Department Clinical Sciences, Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, USA.
Department of Biostatistics, São Paulo State University. R. Prof. Dr. Antônio Celso Wagner Zanin, São Paulo, Brazil.
Vet Radiol Ultrasound. 2022 Sep;63(5):573-579. doi: 10.1111/vru.13089. Epub 2022 Apr 22.
The use of artificial intelligence (AI) algorithms in diagnostic radiology is a developing area in veterinary medicine and may provide substantial benefit in many clinical settings. These range from timely image interpretation in the emergency setting when no boarded radiologist is available to allowing boarded radiologists to focus on more challenging cases that require complex medical decision making. Testing the performance of artificial intelligence (AI) software in veterinary medicine is at its early stages, and only a scant number of reports of validation of AI software have been published. The purpose of this study was to investigate the performance of an AI algorithm (Vetology AI ) in the detection of pleural effusion in thoracic radiographs of dogs. In this retrospective, diagnostic case-controlled study, 62 canine patients were recruited. A control group of 21 dogs with normal thoracic radiographs and a sample group of 41 dogs with confirmed pleural effusion were selected from the electronic medical records at the Cummings School of Veterinary Medicine. The images were cropped to include only the area of interest (i.e., thorax). The software then classified images into those with pleural effusion and those without. The AI algorithm was able to determine the presence of pleural effusion with 88.7% accuracy (P < 0.05). The sensitivity and specificity were 90.2% and 81.8%, respectively (positive predictive value, 92.5%; negative predictive value, 81.8%). The application of this technology in the diagnostic interpretation of thoracic radiographs in veterinary medicine appears to be of value and warrants further investigation and testing.
人工智能（AI）算法在兽医诊断放射学中的应用是一个正在发展的领域，可能在许多临床环境中带来实质性的益处。这些益处包括在没有执业放射科医生的紧急情况下及时进行图像解释，以及让执业放射科医生专注于需要复杂医学决策的更具挑战性的病例。兽医人工智能软件性能的测试仍处于早期阶段，只有少数关于人工智能软件验证的报告已经发表。本研究旨在调查一种人工智能算法（Vetology AI）在检测犬胸部放射片中胸腔积液的性能。在这项回顾性诊断病例对照研究中，共招募了 62 只犬。从 Cummings 兽医学院的电子病历中选择了一个对照组（21 只胸部放射图正常的犬）和一个样本组（41 只确诊胸腔积液的犬）。将图像裁剪为仅包含感兴趣区域（即胸部）。然后，软件将图像分类为有胸腔积液和无胸腔积液。该人工智能算法能够以 88.7%的准确率（P<0.05）确定胸腔积液的存在。灵敏度和特异性分别为 90.2%和 81.8%（阳性预测值为 92.5%；阴性预测值为 81.8%）。这项技术在兽医胸部放射图诊断解释中的应用似乎具有价值，值得进一步研究和测试。
