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兽医病理学的数字化革命。

The digital revolution in veterinary pathology.

机构信息

The Royal Veterinary College, Hawkshead Campus, Hawkshead Lane, Hatfield, Hertfordshire AL9 7TA, UK.

IDEXX Laboratories Ltd, Grange House, Sandbeck Way, Wetherby, Yorkshire LS22 7DN, UK.

出版信息

J Comp Pathol. 2024 Oct;214:19-31. doi: 10.1016/j.jcpa.2024.08.001. Epub 2024 Sep 5.

DOI:10.1016/j.jcpa.2024.08.001
PMID:39241697
Abstract

For the past two centuries, the use of traditional light microscopy to examine tissues to make diagnoses has remained relatively unchanged. While the fundamental concept of tissue slide analysis has stayed the same, our interaction with the microscope is undergoing significant changes. Digital pathology (DP) has gained momentum in veterinary science and is on the verge of becoming a vital tool in diagnostics, research and education. Many diagnostic laboratories have incorporated DP as a critical part of their workflows. Innovations in DP and whole slide image technology have made telediagnosis (the process of transmitting digital clinical data using telecommunication networks for distant diagnosis) more accessible, leading to improved patient care through streamlining of workflows and greater accessibility of second opinions. The integration of machine learning and artificial intelligence and human-in-the-loop protocols for DP workflows will further the development of computer-aided diagnosis and prognostic tools. Despite its present weaknesses, DP will progressively aid veterinary clinicians and pathologists in delivering more accurate and reliable diagnoses. Consistent incorporation of DP frontline advancements into routine veterinary diagnostic pipelines will assist in improving current tools and help prepare pathologists for the progression of digitalization in the field.

摘要

在过去的两个世纪里,使用传统的光学显微镜检查组织以做出诊断的方法基本保持不变。虽然组织切片分析的基本概念保持不变,但我们与显微镜的交互方式正在发生重大变化。数字病理学(DP)在兽医科学中得到了迅猛的发展,并且即将成为诊断、研究和教育中不可或缺的工具。许多诊断实验室已经将 DP 纳入其工作流程的重要组成部分。DP 和全玻片图像技术的创新使得远程诊断(使用远程通信网络传输数字临床数据进行远程诊断的过程)更加便捷,通过简化工作流程和更容易获得第二意见,从而改善了患者的护理。将机器学习和人工智能以及 DP 工作流程中的人机交互协议进行集成,将进一步开发计算机辅助诊断和预后工具。尽管 DP 目前存在一些弱点,但它将逐渐帮助兽医临床医生和病理学家做出更准确和可靠的诊断。将 DP 的前沿进展持续纳入常规兽医诊断工作流程,将有助于改进当前的工具,并帮助病理学家为该领域的数字化发展做好准备。

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