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

Burti, Silvia; Osti, Valentina Longhin; Zotti, Alessandro; Banzato, Tommaso

doi:10.1016/j.tvjl.2020.105505

Cited by 43 publications

(60 citation statements)

References 44 publications

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“…Currently, in human medicine, the main applications of AI on plain radiographs are related to the automatic detection of findings or pathologies (7,8). In the veterinary field, the scope to use AI-based algorithms to detect some radiographic findings has been explored in dogs over the last few years (9)(10)(11)(12). The same potential use has not yet been explored in cats.…”

Section: Introductionmentioning

confidence: 99%

An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats

et al. 2021

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An artificial intelligence (AI)-based computer-aided detection (CAD) algorithm to detect some of the most common radiographic findings in the feline thorax was developed and tested. The database used for training comprised radiographs acquired at two different institutions. Only correctly exposed and positioned radiographs were included in the database used for training. The presence of several radiographic findings was recorded. Consequenly, the radiographic findings included for training were: no findings, bronchial pattern, pleural effusion, mass, alveolar pattern, pneumothorax, cardiomegaly. Multi-label convolutional neural networks (CNNs) were used to develop the CAD algorithm, and the performance of two different CNN architectures, ResNet 50 and Inception V3, was compared. Both architectures had an area under the receiver operating characteristic curve (AUC) above 0.9 for alveolar pattern, bronchial pattern and pleural effusion, an AUC above 0.8 for no findings and pneumothorax, and an AUC above 0.7 for cardiomegaly. The AUC for mass was low (above 0.5) for both architectures. No significant differences were evident in the diagnostic accuracy of either architecture.

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Section: Introductionmentioning

confidence: 99%

An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats

et al. 2021

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“…The possible applications of machine learning algorithms have been widely explored in human diagnostic imaging in the last decades (15). On the other hand, in the last decade, the possibilities offered by this technology has raised an increasing interest also in veterinary medicine (16)(17)(18)(19)(20). Machine learning comprise a wide range of algorithms that can be broadly divided into machine learning and deep learning (21).…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases

et al. 2021

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To describe the computed tomographic (CT) features of focal liver lesions (FLLs) in dogs, that could enable predicting lesion histotype. Dogs diagnosed with FLLs through both CT and cytopathology and/or histopathology were retrospectively collected. Ten qualitative and 6 quantitative CT features have been described for each case. Lastly, a machine learning-based decision tree was developed to predict the lesion histotype. Four categories of FLLs - hepatocellular carcinoma (HCC, n = 13), nodular hyperplasia (NH, n = 19), other benign lesions (OBL, n = 18), and other malignant lesions (OML, n = 19) - were evaluated in 69 dogs. Five of the observed qualitative CT features resulted to be statistically significant in the distinction between the 4 categories: surface, appearance, lymph-node appearance, capsule formation, and homogeneity of contrast medium distribution. Three of the observed quantitative CT features were significantly different between the 4 categories: the Hounsfield Units (HU) of the radiologically normal liver parenchyma during the pre-contrast scan, the maximum dimension, and the ellipsoid volume of the lesion. Using the machine learning-based decision tree, it was possible to correctly classify NHs, OBLs, HCCs, and OMLs with an accuracy of 0.74, 0.88, 0.87, and 0.75, respectively. The developed decision tree could be an easy-to-use tool to predict the histotype of different FLLs in dogs. Cytology and histology are necessary to obtain the final diagnosis of the lesions.

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“…Finally, veterinary cardiologists and radiologists have very recently started the early application of deep learning, an artificial intelligence application that uses computer-aided detection methods with the aim of helping clinicians recognize cardiac or left atrial enlargement on canine thoracic radiographs [19,20].…”

Section: From Computed Tomography and Magnetic Resonance Imaging To Amentioning

confidence: 99%

Diagnostic Imaging of Cardiovascular Disease in Small Animals

Diana

Guglielmini

2020

Animals

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

Cited by 43 publications

References 44 publications

An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats

An AI-Based Algorithm for the Automatic Classification of Thoracic Radiographs in Cats

Diagnostic Accuracy of Delayed Phase Post Contrast Computed Tomographic Images in the Diagnosis of Focal Liver Lesions in Dogs: 69 Cases

Diagnostic Imaging of Cardiovascular Disease in Small Animals

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