Article(id=1317115034447237362, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1317114999827452826, articleNumber=null, orderNo=null, doi=10.13193/j.issn.1673-7717.2026.06.003, 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=null, acceptedDateStr=null, onlineDate=1791954295025, onlineDateStr=2026-10-14, pubDate=1781020800000, pubDateStr=2026-06-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1791954295025, onlineIssueDateStr=2026-10-14, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1791954295025, creator=13701087609, updateTime=1791954295025, updator=13701087609, issue=Issue{id=1317114999827452826, tenantId=1146029695717560320, journalId=1255847919539208197, year='2026', volume='44', issue='6', pageStart='1', pageEnd='258', issueExtLink='null', onlineDate='null', pubDate='1781020800000', pubDateStr='2026-06-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1791954286772, creator='13701087609', updateTime=1791958677365, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1317133415753831385, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1317114999827452826, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1317133415753831386, tenantId=1146029695717560320, journalId=1255847919539208197, issueId=1317114999827452826, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=15, endPage=19, ext={EN=ArticleExt(id=1317115034652758259, articleId=1317115034447237362, tenantId=1146029695717560320, journalId=1255847919539208197, language=EN, title=Innovation Research on Traditional Chinese Medicine Eye Diagnosis Technology Assisted by Artificial Intelligence, columnId=1256183375241535778, journalTitle=Chinese Archives of Traditional Chinese Medicine, columnName=Digital Traditional Chinese Medicine, runingTitle=null, highlight=null, articleAbstract=

Traditional Chinese medicine (TCM) eye diagnosis, as one of the quintessences of TCM inspection, holds a significant role in clinical diagnosis. It also enjoys a solid theoretical foundation and a long history of development. The five-wheel and eight-region theories are the most important theories in TCM eye diagnosis. With the trend of TCM diagnosis objectivity, the modernization of TCM eye diagnosis has a strong development momentum. The author traced the evolution of TCM eye diagnosis theory, introduced the current technologies of eye diagnosis, and presented the team's latest research—TCM eye image recognition system. This system integrates TCM foundational eye diagnostic theory of “eight zones and eight acupoints” with detailed indicators from “four forms” and “eight colors” to establish a modern, precise framework for white eye image acquisition, processing and analysis by leveraging modern artificial intelligence (AI) technology. The paper outlined the construction of the eye diagnosis image recognition model, delved into the key technologies of system development, and proposed targeted solutions to existing challenges to promote the objectivity and precision of TCM diagnosis.

, authors=Meng SONG¹, Fei NI¹, Jiamei LI¹, Bingyuan ZHENG¹, Kun YI², Keda LI¹, Ke FENG¹, authorsList=Meng SONG, Fei NI, Jiamei LI, Bingyuan ZHENG, Kun YI, Keda LI, Ke FENG, authorCompany=null, correspAuthors=Keda LI, Ke FENG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1317115034992496884, articleId=1317115034447237362, tenantId=1146029695717560320, journalId=1255847919539208197, language=CN, title=AI辅助中医眼诊技术创新研究, columnId=1256183387140776248, journalTitle=中华中医药学刊, columnName=数字中医中药, runingTitle=null, highlight=null, articleAbstract=

中医眼诊作为中医望诊的精髓之一，在临床诊断中占据着举足轻重的地位。中医眼诊理论基础坚实，发展历史悠久，其中五轮、八廓学说为中医眼科最重要的理论。随着中医诊断客观化趋势的不断增强，中医眼诊现代化发展势头强劲。追溯中医眼诊理论的发展过程，阐述现有眼诊方法，在此基础上呈现了团队最新研究成果——中医眼像识别系统。该系统基于中医眼诊“八区八穴”理论，并融入了“四种形态”与“八种颜色”的详细指征，旨在通过现代人工智能（Artificial Intelligence，AI）技术手段，对白睛眼像进行精准采集、处理与分析，为临床诊断提供有力辅助。详细阐述了眼诊图像识别算法模型的构建过程，剖析了系统研发中的关键技术，并针对现有问题提出相应的解决方案，以促进中医诊断的客观化、精准化。

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宋梦（1987-），女，河北邢台人，博士在读，研究方向：中医药标准化与中医药翻译。

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宋梦（1987-），女，河北邢台人，博士在读，研究方向：中医药标准化与中医药翻译。

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