Overview of quality control for digital tissue image analysis in artificial intelligence and toxicologic pathology
With the rapid development of artificial intelligence(AI)and machine learning(ML),the diagnosis of whole slide images(WSIs)by AI is almost comparable to that by pathologists.Establishment of algorithms of AI needs a large number of digital tissue image training set data.Digital tissue image analysis analyzes WSIs through various algorithms and extracts a large number of complex quantitative data sets from WSIs.The quality control(QC)of digital tissue image analysis is not only very important but also the basis and premise to ensure the establishment of high-quality data sets and AI algorithms.The paper briefly overviews the QC strategy for WSIs,factors affecting digital tissue image analysis,QC methods of digital tissue image analysis,QC methods of results of the digital tissue image analysis,roles of toxicologic pathologists in digital tissue image analysis,data interpretation and reporting,as well as the challenges of using digital tissue images in toxicologic pathology diagnosis and AI itself,hoping to provide references for using WSIs in toxicologic pathology diagnosis and establishing various AI algorithms in toxicity studies of non-clinical safety evaluation of drugs in China.
NETL
NSTL
万方数据
