Comparison of error rates between four pretrained DenseNet convolutional neural network models and 13 board‐certified veterinary radiologists when evaluating 15 labels of canine thoracic radiographs-Reference-Cited by-同舟云学术

Comparison of error rates between four pretrained DenseNet convolutional neural network models and 13 board‐certified veterinary radiologists when evaluating 15 labels of canine thoracic radiographs

Published:2022-02-08 Issue:4 Volume:63 Page:456-468
ISSN:1058-8183
Container-title:Veterinary Radiology & Ultrasound
language:en
Short-container-title:Vet Radiology Ultrasound

Author:

Adrien‐Maxence Hespel¹^ORCID,Emilie Boissady²,Alois De La Comble²,Michelle Acierno³^ORCID,Kate Alexander⁴^ORCID,Mylene Auger⁵^ORCID,David Biller⁶,Marie de Swarte⁷,Jason Fuerst⁸,Eric Green⁹,Séamus Hoey¹⁰^ORCID,Kevin Koernig¹¹,Alison Lee¹²^ORCID,Megan MacLellan¹³,Hester McAllister¹⁰,Jaime Rechy Jr¹⁴,Zhu Xiaojuan¹⁵,Micaela Zarelli¹¹,Federica Morandi¹

Affiliation:

1. Department of Small Animal Clinical Sciences University of Tennessee Knoxville Tennessee USA

2. PicoxIA Maisons‐Alfort France

3. Michelle Acierno Veterinary Radiology Consulting, Kirkland WA and Summit Veterinary Referral Center Tacoma Washington USA

4. DMV Veterinary Center Diagnostic Imaging Montreal Quebec Canada

5. Animages Longueil Quebec Canada

6. Kansas State University College of Veterinary Medicine Clinical Sciences Manhattan Kansas USA

7. VetCT Orlando Florida USA

8. VCA Santa Monica California USA

9. The Ohio State University Veterinary Clinical Sciences Columbus Ohio USA

10. University College Dublin Veterinary Diagnostic Imaging Dublin Ireland

11. AIS Fountain Valley California USA

12. Mississippi State University College of Veterinary Medicine Department of Clinical Sciences Starkville Mississippi USA

13. BluePearl Veterinary Partners Elden Prairie Minnesota USA

14. Bluepearl Veterinary Partners Franklin Tennessee USA

15. Office of Information Technology The University of Tennessee Knoxville Tennessee USA

Publisher

Wiley

Subject

General Veterinary

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/vru.13069

Reference39 articles.

1. Automatic classification of canine thoracic radiographs using deep learning

2. Pilot study: Application of artificial intelligence for detecting left atrial enlargement on canine thoracic radiographs

3. Use of deep learning to detect cardiomegaly on thoracic radiographs in dogs

4. Artificial intelligence evaluating primary thoracic lesions has an overall lower error rate compared to veterinarians or veterinarians in conjunction with the artificial intelligence

5. Prediction of radiographic abnormalities by the use of bag-of-features and convolutional neural networks

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