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.

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ID: 275726
2022
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Ranked #49 of 138 articles by views in Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association

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Abstract
Convolutional neural networks (CNNs) are commonly used as artificial intelligence (AI) tools for evaluating radiographs, but published studies testing their performance in veterinary patients are currently lacking. The purpose of this retrospective, secondary analysis, diagnostic accuracy study was to compare the error rates of four CNNs to the error rates of 13 veterinary radiologists for evaluating canine thoracic radiographs using an independent gold standard. Radiographs acquired at a referral institution were used to evaluate the four CNNs sharing a common architecture. Fifty radiographic studies were selected at random. The studies were evaluated independently by three board-certified veterinary radiologists for the presence or absence of 15 thoracic labels, thus creating the gold standard through the majority rule. The labels included "cardiovascular," "pulmonary," "pleural," "airway," and "other categories." The error rates for each of the CNNs and for 13 additional board-certified veterinary radiologists were calculated on those same studies. There was no statistical difference in the error rates among the four CNNs for the majority of the labels. However, the CNN's training method impacted the overall error rate for three of 15 labels. The veterinary radiologists had a statistically lower error rate than all four CNNs overall and for five labels (33%). There was only one label ("esophageal dilation") for which two CNNs were superior to the veterinary radiologists. Findings from the current study raise numerous questions that need to be addressed to further develop and standardize AI in the veterinary radiology environment and to optimize patient care.
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adrienmaxence2022comparisonveterinary Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Adrien-Maxence, Hespel;Emilie, Boissady;Alois, De La Comble;Michelle, Acierno;Kate, Alexander;Mylene, Auger;David, Biller;Marie, de Swarte;Jason, Fuerst;Eric, Green;Séamus, Hoey;Kevin, Koernig;Alison, Lee;Megan, MacLellan;Hester, McAllister;Jaime, Rechy Jr;Zhu, Xiaojuan;Micaela, Zarelli;Federica, Morandi;
Journal Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Year 2022
DOI
10.1111/vru.13069
URL
Keywords
Machine learning Convolutional neural network dog radiology thorax

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