Pereira Ana Inês, Franco-Gonçalo Pedro, Leite Pedro, Ribeiro Alexandrine, Alves-Pimenta Maria Sofia, Colaço Bruno, Loureiro Cátia, Gonçalves Lio, Filipe Vítor, Ginja Mário
Department of Veterinary Science, University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal.
Veterinary and Animal Research Centre (CECAV), University of Trás-os-Montes and Alto Douro (UTAD), 5000-801 Vila Real, Portugal.
Vet Sci. 2023 Apr 28;10(5):320. doi: 10.3390/vetsci10050320.
Artificial intelligence and machine learning have been increasingly used in the medical imaging field in the past few years. The evaluation of medical images is very subjective and complex, and therefore the application of artificial intelligence and deep learning methods to automatize the analysis process would be very beneficial. A lot of researchers have been applying these methods to image analysis diagnosis, developing software capable of assisting veterinary doctors or radiologists in their daily practice. This article details the main methodologies used to develop software applications on machine learning and how veterinarians with an interest in this field can benefit from such methodologies. The main goal of this study is to offer veterinary professionals a simple guide to enable them to understand the basics of artificial intelligence and machine learning and the concepts such as deep learning, convolutional neural networks, transfer learning, and the performance evaluation method. The language is adapted for medical technicians, and the work already published in this field is reviewed for application in the imaging diagnosis of different animal body systems: musculoskeletal, thoracic, nervous, and abdominal.
在过去几年中，人工智能和机器学习在医学成像领域的应用越来越广泛。医学图像的评估非常主观且复杂，因此应用人工智能和深度学习方法使分析过程自动化将非常有益。许多研究人员一直在将这些方法应用于图像分析诊断，开发能够在日常实践中协助兽医或放射科医生的软件。本文详细介绍了用于开发机器学习软件应用程序的主要方法，以及对该领域感兴趣的兽医如何从这些方法中受益。本研究的主要目标是为兽医专业人员提供一个简单的指南，使他们能够理解人工智能和机器学习的基础知识以及深度学习、卷积神经网络、迁移学习和性能评估方法等概念。语言针对医学技术人员进行了调整，并对该领域已发表的工作进行了综述，以应用于不同动物身体系统（肌肉骨骼、胸部、神经和腹部）的成像诊断。
