Publications by authors named "T Banzato"
The topic of diagnostic imaging error and the tools and strategies for error mitigation are poorly investigated in veterinary medicine. The increasing popularity of diagnostic imaging and the high demand for teleradiology make mitigating diagnostic imaging errors paramount in high-quality services. The different sources of error have been thoroughly investigated in human medicine, and the use of AI-based products is advocated as one of the most promising strategies for error mitigation.
View Article and Find Full Text PDFA heart-convolutional neural network (heart-CNN) was designed and tested for the automatic classification of chest radiographs in dogs affected by myxomatous mitral valve disease (MMVD) at different stages of disease severity. A retrospective and multicenter study was conducted. Lateral radiographs of dogs with concomitant X-ray and echocardiographic examination were selected from the internal databases of two institutions.
View Article and Find Full Text PDFThe field of veterinary diagnostic imaging is undergoing significant transformation with the integration of artificial intelligence (AI) tools. This manuscript provides an overview of the current state and future prospects of AI in veterinary diagnostic imaging. The manuscript delves into various applications of AI across different imaging modalities, such as radiology, ultrasound, computed tomography, and magnetic resonance imaging.
View Article and Find Full Text PDFThe objective of this study was to assess changes in the echogenicity of the cortex and medulla of canine fetal kidneys in relation to days before parturition (dbp), maternal size and litter size. Monitoring of 10 healthy pregnant bitches (2-8 years old, 8.8-40.
View Article and Find Full Text PDFArticle Synopsis
- The analysis of veterinary radiographic imaging data is crucial for diagnosing thoracic lesions but is time-consuming for physicians; an automated system could streamline this process.
- Most current systems rely on supervised deep learning, which requires extensive labeled data that is often hard to obtain due to high costs and time commitments.
- This study introduces a novel approach using self-supervised learning techniques on publicly available unlabeled radiographic data to enhance classification accuracy, achieving mean ROC AUC scores of 0.77 and 0.66 in laterolateral and dorsoventral projections, respectively.