Interpreting Thoracic Radiograph of Small Animals Assisted by Convolutional Neural Network Models: A Review Study

Background: Artificial intelligence (AI) refers to the capability of computer systems to perform tasks typically associated with human reasoning, decision making, and pattern recognition. Diagnostic imaging plays an essential role in both human and veterinary medicine, providing vital information for disease detection and treatment planning. Among AI techniques, Convolutional Neural Networks (CNNs), a subclass of deep learning models have shown high efficiency in interpreting complex visual data due to their mathematical convolution operations specialized for image analysis. Methods: This review study summarizes previous investigations on CNN applications in interpreting thoracic radiographs of small animals, focusing on their performance compared with that of veterinary radiologists. Datasets used in these studies were collected from multiple veterinary institutions, and images with poor positioning or exposure were excluded. The remaining labeled images were divided into training and test sets using transfer learning techniques such as pretraining on ImageNet. Model performance was assessed through accuracy, sensitivity, and specificity values derived statistically. Results: Findings from the reviewed studies revealed that models such as ResNet ۵۰ and DenseNet ۱۲۱ achieved overall accuracies typically higher than ۸۰%, showing diagnostic performance comparable to, or exceeding, that of veterinary radiologists for conditions like cardiomegaly, pleural effusion, and pneumothorax. However, detection of subtle lesions (e.g., small masses or interstitial patterns) remained challenging. Conclusion: Most investigations concluded that CNN systems could serve as valuable diagnostic assistants in veterinary thoracic radiology, especially under emergency or high workload conditions. Further comparative studies between board certified radiologists and AI models are required to quantify error rates, improve diagnostic reliability, and establish standardized AI assisted protocols for clinical use in veterinary radiology.

کلیدواژه ها:

Artificial intelligence ، Convolutional neural network ، Thoracic radiography ، Small animals