Tamai S
Department of Laboratory Medicine, National Defense Medical College, Tokorozawa.
Rinsho Byori. 1999 Feb;47(2):126-31.
In this decade, the pathological information system has gradually been settled in many hospitals in Japan. Pathological reports and images are now digitized and managed in the database, and are referred by clinicians at the peripherals. Tele-pathology is also developing; and its users are increasing. However, in many occasions, the problem solving in diagnostic pathology is completely dependent on the solo-pathologist. Considering the need for timely and efficient supports to the solo-pathologist, I reviewed the papers on the knowledge-based interactive expert systems. The interpretations of the histopathological images are dependent on the pathologist, and these expert systems have been evaluated as "educational". With the view of the success in the cytological screening, the development of "image-analysis-based" automatic "histopathological image" classifier has been on ongoing challenges. Our 3 years experience of the development of the pathological image classifier using the artificial neural networks technology is briefly presented. This classifier provides us a "fitting rate" for the individual diagnostic pattern of the breast tumors, such as "fibroadenoma pattern". The diagnosis assisting system with computer technology should provide pathologists, especially solo-pathologists, a useful tool for the quality assurance and improvement of pathological diagnosis.
在这十年间,病理信息系统已逐渐在日本的许多医院中落地。如今,病理报告和图像已被数字化并存储于数据库中,供周边的临床医生查阅。远程病理学也在不断发展,其用户数量日益增加。然而,在很多情况下,诊断病理学中的问题解决完全依赖于个体病理学家。考虑到需要及时且高效地支持个体病理学家,我查阅了关于基于知识的交互式专家系统的论文。组织病理学图像的解读依赖于病理学家,这些专家系统被评价为具有“教育意义”。鉴于细胞学筛查取得的成功,开发基于图像分析的自动组织病理学图像分类器一直是一项持续的挑战。本文简要介绍了我们运用人工神经网络技术开发病理图像分类器的三年经验。该分类器为乳腺肿瘤的个体诊断模式,如“纤维腺瘤模式”,提供了一个“拟合率”。借助计算机技术的诊断辅助系统应为病理学家,尤其是个体病理学家,提供一个用于病理诊断质量保证和提升的有用工具。