal imaging datasets (e.g., extracranial aetiologies, idiopathic epilepsy, paroxysmal dyskinesia) and 239 abnormal ones (e.g., neoplasms, inflammatory lesions, other pathologies).The model correctly predicted 74% of the true normal slices in the test set as normal and 73% of the true abnormal as abnormal. A ROC analysis of the model’s prediction at the patient level revealed that, at a threshold of 51% abnormal slices per patient, the model reached an optimal balance of 83% sensitivity and 78% specificity, with a maximal accuracy of 80%. ANCOVA revealed that the CNN’s classification performance was influenced by multiple biological, technical, and institutional factors. Lesion diagnosis, institutional setting, and breed size had significant large effects, whereas body weight showed a significant medium effect. Significant interactions with large effect sizes were also observed between diagnosis and institute, as well as between breed size and weight. Additionally, a distinct pattern was observed in the distribution of prediction categories along the anatomical axes, with a trend towards better CNN performance in the central quartiles across all MRI sequences. The T1w pre-contrast sagittal sequence demonstrated the highest classification accuracy (81.8%) compared to the other T1w sequences. This study evaluates a CNN-based model designed to support a triage system for classifying canine brain MRI studies, with the aim of identifying abnormalities and improving reporting efficiency.