Pathology Department, 25913Charles River Laboratories, Frederick, MD, USA.
Pathology Department, 25913Charles River Laboratories, Durham, NC, USA.
Toxicol Pathol. 2021 Jun;49(4):773-783. doi: 10.1177/0192623320980310. Epub 2020 Dec 29.
Digital tissue image analysis is a computational method for analyzing whole-slide images and extracting large, complex, and quantitative data sets. However, as with any analysis method, the quality of generated results is dependent on a well-designed quality control system for the entire digital pathology workflow. Such system requires clear procedural controls, appropriate user training, and involvement of specialists to oversee key steps of the workflow. The toxicologic pathologist is responsible for reporting data obtained by digital image analysis and therefore needs to ensure that it is correct. To accomplish that, they must understand the main parameters of the quality control system and should play an integral part in its conception and implementation. This manuscript describes the most common digital tissue image analysis end points and potential sources of analysis errors. In addition, it outlines recommended approaches for ensuring quality and correctness of results for both classical and machine-learning based image analysis solutions, as adapted from a recently proposed Food and Drug Administration regulatory framework for modifications to artificial intelligence/machine learning-based software as a medical device. These approaches are beneficial for any type of toxicopathologic study which uses the described end points and can be adjusted based on the intended use of the image analysis solution.
数字组织图像分析是一种用于分析全切片图像并提取大型、复杂和定量数据集的计算方法。然而，与任何分析方法一样，生成结果的质量取决于整个数字病理学工作流程的精心设计的质量控制系统。这样的系统需要明确的程序控制、适当的用户培训以及专家的参与，以监督工作流程的关键步骤。毒理学病理学家负责报告数字图像分析获得的数据，因此需要确保其正确性。为了实现这一目标，他们必须了解质量控制系统的主要参数，并应成为其构思和实施的重要组成部分。本文描述了最常见的数字组织图像分析终点和分析误差的潜在来源。此外，它还概述了为基于经典和机器学习的图像分析解决方案确保结果质量和正确性的建议方法，这些方法改编自最近提出的食品和药物管理局(FDA)的人工智能/机器学习软件修改的监管框架，作为医疗器械。这些方法适用于使用所述终点的任何类型的毒理学研究，并可以根据图像分析解决方案的预期用途进行调整。
