Abstract
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.
Highlights
Plain radiographs are, nowadays, a widely used diagnostic imaging tool used in the veterinary clinical routine to investigate the thorax in small animals
The findings included for training were: no findings, bronchial pattern, pleural effusion, mass, alveolar pattern, pneumothorax, cardiomegaly (Figure 1)
The complete classification results in the test set for ResNet 50 and for Inception V3 (IncV3) are reported in Tables 2, 3, respectively
Summary
Nowadays, a widely used diagnostic imaging tool used in the veterinary clinical routine to investigate the thorax in small animals. Often the decision whether to perform additional, and more advanced, imaging investigations is based on the results of plain radiographs. In such a scenario, the correct interpretation of plain radiographs is paramount in prescribing successful treatment. The reported incidence of interpretation errors (in human medicine) for trained radiologists is still around 10–15% [1,2,3]. The incidence of interpretation errors in veterinary medicine has not yet been reported
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