Article(id=1236331738545312682, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1236331736221676464, articleNumber=null, orderNo=null, doi=null, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=1687881600000, acceptedDateStr=2023-06-28, onlineDate=1772694056401, onlineDateStr=2026-03-05, pubDate=1699977600000, pubDateStr=2023-11-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1772694056401, onlineIssueDateStr=2026-03-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1772694056401, creator=13701087609, updateTime=1772694056401, updator=13701087609, issue=Issue{id=1236331736221676464, tenantId=1146029695717560320, journalId=1235980733773295621, year='2023', volume='32', issue='21', pageStart='2121', pageEnd='2224', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1772694055848, creator=13701087609, updateTime=1772696903836, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1236343681607856366, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1236331736221676464, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1236343681607856367, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1236331736221676464, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=2147, endPage=2154, ext={EN=ArticleExt(id=1236331738759222199, articleId=1236331738545312682, tenantId=1146029695717560320, journalId=1235980733773295621, language=EN, title=Overview of approaches and applications of deep learning in toxicologic pathology during nonclinical safety evaluation of drugs, columnId=null, journalTitle=Chinese Journal of New Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Histopathology data of toxicology studies during nonclinical safety evaluation of drugs are critical during the drug discovery and development process that is necessary for regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Artificial intelligence (AI) has been widely used in clinical medical practice with progress in the whole slide image (WSI), digital pathology and algorithms. However, the progress of machine learning (ML)-in particular, deep learning (DL)-has been rather slow in toxicologic pathology of nonclinical toxicology studies. This paper briefly reviews the role of toxicologic pathology in drug discovery and development, overview of AI and approaches of DL, applications of approaches of DL in toxicologic pathology, as well as the challenges in implementation of approaches of DL in toxicologic pathology, in order to provide reference for implementation of approaches of DL in toxicologic pathology for toxicology studies during nonclinical safety evaluation of drugs in China.

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药物非临床安全性评价毒理学试验组织病理学数据在药物发现和开发过程中非常关键，是监管部门在实验动物中评估药物相关毒性及其对临床试验人类安全性影响所必需的。随着全切片图像、数字病理学和算法研究的进步，人工智能已广泛应用于人类临床医学实践。但机器学习，尤其是深度学习在非临床毒理学试验毒性病理学的进展比较缓慢。本文简要综述了毒性病理学在药物发现和开发中的作用、人工智能和深度学习方法概述、深度学习方法在毒性病理学中的应用以及在毒性病理学中应用深度学习方法所面临的挑战，以期为我国药物非临床安全性评价毒理学试验组织病理学检查应用深度学习方法提供一定参考。

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张頔，女，本科，主要从事药物非临床安全性评价工作。E-mail: 1374390958@qq.com。

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张頔，女，本科，主要从事药物非临床安全性评价工作。E-mail: 1374390958@qq.com。

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闫振龙，男，硕士，主要从事药物非临床安全性评价毒性病理学诊断工作。E-mail: zlyan-1@innostar.cn。

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闫振龙，男，硕士，主要从事药物非临床安全性评价毒性病理学诊断工作。E-mail: zlyan-1@innostar.cn。

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