ArticleVeterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association2020
Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs.
Article in Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 63 citations in OpenAlex.
- Review of applications of deep learning in veterinary diagnostics and animal health.Frontiers in veterinary science · 2025Pooled it
- Development of an artificial intelligence-based algorithm for the detection of left atrial enlargement from feline thoracic radiographs.The veterinary quarterly · 2026Article
- Bibliometric Analysis of Artificial Intelligence Use in Small Animal Thoracic Radiography.Veterinary medicine and science · 2026Article
- Lung ultrasound interpretation using deep learning for the detection of B-lines in dogs.Journal of veterinary internal medicine · 2026Article
- Deep Learning-Based Automated Anatomical Landmark Detection and Saw Blade Size Prediction for Canine Tibial Plateau Leveling Osteotomy.Animals : an open access journal from MDPI · 2026Article
- The Expanding Role of Artificial Intelligence in Companion Animal Care: A Systematic Review.Animals : an open access journal from MDPI · 2026Review
- The influence, promise, and potential perils of artificial intelligence in veterinary medicine: a call for improved awareness and literacy.Journal of veterinary internal medicine · 2026Article
- Article
- Validation of deep learning enabled software MetronMind to measure vertebral heart size and vertebral left atrial size in dogs.PloS one · 2026Article
- Machine learning based diagnostics of veterinary cancer on ultrasound and optical imaging data.The veterinary quarterly · 2025Article
- Raman spectral band imaging for the diagnostics and classification of canine and feline cutaneous tumors.The veterinary quarterly · 2025Article
- Applications and Considerations of Artificial Intelligence in Veterinary Sciences: A Narrative Review.Veterinary medicine and science · 2025Review
- Artificial Intelligence in Chest Radiography-A Comparative Review of Human and Veterinary Medicine.Veterinary sciences · 2025Review
- A Veterinary DICOM-Based Deep Learning Denoising Algorithm Can Improve Subjective and Objective Brain MRI Image Quality.Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association · 2025Article
- Vertebral Heart Score and Vertebral Left Atrial Size as Radiographic Measurements for Cardiac Size in Dogs-A Literature Review.Animals : an open access journal from MDPI · 2025Review
- Regressive vision transformer for dog cardiomegaly assessment.Scientific reports · 2024Article
- Deep learning models for interpretation of point of care ultrasound in military working dogs.Frontiers in veterinary science · 2024Article
- Deep learning-based ultrasonographic classification of canine chronic kidney disease.Frontiers in veterinary science · 2024Article
- Construction of diagnostic prediction model for canine nasal diseases using less invasive examinations without anesthesia.The Journal of veterinary medical science · 2023Article
- Artificial Intelligence in Veterinary Imaging: An Overview.Veterinary sciences · 2023Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although deep learning has been explored extensively for computer-aided medical imaging diagnosis in human medicine, very little has been done in veterinary medicine. The goal of this retrospective, pilot project was to apply the deep learning artificial intelligence technique using thoracic radiographs for detection of canine left atrial enlargement and compare results with those of veterinary radiologist interpretations. Seven hundred ninety-two right lateral radiographs from canine patients with thoracic radiographs and contemporaneous echocardiograms were used to train, validate, and test a convolutional neural network algorithm. The accuracy, sensitivity, and specificity for determination of left atrial enlargement were then compared with those of board-certified veterinary radiologists as recorded on radiology reports. The accuracy, sensitivity, and specificity were 82.71%, 68.42%, and 87.09%, respectively, using an accuracy driven variant of the convolutional neural network algorithm and 79.01%, 73.68%, and 80.64%, respectively, using a sensitivity driven variant. By comparison, accuracy, sensitivity, and specificity achieved by board-certified veterinary radiologists was 82.71%, 68.42%, and 87.09%, respectively. Although overall accuracy of the accuracy driven convolutional neural network algorithm and veterinary radiologists was identical, concordance between the two approaches was 85.19%. This study documents proof-of-concept for application of deep learning techniques for computer-aided diagnosis in veterinary medicine.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.