Publications by authors named "S Burti"
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 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 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.
The aim of this study was to develop and test an artificial intelligence (AI)-based algorithm for detecting common technical errors in canine thoracic radiography. The algorithm was trained using a database of thoracic radiographs from three veterinary clinics in Italy, which were evaluated for image quality by three experienced veterinary diagnostic imagers. The algorithm was designed to classify the images as correct or having one or more of the following errors: rotation, underexposure, overexposure, incorrect limb positioning, incorrect neck positioning, blurriness, cut-off, or the presence of foreign objects, or medical devices.
View Article and Find Full Text PDFBackground: The contrast-enhanced ultrasound (CEUS) features of adrenal lesions are poorly reported in veterinary literature.
Methods: Qualitative and quantitative B-mode ultrasound and CEUS features of 186 benign (adenoma) and malignant (adenocarcinoma and pheochromocytoma) adrenal lesions were evaluated.
Results: Adenocarcinomas (n = 72) and pheochromocytomas (n = 32) had mixed echogenicity with B-mode, and a non-homogeneous aspect with a diffused or peripheral enhancement pattern, hypoperfused areas, intralesional microcirculation and non-homogeneous wash-out with CEUS.