ArticleScientific reports2023
An AI-based algorithm for the automatic evaluation of image quality in canine thoracic radiographs.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Bibliometric Analysis of Artificial Intelligence Use in Small Animal Thoracic Radiography.Veterinary medicine and science · 2026Article
- The Expanding Role of Artificial Intelligence in Companion Animal Care: A Systematic Review.Animals : an open access journal from MDPI · 2026Review
- Deep learning-based assessment of periapical radiographic image quality.Scientific reports · 2026Article
- Physical models and simulators in veterinary education: current status, learning impact, and future perspectives.Frontiers in veterinary science · 2026Review
- Automated segmentation of canine pulmonary masses in CT imaging using AI.The veterinary quarterly · 2025Article
- Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors.The veterinary quarterly · 2025Article
- Commentary: Comparison of radiological interpretation made by veterinary radiologists and state-of-the-art commercial AI software for canine and feline radiographic studies.Frontiers in veterinary science · 2025Article
- Automated AI-based segmentation of canine hepatic focal lesions from CT studies.Frontiers in veterinary science · 2025Article
- Role of AI in diagnostic imaging error reduction.Frontiers in veterinary science · 2024Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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. The algorithm was able to correctly identify errors in thoracic radiographs with an overall accuracy of 81.5% in latero-lateral and 75.7% in sagittal images. The most accurately identified errors were limb mispositioning and underexposure both in latero-lateral and sagittal images. The accuracy of the developed model in the classification of technically correct radiographs was fair in latero-lateral and good in sagittal images. The authors conclude that their AI-based algorithm is a promising tool for improving the accuracy of radiographic interpretation by identifying technical errors in canine thoracic radiographs.
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Registered trials
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