Department of Clinical Sciences, Tufts University Cummings School of Veterinary Medicine, North Grafton, Massachusetts, USA.
Vet Radiol Ultrasound. 2023 Sep;64(5):881-889. doi: 10.1111/vru.13287. Epub 2023 Aug 7.
Advancements in the field of artificial intelligence (AI) are modest in veterinary medicine relative to their substantial growth in human medicine. However, interest in this field is increasing, and commercially available veterinary AI products are already on the market. In this retrospective, diagnostic accuracy study, the accuracy of a commercially available convolutional neural network AI product (Vetology AI®) is assessed on 56 thoracic radiographic studies of pulmonary nodules and masses, as well as 32 control cases. Positive cases were confirmed to have pulmonary pathology consistent with a nodule/mass either by CT, cytology, or histopathology. The AI software detected pulmonary nodules/masses in 31 of 56 confirmed cases and correctly classified 30 of 32 control cases. The AI model accuracy is 69.3%, balanced accuracy 74.6%, F1-score 0.7, sensitivity 55.4%, and specificity 93.75%. Building on these results, both the current clinical relevance of AI and how veterinarians can be expected to use available commercial products are discussed.
与在人类医学中的大量应用相比，人工智能（AI）在兽医领域的进展较为有限。然而，兽医领域对 AI 的兴趣正在不断增加，且市面上已有商业化的兽医 AI 产品。在本回顾性诊断准确性研究中，评估了一款商业化的卷积神经网络 AI 产品（Vetology AI®）在 56 例肺部结节和肿块的放射学研究以及 32 例对照病例中的准确性。阳性病例通过 CT、细胞学或组织病理学检查，证实存在与结节/肿块一致的肺部病变。该 AI 软件在 56 例确诊病例中检测到 31 例肺部结节/肿块，并正确分类了 32 例对照病例中的 30 例。AI 模型的准确率为 69.3%，平衡准确率为 74.6%，F1 得分为 0.7，灵敏度为 55.4%，特异性为 93.75%。基于这些结果，讨论了 AI 当前的临床相关性以及兽医如何预期使用现有商业化产品。
