Banzato Tommaso, Wodzinski Marek, Tauceri Federico, Donà Chiara, Scavazza Filippo, Müller Henning, Zotti Alessandro
Department of Animal Medicine, Production and Health, University of Padua, Legnaro, Italy.
Department of Measurement and Electronics, AGH University of Science and Technology, Krakow, Poland.
Front Vet Sci. 2021 Oct 15;8:731936. doi: 10.3389/fvets.2021.731936. eCollection 2021.
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.
开发并测试了一种基于人工智能（AI）的计算机辅助检测（CAD）算法，用于检测猫科动物胸部一些最常见的放射学表现。用于训练的数据库包含在两个不同机构获取的X光片。用于训练的数据库仅包括曝光正确且位置正确的X光片。记录了几种放射学表现的存在情况。因此，用于训练的放射学表现包括：无异常、支气管影像、胸腔积液、肿块、肺泡影像、气胸、心脏肥大。使用多标签卷积神经网络（CNN）开发CAD算法，并比较了两种不同CNN架构ResNet 50和Inception V3的性能。两种架构对于肺泡影像、支气管影像和胸腔积液的受试者工作特征曲线下面积（AUC）均高于0.9，对于无异常和气胸的AUC高于0.8，对于心脏肥大的AUC高于0.7。两种架构的肿块AUC都很低（高于0.5）。两种架构的诊断准确性均无明显差异。
