首页 期刊 论文 学科刊群 资讯 加盟 关于
EN
image
Article(id=1207433495325024476, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1207433493215289544, articleNumber=null, orderNo=null, doi=10.11855/j.issn.0577-7402.2022.08.0845, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1627488000000, receivedDateStr=2021-07-29, revisedDate=null, revisedDateStr=null, acceptedDate=1635696000000, acceptedDateStr=2021-11-01, onlineDate=1765804178315, onlineDateStr=2025-12-15, pubDate=1661616000000, pubDateStr=2022-08-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1765804178315, onlineIssueDateStr=2025-12-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1765804178315, creator=13701087609, updateTime=1765804178315, updator=13701087609, issue=Issue{id=1207433493215289544, tenantId=1146029695717560320, journalId=1189873630562394117, year='2022', volume='47', issue='8', pageStart='745', pageEnd='850', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1765804177811, creator=13701087609, updateTime=1765804292764, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1207433975413444883, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1207433493215289544, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1207433975413444884, tenantId=1146029695717560320, journalId=1189873630562394117, issueId=1207433493215289544, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=845, endPage=850, ext={EN=ArticleExt(id=1207433495715094762, articleId=1207433495325024476, tenantId=1146029695717560320, journalId=1189873630562394117, language=EN, title=Progress in application research of artificial intelligence in the field of chronic liver diseases, columnId=1190243275882729994, journalTitle=Medical Journal of Chinese People’s Liberation Army, columnName=Review, runingTitle=null, highlight=null, articleAbstract=

Artificial intelligence has made breakthroughs in medicine in the past decade. Compared with the traditional statistical model, the advantage of artificial intelligence is that it can establish algorithm and prediction model through machine learning to efficiently and effectively identify the patterns in large data sets, and combine a variety of factors to create more accurate prediction model. Therefore, artificial intelligence is particularly suitable for huge and complex or high-dimensional clinical data analysis and predictive modeling tasks. There are many kinds of data formats in the clinical practice of hepatology. Many studies have applied artificial intelligence in the diagnosis and classification of liver diseases, assisting treatment, predicting efficacy and prognosis, and evaluation of liver imaging and pathology. Based on the study outcomes in related fields at home and aboard, this paper summarizes the research progress and application of artificial intelligence in the field of diagnosis and treatment of chronic liver diseases.

, correspAuthors=Mao-Yun Guo, authorNote=null, correspAuthorsNote=
*E-mail:
, 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, authorCompany=null, fund=null, authors=null, authorsList=Ying-Zi Tang, Jie Xia, Hui-Min Liu, Hua-Jie Lv, Mao-Yun Guo), CN=ArticleExt(id=1207433496033861878, articleId=1207433495325024476, tenantId=1146029695717560320, journalId=1189873630562394117, language=CN, title=人工智能在慢性肝病领域中的应用研究进展, columnId=1190243276029530637, journalTitle=解放军医学杂志, columnName=综述, runingTitle=null, highlight=null, articleAbstract=

近十年来，人工智能已经在医学领域取得了突破性进展。与传统统计模型相比，人工智能的优势在于可通过机器学习建立算法及预测模型来高效、有效地识别目标数据集中的模式，并结合多种因素创建更为精确的预测模型，特别适用于具有海量高维数据特征的医学临床数据分析及预测建模任务。在肝脏病学的临床实践领域，已有越来越多的研究将人工智能应用于肝病的诊断分类、协助治疗、预测疗效及预后，以及协助肝脏疾病的影像学、病理学诊断等。本文结合国内外研究成果，总结人工智能在慢性肝病领域中的应用情况及研究进展。

, correspAuthors=郭茂耘, authorNote=null, correspAuthorsNote=
郭茂耘，E-mail：
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=Etxf0isv3In+HVRa0ZWeKQ==, magXml=ZmLz0vqiQmEjnE58wrdR4w==, pdfUrl=null, pdf=iBRrNvs1PNSg07V1h8DIBg==, pdfFileSize=771290, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=BEcHf3e6Sg0gMKONvs/rAQ==, mapNumber=null, authorCompany=null, fund=null, authors=

汤影子，医学硕士，主治医师，主要从事肝病方面的临床研究

, authorsList=汤影子, 夏杰, 刘慧敏, 吕化杰, 郭茂耘)}, authors=[Author(id=1207433497493479693, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1207433497682223383, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433497493479693, language=EN, stringName=Ying-Zi Tang, firstName=Ying-Zi, middleName=null, lastName=Tang, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1207433497774498076, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433497493479693, language=CN, stringName=汤影子, firstName=影子, middleName=null, lastName=汤, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1陆军军医大学第一附属医院感染科，重庆 400038, bio={"content":"

汤影子，医学硕士，主治医师，主要从事肝病方面的临床研究

"}, bioImg=null, bioContent=

汤影子，医学硕士，主治医师，主要从事肝病方面的临床研究

, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1207433496314880251, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, xref=1, ext=[AuthorCompanyExt(id=1207433496319074556, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China), AuthorCompanyExt(id=1207433496327463165, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学第一附属医院感染科，重庆 400038)])]), Author(id=1207433497866772770, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, orderNo=1, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1207433497975824683, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433497866772770, language=EN, stringName=Jie Xia, firstName=Jie, middleName=null, lastName=Xia, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1207433498068099378, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433497866772770, language=CN, stringName=夏杰, firstName=杰, middleName=null, lastName=夏, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1陆军军医大学第一附属医院感染科，重庆 400038, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1207433496314880251, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, xref=1, ext=[AuthorCompanyExt(id=1207433496319074556, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China), AuthorCompanyExt(id=1207433496327463165, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学第一附属医院感染科，重庆 400038)])]), Author(id=1207433498160374072, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, orderNo=2, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1207433498282008899, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433498160374072, language=EN, stringName=Hui-Min Liu, firstName=Hui-Min, middleName=null, lastName=Liu, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1207433498399449419, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433498160374072, language=CN, stringName=刘慧敏, firstName=慧敏, middleName=null, lastName=刘, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1陆军军医大学第一附属医院感染科，重庆 400038, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1207433496314880251, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, xref=1, ext=[AuthorCompanyExt(id=1207433496319074556, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China), AuthorCompanyExt(id=1207433496327463165, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学第一附属医院感染科，重庆 400038)])]), Author(id=1207433498542055760, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, orderNo=3, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1207433498630136153, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433498542055760, language=EN, stringName=Hua-Jie Lv, firstName=Hua-Jie, middleName=null, lastName=Lv, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1207433498726605150, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433498542055760, language=CN, stringName=吕化杰, firstName=化杰, middleName=null, lastName=吕, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, address=1陆军军医大学第一附属医院感染科，重庆 400038, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1207433496314880251, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, xref=1, ext=[AuthorCompanyExt(id=1207433496319074556, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China), AuthorCompanyExt(id=1207433496327463165, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学第一附属医院感染科，重庆 400038)])]), Author(id=1207433498814685540, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, orderNo=4, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=gmy@cqu.edu.cn, emailSecond=null, emailThird=null, correspondingAuthor=1, authorType=1, ext={EN=AuthorExt(id=1207433498898571631, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433498814685540, language=EN, stringName=Mao-Yun Guo, firstName=Mao-Yun, middleName=null, lastName=Guo, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=2, *, address=2School of Automation, Chongqing University, Chongqing 400044, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1207433499020206453, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, authorId=1207433498814685540, language=CN, stringName=郭茂耘, firstName=茂耘, middleName=null, lastName=郭, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=2, *, address=2重庆大学自动化学院，重庆 400044, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null)}, companyList=[AuthorCompany(id=1207433497380233478, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, xref=2, ext=[AuthorCompanyExt(id=1207433497397010697, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433497380233478, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2School of Automation, Chongqing University, Chongqing 400044, China), AuthorCompanyExt(id=1207433497409593609, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433497380233478, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2重庆大学自动化学院，重庆 400044)])])], keywords=[Keyword(id=1207433499162812802, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, language=EN, orderNo=1, keyword=artificial intelligence), Keyword(id=1207433499246698888, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, language=EN, orderNo=2, keyword=machine learning), Keyword(id=1207433499364139409, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, language=EN, orderNo=3, keyword=chronic liver diseases), Keyword(id=1207433499456414105, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, language=CN, orderNo=1, keyword=人工智能), Keyword(id=1207433499544494493, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, language=CN, orderNo=2, keyword=机器学习), Keyword(id=1207433499636769191, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, language=CN, orderNo=3, keyword=慢性肝病)], refs=[Reference(id=1207433499729043891, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2019, volume=70, issue=1, pageStart=151, pageEnd=171, url=null, language=null, rfNumber=[1], rfOrder=0, authorNames=Asrani SK, Devarbhavi H, Eaton J, journalName=J Hepatol, refType=null, unstructuredReference=Asrani SK, Devarbhavi H, Eaton J, et al. Burden of liver diseases in the world[J]. J Hepatol, 2019, 70(1): 151-171., articleTitle=Burden of liver diseases in the world, refAbstract=null), Reference(id=1207433499821318584, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=45, issue=9, pageStart=990, pageEnd=995, url=null, language=null, rfNumber=[2], rfOrder=1, authorNames=Gao MY, Yang YK, Yin YN, journalName=Med J Chin PLA, refType=null, unstructuredReference=Gao MY, Yang YK, Yin YN, et al. Research advances in the pathogenesis of non-alcoholic fatty liver mediated by intestinal flora[J]. Med J Chin PLA, 2020, 45(9): 990-995., articleTitle=Research advances in the pathogenesis of non-alcoholic fatty liver mediated by intestinal flora, refAbstract=null), Reference(id=1207433499905204671, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=45, issue=9, pageStart=990, pageEnd=995, url=null, language=null, rfNumber=[2], rfOrder=2, authorNames=高明月, 杨雅坤, 殷雅楠, journalName=解放军医学杂志, refType=null, unstructuredReference=[高明月, 杨雅坤, 殷雅楠, 等. 肠道菌群介导的非酒精性脂肪性肝病发病机制研究进展[J]. 解放军医学杂志, 2020, 45(9): 990-995.], articleTitle=肠道菌群介导的非酒精性脂肪性肝病发病机制研究进展, refAbstract=null), Reference(id=1207433500031033799, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=36, issue=3, pageStart=569, pageEnd=580, url=null, language=null, rfNumber=[3], rfOrder=3, authorNames=Su TH, Wu CH, Kao JH, journalName=J Gastroenterol Hepatol, refType=null, unstructuredReference=Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology[J]. J Gastroenterol Hepatol, 2021, 36(3): 569-580., articleTitle=Artificial intelligence in precision medicine in hepatology, refAbstract=null), Reference(id=1207433500127502796, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=46, issue=3, pageStart=286, pageEnd=293, url=null, language=null, rfNumber=[4], rfOrder=4, authorNames=Liu ZN, Yang JJ, Chen YD, journalName=Med J Chin PLA, refType=null, unstructuredReference=Liu ZN, Yang JJ, Chen YD. Application and progress of machine learning in coronary computed tomography angiography[J]. Med J Chin PLA, 2021, 46(3): 286-293., articleTitle=Application and progress of machine learning in coronary computed tomography angiography, refAbstract=null), Reference(id=1207433500207194577, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=null, issue=3, pageStart=286, pageEnd=293, url=null, language=null, rfNumber=[4], rfOrder=5, authorNames=刘子暖, 杨俊杰, 陈韵岱, journalName=解放军医学杂志, refType=null, unstructuredReference=[刘子暖, 杨俊杰, 陈韵岱. 机器学习在冠状动脉计算机断层扫描领域的应用及进展[J]. 解放军医学杂志, 2021, 46(3): 286-293.], articleTitle=机器学习在冠状动脉计算机断层扫描领域的应用及进展, refAbstract=null), Reference(id=1207433500282692057, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2016, volume=39, issue=8, pageStart=1697, pageEnd=1716, url=null, language=null, rfNumber=[5], rfOrder=6, authorNames=Jiao LC, Yang SY, Liu F, journalName=Chin J Comput, refType=null, unstructuredReference=Jiao LC, Yang SY, Liu F, et al. Seventy years beyond neural networks: Retrospect and prospect[J]. Chin J Comput, 2016, 39(8): 1697-1716., articleTitle=Seventy years beyond neural networks: Retrospect and prospect, refAbstract=null), Reference(id=1207433500425298403, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2016, volume=39, issue=8, pageStart=1697, pageEnd=1716, url=null, language=null, rfNumber=[5], rfOrder=7, authorNames=焦李成, 杨淑媛, 刘芳, journalName=计算机学报, refType=null, unstructuredReference=[焦李成, 杨淑媛, 刘芳, 等. 神经网络七十年：回顾与展望[J]. 计算机学报, 2016, 39(8): 1697-1716.], articleTitle=神经网络七十年：回顾与展望, refAbstract=null), Reference(id=1207433500521767402, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=46, issue=10, pageStart=1034, pageEnd=1039, url=null, language=null, rfNumber=[6], rfOrder=8, authorNames=Shi QH, Zhang ZF, Hu B, journalName=Med J Chin PLA, refType=null, unstructuredReference=Shi QH, Zhang ZF, Hu B, et al. Recent advances in the use of deep learning and artificial intelligence in the diagnosis and treatment of cervical and lumbar spine degenerative diseases[J]. Med J Chin PLA, 2021, 46(10): 1034-1039., articleTitle=Recent advances in the use of deep learning and artificial intelligence in the diagnosis and treatment of cervical and lumbar spine degenerative diseases, refAbstract=null), Reference(id=1207433500609847792, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=null, issue=10, pageStart=1034, pageEnd=1039, url=null, language=null, rfNumber=[6], rfOrder=9, authorNames=施强慧, 张子凡, 胡博, journalName=解放军医学杂志, refType=null, unstructuredReference=[施强慧, 张子凡, 胡博, 等. 深度学习与人工智能在颈腰椎退变性疾病诊断及治疗中的应用研究进展[J]. 解放军医学杂志, 2021, 46(10):1034-1039.], articleTitle=深度学习与人工智能在颈腰椎退变性疾病诊断及治疗中的应用研究进展, refAbstract=null), Reference(id=1207433501796835829, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2015, volume=521, issue=7553, pageStart=436, pageEnd=444, url=null, language=null, rfNumber=[7], rfOrder=10, authorNames=LeCun Y, Bengio Y, Hinton G, journalName=Nature, refType=null, unstructuredReference=LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444., articleTitle=Deep learning, refAbstract=null), Reference(id=1207433501918470652, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2018, volume=32, issue=null, pageStart=85, pageEnd=null, url=null, language=null, rfNumber=[8], rfOrder=11, authorNames=Langarizadeh M, Orooji A, journalName=Med J Islam Repub Iran, refType=null, unstructuredReference=Langarizadeh M, Orooji A. A novel method for fuzzy diagnostic system design[J]. Med J Islam Repub Iran, 2018, 32: 85., articleTitle=A novel method for fuzzy diagnostic system design, refAbstract=null), Reference(id=1207433502052688388, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=45, issue=5, pageStart=61, pageEnd=null, url=null, language=null, rfNumber=[9], rfOrder=12, authorNames=Peng JF, Zou KQ, Zhou M, journalName=J Med Syst, refType=null, unstructuredReference=Peng JF, Zou KQ, Zhou M, et al. An explainable artificial intelligence framework for the deterioration risk prediction of hepatitis patients[J]. J Med Syst, 2021, 45(5): 61., articleTitle=An explainable artificial intelligence framework for the deterioration risk prediction of hepatitis patients, refAbstract=null), Reference(id=1207433502153351688, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=1529, issue=null, pageStart=032002, pageEnd=null, url=null, language=null, rfNumber=[10], rfOrder=13, authorNames=Razali N, Mustapha A, Wahab MHA, journalName=J Phys Conf Ser, refType=null, unstructuredReference=Razali N, Mustapha A, Wahab MHA, et al. A data mining approach to prediction of liver diseases[J]. J Phys Conf Ser, 2020, 1529: 032002., articleTitle=A data mining approach to prediction of liver diseases, refAbstract=null), Reference(id=1207433502233043467, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2018, volume=2018, issue=null, pageStart=430, pageEnd=439, url=null, language=null, rfNumber=[11], rfOrder=14, authorNames=Fialoke S, Malarstig A, Miller MR, journalName=AMIA Annu Symp Proc, refType=null, unstructuredReference=Fialoke S, Malarstig A, Miller MR, et al. Application of machine learning methods to predict non-alcoholic steatohepatitis(NASH) in non-alcoholic fatty liver (NAFL) patients[J]. AMIA Annu Symp Proc, 2018, 2018: 430-439., articleTitle=Application of machine learning methods to predict non-alcoholic steatohepatitis(NASH) in non-alcoholic fatty liver (NAFL) patients, refAbstract=null), Reference(id=1207433502417592854, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=25, issue=null, pageStart=91, pageEnd=102, url=null, language=null, rfNumber=[12], rfOrder=15, authorNames=Vandromme M, Jun T, Perumalswami P, journalName=Pac Symp Biocomput, refType=null, unstructuredReference=Vandromme M, Jun T, Perumalswami P, et al. Automated phenotyping of patients with non-alcoholic fatty liver disease reveals clinically relevant disease subtypes[J]. Pac Symp Biocomput, 2020, 25: 91-102., articleTitle=Automated phenotyping of patients with non-alcoholic fatty liver disease reveals clinically relevant disease subtypes, refAbstract=null), Reference(id=1207433502497284637, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=11, issue=1, pageStart=5081, pageEnd=null, url=null, language=null, rfNumber=[13], rfOrder=16, authorNames=Pu XK, Deng DN, Chu CY, journalName=Sci Rep, refType=null, unstructuredReference=Pu XK, Deng DN, Chu CY, et al. High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis[J]. Sci Rep, 2021, 11(1): 5081., articleTitle=High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis, refAbstract=null), Reference(id=1207433502576976417, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=144, issue=null, pageStart=110028, pageEnd=null, url=null, language=null, rfNumber=[14], rfOrder=17, authorNames=Altay EV, Alatas B, journalName=Med Hypotheses, refType=null, unstructuredReference=Altay EV, Alatas B. A novel clinical decision support system for liver fibrosis using evolutionary multi-objective method based numerical association analysis[J]. Med Hypotheses, 2020, 144:110028., articleTitle=A novel clinical decision support system for liver fibrosis using evolutionary multi-objective method based numerical association analysis, refAbstract=null), Reference(id=1207433502656668196, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2018, volume=30, issue=2, pageStart=201, pageEnd=206, url=null, language=null, rfNumber=[15], rfOrder=18, authorNames=Saad Y, Awad A, Alakel W, journalName=Eur J Gastroenterol Hepatol, refType=null, unstructuredReference=Saad Y, Awad A, Alakel W, et al. Data mining of routine laboratory tests can predict liver disease progression in Egyptian diabetic patients with hepatitis C virus (G4) infection: a cohort study of 71 806 patients[J]. Eur J Gastroenterol Hepatol, 2018, 30(2): 201-206., articleTitle=Data mining of routine laboratory tests can predict liver disease progression in Egyptian diabetic patients with hepatitis C virus (G4) infection: a cohort study of 71 806 patients, refAbstract=null), Reference(id=1207433502732165672, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2015, volume=16, issue=1, pageStart=381, pageEnd=385, url=null, language=null, rfNumber=[16], rfOrder=19, authorNames=Omran DAEH, Awad AH, Mabrouk MAER, journalName=Asian Pac J Cancer Prev, refType=null, unstructuredReference=Omran DAEH, Awad AH, Mabrouk MAER, et al. Application of data mining techniques to explore predictors of HCC in Egyptian patients with HCV-related chronic liver disease[J]. Asian Pac J Cancer Prev, 2015, 16(1): 381-385., articleTitle=Application of data mining techniques to explore predictors of HCC in Egyptian patients with HCV-related chronic liver disease, refAbstract=null), Reference(id=1207433502828634667, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=100, issue=48, pageStart=3870, pageEnd=3873, url=null, language=null, rfNumber=[17], rfOrder=20, authorNames=Yang J, Guo F, Lyu T, journalName=Natl Med J China, refType=null, unstructuredReference=Yang J, Guo F, Lyu T, et al. Research of artificial intelligence-based clinical decision support system for primary hepatocellular carcinoma[J]. Natl Med J China, 2020, 100(48): 3870-3873., articleTitle=Research of artificial intelligence-based clinical decision support system for primary hepatocellular carcinoma, refAbstract=null), Reference(id=1207433502912520750, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=null, issue=48, pageStart=3870, pageEnd=3873, url=null, language=null, rfNumber=[17], rfOrder=21, authorNames=杨俭, 郭飞, 吕涛, journalName=中华医学杂志, refType=null, unstructuredReference=[杨俭, 郭飞, 吕涛, 等. 肝癌人工智能临床决策支持系统研究[J]. 中华医学杂志, 2020, 100(48): 3870-3873.], articleTitle=肝癌人工智能临床决策支持系统研究, refAbstract=null), Reference(id=1207433503004795443, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2016, volume=125, issue=null, pageStart=58, pageEnd=65, url=null, language=null, rfNumber=[18], rfOrder=22, authorNames=Rau HH, Hsu CY, Lin Y, journalName=Comput Methods Programs Biomed, refType=null, unstructuredReference=Rau HH, Hsu CY, Lin Y, et al. Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network[J]. Comput Methods Programs Biomed, 2016, 125: 58-65., articleTitle=Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network, refAbstract=null), Reference(id=1207433503113847353, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=8, issue=10, pageStart=e24305, pageEnd=null, url=null, language=null, rfNumber=[19], rfOrder=23, authorNames=Lin YJ, Chen RJ, Tang JH, journalName=JMIR Med Inform, refType=null, unstructuredReference=Lin YJ, Chen RJ, Tang JH, et al. Machine-learning monitoring system for predicting mortality among patients with noncancer end-stage liver disease: retrospective study[J]. JMIR Med Inform, 2020, 8(10): e24305., articleTitle=Machine-learning monitoring system for predicting mortality among patients with noncancer end-stage liver disease: retrospective study, refAbstract=null), Reference(id=1207433503210316349, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=8, issue=7, pageStart=434, pageEnd=null, url=null, language=null, rfNumber=[20], rfOrder=24, authorNames=Schoenberg MB, Bucher JN, Koch D, journalName=Ann Transl Med, refType=null, unstructuredReference=Schoenberg MB, Bucher JN, Koch D, et al. A novel machine learning algorithm to predict disease free survival after resection of hepatocellular carcinoma[J]. Ann Transl Med, 2020, 8(7):434., articleTitle=A novel machine learning algorithm to predict disease free survival after resection of hepatocellular carcinoma, refAbstract=null), Reference(id=1207433503348728382, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2019, volume=17, issue=6, pageStart=775, pageEnd=783, url=null, language=null, rfNumber=[21], rfOrder=25, authorNames=Kazemi A, Kazemi K, Sami A, journalName=Exp Clin Transplant, refType=null, unstructuredReference=Kazemi A, Kazemi K, Sami A, et al. Identifying factors that affect patient survival after orthotopic liver transplant using machine-learning techniques[J]. Exp Clin Transplant, 2019, 17(6): 775-783., articleTitle=Identifying factors that affect patient survival after orthotopic liver transplant using machine-learning techniques, refAbstract=null), Reference(id=1207433503474557511, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=116, issue=2, pageStart=336, pageEnd=346, url=null, language=null, rfNumber=[22], rfOrder=26, authorNames=Hu C, Anjur V, Saboo K, journalName=Am J Gastroenterol, refType=null, unstructuredReference=Hu C, Anjur V, Saboo K, et al. Low predictability of readmissions and death using machine learning in cirrhosis[J]. Am J Gastroenterol, 2021, 116(2): 336-346., articleTitle=Low predictability of readmissions and death using machine learning in cirrhosis, refAbstract=null), Reference(id=1207433503545860681, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2017, volume=62, issue=3, pageStart=615, pageEnd=625, url=null, language=null, rfNumber=[23], rfOrder=27, authorNames=Heidemann L, Law J, Fontana RJ, journalName=Dig Dis Sci, refType=null, unstructuredReference=Heidemann L, Law J, Fontana RJ. A text searching tool to identify patients with idiosyncratic drug-induced liver injury[J]. Dig Dis Sci, 2017, 62(3): 615-625., articleTitle=A text searching tool to identify patients with idiosyncratic drug-induced liver injury, refAbstract=null), Reference(id=1207433503629746765, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2018, volume=359, issue=6381, pageStart=1233, pageEnd=1239, url=null, language=null, rfNumber=[24], rfOrder=28, authorNames=Bastarache L, Hughey JJ, Hebbring S, journalName=Science, refType=null, unstructuredReference=Bastarache L, Hughey JJ, Hebbring S, et al. Phenotype risk scores identify patients with unrecognized Mendelian disease patterns[J]. Science, 2018, 359(6381): 1233-1239., articleTitle=Phenotype risk scores identify patients with unrecognized Mendelian disease patterns, refAbstract=null), Reference(id=1207433503722021456, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=21, issue=1, pageStart=10, pageEnd=null, url=null, language=null, rfNumber=[25], rfOrder=29, authorNames=Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, journalName=BMC Gastroenterol, refType=null, unstructuredReference=Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, et al. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis[J]. BMC Gastroenterol, 2021, 21(1): 10., articleTitle=Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis, refAbstract=null), Reference(id=1207433503839461973, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2016, volume=43, issue=3, pageStart=1428, pageEnd=1436, url=null, language=null, rfNumber=[26], rfOrder=30, authorNames=Gatos I, Tsantis S, Spiliopoulos S, journalName=Med Phys, refType=null, unstructuredReference=Gatos I, Tsantis S, Spiliopoulos S, et al. A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging[J]. Med Phys, 2016, 43(3): 1428-1436., articleTitle=A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging, refAbstract=null), Reference(id=1207433503948513884, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2019, volume=46, issue=5, pageStart=2298, pageEnd=2309, url=null, language=null, rfNumber=[27], rfOrder=31, authorNames=Gatos I, Tsantis S, Spiliopoulos S, journalName=Med Phys, refType=null, unstructuredReference=Gatos I, Tsantis S, Spiliopoulos S, et al. Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment[J]. Med Phys, 2019, 46(5): 2298-2309., articleTitle=Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment, refAbstract=null), Reference(id=1207433504049177184, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2017, volume=89, issue=null, pageStart=18, pageEnd=23, url=null, language=null, rfNumber=[28], rfOrder=32, authorNames=Chen Y, Luo Y, Huang W, journalName=Comput Biol Med, refType=null, unstructuredReference=Chen Y, Luo Y, Huang W, et al. Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B[J]. Comput Biol Med, 2017, 89: 18-23., articleTitle=Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B, refAbstract=null), Reference(id=1207433504133063268, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=21, issue=10, pageStart=200, pageEnd=209, url=null, language=null, rfNumber=[29], rfOrder=33, authorNames=Alirr OI, journalName=J Appl Clin Med Phys, refType=null, unstructuredReference=Alirr OI. Deep learning and level set approach for liver and tumor segmentation from CT scans[J]. J Appl Clin Med Phys, 2020, 21(10): 200-209., articleTitle=Deep learning and level set approach for liver and tumor segmentation from CT scans, refAbstract=null), Reference(id=1207433504229532265, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2018, volume=47, issue=3, pageStart=723, pageEnd=728, url=null, language=null, rfNumber=[30], rfOrder=34, authorNames=Esses SJ, Lu XG, Zhao TJ, journalName=J Magn Reson Imaging, refType=null, unstructuredReference=Esses SJ, Lu XG, Zhao TJ, et al. Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture[J]. J Magn Reson Imaging, 2018, 47(3): 723-728., articleTitle=Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture, refAbstract=null), Reference(id=1207433504351167085, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2018, volume=286, issue=3, pageStart=887, pageEnd=896, url=null, language=null, rfNumber=[31], rfOrder=35, authorNames=Yasaka K, Akai H, Abe O, journalName=Radiology, refType=null, unstructuredReference=Yasaka K, Akai H, Abe O, et al. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study[J]. Radiology, 2018, 286(3): 887-896., articleTitle=Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study, refAbstract=null), Reference(id=1207433504460218995, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2019, volume=100, issue=4, pageStart=227, pageEnd=233, url=null, language=null, rfNumber=[32], rfOrder=36, authorNames=Schmauch B, Herent P, Jehanno P, journalName=Diagn Interv Imaging, refType=null, unstructuredReference=Schmauch B, Herent P, Jehanno P, et al. Diagnosis of focal liver lesions from ultrasound using deep learning[J]. Diagn Interv Imaging, 2019, 100(4): 227-233., articleTitle=Diagnosis of focal liver lesions from ultrasound using deep learning, refAbstract=null), Reference(id=1207433504556687986, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2018, volume=69, issue=3, pageStart=343, pageEnd=354, url=null, language=null, rfNumber=[33], rfOrder=37, authorNames=Guo LH, Wang D, Qian YY, journalName=Clin Hemorheol Microcirc, refType=null, unstructuredReference=Guo LH, Wang D, Qian YY, et al. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images[J]. Clin Hemorheol Microcirc, 2018, 69(3): 343-354., articleTitle=A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images, refAbstract=null), Reference(id=1207433504674128501, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2017, volume=14, issue=12, pageStart=749, pageEnd=762, url=null, language=null, rfNumber=[34], rfOrder=38, authorNames=Lambin P, Leijenaar RTH, Deist TM, journalName=Nat Rev Clin Oncol, refType=null, unstructuredReference=Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762., articleTitle=Radiomics: the bridge between medical imaging and personalized medicine, refAbstract=null), Reference(id=1207433504783180407, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2017, volume=12, issue=11, pageStart=1945, pageEnd=1957, url=null, language=null, rfNumber=[35], rfOrder=39, authorNames=Vivanti R, Szeskin A, Lev-Cohain N, journalName=Int J Comput Assist Radiol Surg, refType=null, unstructuredReference=Vivanti R, Szeskin A, Lev-Cohain N, et al. Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies[J]. Int J Comput Assist Radiol Surg, 2017, 12(11): 1945-1957., articleTitle=Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies, refAbstract=null), Reference(id=1207433504871260797, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2019, volume=1, issue=5, pageStart=e180021, pageEnd=null, url=null, language=null, rfNumber=[36], rfOrder=40, authorNames=Morshid A, Elsayes KM, Khalaf AM, journalName=Radiol Artif Intell, refType=null, unstructuredReference=Morshid A, Elsayes KM, Khalaf AM, et al. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization[J]. Radiol Artif Intell, 2019, 1(5): e180021., articleTitle=A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization, refAbstract=null), Reference(id=1207433504967729791, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=71, issue=6, pageStart=1953, pageEnd=1966, url=null, language=null, rfNumber=[37], rfOrder=41, authorNames=Liu F, Goh GB, Tiniakos D, journalName=Hepatology, refType=null, unstructuredReference=Liu F, Goh GB, Tiniakos D, et al. qFIBS: an automated technique for quantitative evaluation of fibrosis, inflammation, ballooning, and steatosis in patients with nonalcoholic steatohepatitis[J]. Hepatology, 2020, 71(6): 1953-1966., articleTitle=qFIBS: an automated technique for quantitative evaluation of fibrosis, inflammation, ballooning, and steatosis in patients with nonalcoholic steatohepatitis, refAbstract=null), Reference(id=1207433506188272257, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=74, issue=1, pageStart=133, pageEnd=147, url=null, language=null, rfNumber=[38], rfOrder=42, authorNames=Taylor-Weiner A, Pokkalla H, Han L, journalName=Hepatology, refType=null, unstructuredReference=Taylor-Weiner A, Pokkalla H, Han L, et al. A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH[J]. Hepatology, 2021, 74(1):133-147., articleTitle=A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH, refAbstract=null), Reference(id=1207433506318295685, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2019, volume=9, issue=1, pageStart=2975, pageEnd=null, url=null, language=null, rfNumber=[39], rfOrder=43, authorNames=Goh GB, Leow WQ, Liang S, journalName=Sci Rep, refType=null, unstructuredReference=Goh GB, Leow WQ, Liang S, et al. Quantification of hepatic steatosis in chronic liver disease using novel automated method of second harmonic generation and two-photon excited fluorescence[J]. Sci Rep, 2019, 9(1): 2975., articleTitle=Quantification of hepatic steatosis in chronic liver disease using novel automated method of second harmonic generation and two-photon excited fluorescence, refAbstract=null), Reference(id=1207433506414764682, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=100, issue=10, pageStart=1367, pageEnd=1383, url=null, language=null, rfNumber=[40], rfOrder=44, authorNames=Roy M, Wang F, Vo H, journalName=Lab Invest, refType=null, unstructuredReference=Roy M, Wang F, Vo H, et al. Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies[J]. Lab Invest, 2020, 100(10): 1367-1383., articleTitle=Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies, refAbstract=null), Reference(id=1207433506511233676, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2017, volume=84, issue=null, pageStart=156, pageEnd=167, url=null, language=null, rfNumber=[41], rfOrder=45, authorNames=Li SQ, Jiang HY, Pang WB, journalName=Comput Biol Med, refType=null, unstructuredReference=Li SQ, Jiang HY, Pang WB. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading[J]. Comput Biol Med, 2017, 84: 156-167., articleTitle=Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading, refAbstract=null), Reference(id=1207433506620285587, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=4, issue=null, pageStart=14, pageEnd=null, url=null, language=null, rfNumber=[42], rfOrder=46, authorNames=Chen M, Zhang B, Topatana W, journalName=NPJ Precis Oncol, refType=null, unstructuredReference=Chen M, Zhang B, Topatana W, et al. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning[J]. NPJ Precis Oncol, 2020, 4: 14., articleTitle=Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning, refAbstract=null), Reference(id=1207433506720948887, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2020, volume=1, issue=8, pageStart=800, pageEnd=810, url=null, language=null, rfNumber=[43], rfOrder=47, authorNames=Fu Y, Jung AW, Torne RV, journalName=Nat Cancer, refType=null, unstructuredReference=Fu Y, Jung AW, Torne RV, et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis[J]. Nat Cancer, 2020, 1(8): 800-810., articleTitle=Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis, refAbstract=null), Reference(id=1207433506809029276, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, doi=null, pmid=null, pmcid=null, year=2021, volume=27, issue=16, pageStart=1664, pageEnd=1690, url=null, language=null, rfNumber=[44], rfOrder=48, authorNames=Cao JS, Lu ZY, Chen MY, journalName=World J Gastroenterol, refType=null, unstructuredReference=Cao JS, Lu ZY, Chen MY, et al. Artificial intelligence in gastroenterology and hepatology: Status and challenges[J]. World J Gastroenterol, 2021, 27(16): 1664-1690., articleTitle=Artificial intelligence in gastroenterology and hepatology: Status and challenges, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1207433496314880251, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, xref=1, ext=[AuthorCompanyExt(id=1207433496319074556, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China), AuthorCompanyExt(id=1207433496327463165, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433496314880251, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1陆军军医大学第一附属医院感染科，重庆 400038)]), AuthorCompany(id=1207433497380233478, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, xref=2, ext=[AuthorCompanyExt(id=1207433497397010697, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433497380233478, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2School of Automation, Chongqing University, Chongqing 400044, China), AuthorCompanyExt(id=1207433497409593609, tenantId=1146029695717560320, journalId=1189873630562394117, articleId=1207433495325024476, companyId=1207433497380233478, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2重庆大学自动化学院，重庆 400044)])], figs=null, attaches=null, journal=Journal(id=1146441329971666965, delFlag=0, nameCn=解放军医学杂志, nameEn=Medical Journal of Chinese People’s Liberation Army, nameHistory1=null, nameHistory2=null, issn=0577-7402, eissn=null, cn=11-1056/R, coden=null, periodic=0, language=CN, oaType=是, ccby=CC BY-NC-ND, superviseOffice=null, ownerOffice=null, pubOffice=null, editorOffice=null, officeType=null, aims=null, clcCode=null, officeProv=null, officeCity=null, officeAddr=null, officeZip=null, officeEmail=null, officePhone=null, editDirector=null, officeDirector=null, officeDirectorPhone=null, officeStaffNum=null, officeEmpNum=null, coverPicUrl=6srot5PcoYX30Oa4xeTmeg==, journalPrice=null, startedYear=null, abbrevIsoEn=null, journalRemark=null, publicationField=null, createdTime=1751262512917, updatedTime=1761735725513, createdBy=18614031015, updatedBy=13701087609, firstLetterCn=M, firstLetterEn=M, subjectCode=Life Sciences, subjectName=Life Sciences, subjectCodeEn=Life Sciences, subjectNameEn=null, picCn=6srot5PcoYX30Oa4xeTmeg==, picEn=ELwBh5xqrSTlIs7HmSNt2Q==, jcr=null, cjcr=null, exts=[JournalExt(id=1190369167564968109, language=CN, name=解放军医学杂志, nameHistory1=null, nameHistory2=null, managedBy=, sponsoredBy=, publishedBy=, editorOffice=, officeProv=null, officeCity=null, officeAddr=, officeZip=, editDirector=, officeDirector=null, officePhone=null, coverPicUrl=null, journalRemark=, submitArticleUrl=null, websiteUrl=, createdTime=1761735725537, updatedTime=1761735725537, createdBy=13701087609, updatedBy=13701087609, submissionGuidelinesUrl=, submissionAuthorUrl=#, submissionEditorUrl=#, submissionReviewUrl=#, submissionCeEditorUrl=, submissionAeEditorUrl=, option={"copyright":""}), JournalExt(id=1190369167615299758, language=EN, name=Medical Journal of Chinese People’s Liberation Army, nameHistory1=null, nameHistory2=null, managedBy=, sponsoredBy=, publishedBy=, editorOffice=, officeProv=null, officeCity=null, officeAddr=, officeZip=, editDirector=, officeDirector=null, officePhone=null, coverPicUrl=null, journalRemark=, submitArticleUrl=null, websiteUrl=, createdTime=1761735725549, updatedTime=1761735725549, createdBy=13701087609, updatedBy=13701087609, submissionGuidelinesUrl=, submissionAuthorUrl=#, submissionEditorUrl=#, submissionReviewUrl=#, submissionCeEditorUrl=, submissionAeEditorUrl=, option={"copyright":""})], databaseList=null, tenantJournalId=1189873630562394117, websiteList=[Website(id=1189873845923287108, webName=null, webTitle=null, webDomain=null, webCopyrigh=null, webIpcNo=null, seoTitle=null, seoKeywords=null, seoDescription=null, tenantJournalId=null, journalId=1189873630562394117, journalNameCn=null, journalNameEn=null, grayFlag=null, tenantId=1146029695717560320, platformId=null, journalGroupId=null, journalGroupNameCn=null, journalGroupNameEn=null, type=1, domain=https://castjournals.cast.org.cn/joweb/jfjyxzz/CN, language=CN, createTime=1761617631655, createBy=18614031015, updateTime=1761622010471, updateBy=18614031015, name=解放军医学杂志-中文, tplId=1146099689490845704, title=解放军医学杂志, delFlag=0, indexPage=/home, props=[WebsiteProps(id=1189924939378520839, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=articleTextType, value=kx, createTime=1761629813284, updateTime=1761629813284, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924939353355012, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=banner, value=null, createTime=1761629813278, updateTime=1761629813278, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924939399492362, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=grayFlag, value=0, createTime=1761629813289, updateTime=1761629813289, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924939344966403, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=logo, value=https://castjournals.cast.org.cn/joweb/jfjyxzz/CN/file/pic?fileId=+zXjYVhun8ZOAA6+aKx2hw==, createTime=1761629813276, updateTime=1761629813276, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924939412075276, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=minRunFlag, value=0, createTime=1761629813292, updateTime=1761629813292, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924939374326534, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=picServerUrl, value=https://castjournals.cast.org.cn/joweb/jfjyxzz/CN/file/pic, createTime=1761629813283, updateTime=1761629813283, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924939407880971, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=silenceFlag, value=0, createTime=1761629813291, updateTime=1761629813291, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924939361743621, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=staticResourcePath, value=https://castjournals.cast.org.cn/joweb/cast_kjdb_cn_619/, createTime=1761629813280, updateTime=1761629813280, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924939386909448, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=themeColor, value=null, createTime=1761629813286, updateTime=1761629813286, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924939395298057, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873845923287108, code=themeStyle, value=null, createTime=1761629813288, updateTime=1761629813288, creator=18614031015, updator=18614031015)]), Website(id=1189873846057504839, webName=null, webTitle=null, webDomain=null, webCopyrigh=null, webIpcNo=null, seoTitle=null, seoKeywords=null, seoDescription=null, tenantJournalId=null, journalId=1189873630562394117, journalNameCn=null, journalNameEn=null, grayFlag=null, tenantId=1146029695717560320, platformId=null, journalGroupId=null, journalGroupNameCn=null, journalGroupNameEn=null, type=1, domain=https://castjournals.cast.org.cn/joweb/jfjyxzz/EN, language=EN, createTime=1761617631687, createBy=18614031015, updateTime=1761622030030, updateBy=18614031015, name=解放军医学杂志-英文, tplId=1146101810881728533, title=Medical Journal of Chinese People’s Liberation Army, delFlag=0, indexPage=/home, props=[WebsiteProps(id=1189924968168223505, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=articleTextType, value=kx, createTime=1761629820148, updateTime=1761629820148, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924968147251982, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=banner, value=null, createTime=1761629820143, updateTime=1761629820143, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924968185000724, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=grayFlag, value=0, createTime=1761629820152, updateTime=1761629820152, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924968138863373, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=logo, value=https://castjournals.cast.org.cn/joweb/jfjyxzz/EN/file/pic?fileId=+zXjYVhun8ZOAA6+aKx2hw==, createTime=1761629820141, updateTime=1761629820141, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924968197583638, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=minRunFlag, value=0, createTime=1761629820155, updateTime=1761629820155, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924968159834896, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=picServerUrl, value=https://castjournals.cast.org.cn/joweb/jfjyxzz/EN/file/pic, createTime=1761629820146, updateTime=1761629820146, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924968193389333, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=silenceFlag, value=0, createTime=1761629820154, updateTime=1761629820154, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924968155640591, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=staticResourcePath, value=https://castjournals.cast.org.cn/joweb/cast_kjdb_en_623/, createTime=1761629820145, updateTime=1761629820145, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924968172417810, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=themeColor, value=null, createTime=1761629820149, updateTime=1761629820149, creator=18614031015, updator=18614031015), WebsiteProps(id=1189924968180806419, tenantId=1146029695717560320, journalId=null, journalGroupId=null, siteId=1189873846057504839, code=themeStyle, value=null, createTime=1761629820151, updateTime=1761629820151, creator=18614031015, updator=18614031015)])], journalTitle=解放军医学杂志, weixinUrl=null, journalUrl=http://zh.jfjyxzz.org.cn/, iacademicId=null, status=1, seqNo=null, journalTitleEn=Medical Journal of Chinese People’s Liberation Army, journalPhotoCn=6srot5PcoYX30Oa4xeTmeg==, journalPhotoEn=ELwBh5xqrSTlIs7HmSNt2Q==, journalFirstLetter=M, journalRecommend=null, journalNew=null, journalCollection=null, jcrJf=null, cjcrJf=null, jcrJfStr=null, cjcrJfStr=null, submissionFirstDecision=null, sciSubjectClassification=null, casSubjectClassification=null, citeScore=null, totalCitationFrequency=null, icpCode=null, psCode=null, advertisingLicenseCode=null, copyrightInformation=null, country=null, option=, provinceCode=null, provinceName=null, collectFlag=false), detailUrlCn=https://castjournals.cast.org.cn/joweb/jfjyxzz/CN/10.11855/j.issn.0577-7402.2022.08.0845, detailUrlEn=https://castjournals.cast.org.cn/joweb/jfjyxzz/EN/10.11855/j.issn.0577-7402.2022.08.0845, pdfUrlCn=https://castjournals.cast.org.cn/joweb/jfjyxzz/CN/PDF/10.11855/j.issn.0577-7402.2022.08.0845, pdfUrlEn=https://castjournals.cast.org.cn/joweb/jfjyxzz/EN/PDF/10.11855/j.issn.0577-7402.2022.08.0845, aliStartDate=null, aliEndDate=null, collectionFlag=false, citedCount=null, citedUrl=null, reference=null)
收藏切换
人工智能在慢性肝病领域中的应用研究进展
收藏切换
PDF下载
汤影子 1 , 夏杰 1 , 刘慧敏 1 , 吕化杰 1 , 郭茂耘 2, *
解放军医学杂志 | 综述 2022,47(8): 845-850
收起
收藏切换
解放军医学杂志 | 综述 2022, 47(8): 845-850
人工智能在慢性肝病领域中的应用研究进展
全屏
汤影子1, 夏杰1, 刘慧敏1, 吕化杰1, 郭茂耘2, *
作者信息
  • 1陆军军医大学第一附属医院感染科，重庆 400038
  • 2重庆大学自动化学院，重庆 400044

    • 汤影子，医学硕士，主治医师，主要从事肝病方面的临床研究

通讯作者:

郭茂耘，E-mail：
Progress in application research of artificial intelligence in the field of chronic liver diseases
Ying-Zi Tang1, Jie Xia1, Hui-Min Liu1, Hua-Jie Lv1, Mao-Yun Guo2, *
Affiliations
  • 1Department of Infectious Diseases, the First Affiliated Hospital, Army Medical University, Chongqing 400038, China
  • 2School of Automation, Chongqing University, Chongqing 400044, China
出版时间: 2022-08-28 doi: 10.11855/j.issn.0577-7402.2022.08.0845
文章导航
收藏切换
摘要
收起

近十年来，人工智能已经在医学领域取得了突破性进展。与传统统计模型相比，人工智能的优势在于可通过机器学习建立算法及预测模型来高效、有效地识别目标数据集中的模式，并结合多种因素创建更为精确的预测模型，特别适用于具有海量高维数据特征的医学临床数据分析及预测建模任务。在肝脏病学的临床实践领域，已有越来越多的研究将人工智能应用于肝病的诊断分类、协助治疗、预测疗效及预后，以及协助肝脏疾病的影像学、病理学诊断等。本文结合国内外研究成果，总结人工智能在慢性肝病领域中的应用情况及研究进展。

人工智能  /  机器学习  /  慢性肝病

Artificial intelligence has made breakthroughs in medicine in the past decade. Compared with the traditional statistical model, the advantage of artificial intelligence is that it can establish algorithm and prediction model through machine learning to efficiently and effectively identify the patterns in large data sets, and combine a variety of factors to create more accurate prediction model. Therefore, artificial intelligence is particularly suitable for huge and complex or high-dimensional clinical data analysis and predictive modeling tasks. There are many kinds of data formats in the clinical practice of hepatology. Many studies have applied artificial intelligence in the diagnosis and classification of liver diseases, assisting treatment, predicting efficacy and prognosis, and evaluation of liver imaging and pathology. Based on the study outcomes in related fields at home and aboard, this paper summarizes the research progress and application of artificial intelligence in the field of diagnosis and treatment of chronic liver diseases.

artificial intelligence  /  machine learning  /  chronic liver diseases
汤影子, 夏杰, 刘慧敏, 吕化杰, 郭茂耘. 人工智能在慢性肝病领域中的应用研究进展. 解放军医学杂志, 2022 , 47 (8) : 845 -850 . DOI: 10.11855/j.issn.0577-7402.2022.08.0845
Ying-Zi Tang, Jie Xia, Hui-Min Liu, Hua-Jie Lv, Mao-Yun Guo. Progress in application research of artificial intelligence in the field of chronic liver diseases[J]. Medical Journal of Chinese People’s Liberation Army, 2022 , 47 (8) : 845 -850 . DOI: 10.11855/j.issn.0577-7402.2022.08.0845
正文
收起
慢性肝病及肝硬化在全球的死亡原因中居第11位，每年造成110万人死亡，其常见的原因包括乙型肝炎病毒(hepatitis B virus，HBV)感染、丙型肝炎病毒(hepatitis C virus，HCV)感染、酒精相关肝病及非酒精性脂肪性肝病(non-alcoholic fatty liver disease，NAFLD)等[1-2]。随着精准医疗的发展，以及医疗数据和数字图像数量不断增加，需要新的数据处理分析工具来帮助进行疾病的诊断、监测及疗效预测。近年来，人工智能(artificial intelligence，AI)在医疗领域取得了突破性进展，可通过模型及算法发现隐藏在数据中的循证医学逻辑，从而为患者提供个性化的诊疗决策。与传统统计模型相比，AI的优势在于可识别独特的模式并结合多种因素创建更精确的预测模型、风险分层及结果，特别适用于具有海量高维数据特征的临床数据分析及预测建模任务。在肝脏病学的临床实践中会产生多种数据格式，如电子病历、放射成像及肝脏病理资料等。AI在异质性、复杂性及重叠混杂因素数据处理方面具有明显优势，特别适用于慢性肝病领域的数据处理及分析[3]。本文对AI在慢性肝病领域的诊断、病变评估、协助治疗、疗效预测和预后，以及放射组学、病理学等方面的应用情况及研究进展综述如下。
1 AI的基本算法
收起
数据、应用任务及算法是AI的3个关键组成部分。机器学习(machine learning，ML)是实现AI的核心技术，通过ML可以建立算法及预测模型高效、有效地识别大数据集中的模式，通过算法对样本数据特征进行学习，构建决策预测模型，实现对新数据的判决及预测[4]。
1.1 人工神经网络
人工神经网络是由大量神经元单元互联组成的非线性、自适应信息处理系统，具有如下特点：(1)人工神经元在数学上表现为一种非线性关系，可以提高容错性及存储容量；(2)通过神经元之间的大量连接模拟大脑的判断、预测及认知功能；(3)自学习、自组织、自适应性；(4)具有多个较稳定的平衡态，系统演化具备多样性。以上特点使人工神经网络成为当前类脑智能研究中的有效工具[5]。
1.2 深度学习
深度学习的概念源于人工神经网络的研究，通过组合低层特征形成更加抽象的高层属性描述，以发现蕴涵在数据中的深层本质特征。深度学习的优势在于用更多的数据或是更好的算法来提高学习算法的结果，与传统神经网络相比，深度学习能够处理数据量更大、更复杂的问题[6-7]。
1.3 其他算法
支持向量机属于浅层模型，在解决小样本、非线性问题上具有优势。决策树能够直接体现数据的特点，易于理解及实现。随机森林利用多棵树对样本进行训练并预测，对于高维特征的数据集分类有很高的效率，准确率较高，容易实现。k-近邻是一种无建模过程的非线性分类器，也可用于回归，在处理样本量较大及维度较高的数据时有优势。
2 AI在电子病历系统中的应用
收起
2.1 AI在肝病诊断分类中的应用
利用电子病历系统及大数据集，AI可以基于患者个体及人群的风险因素发现肝病的表型特征。利用模糊c均值聚类等方法创建的肝病诊断分类器分类精度高，敏感度、特异度及准确率均超过90%，用户友好，对数据的解释也容易理解，并可对肝病辅助诊断进行全局及局部解释[8-9]。Razali等[10]通过分析416例肝病患者及167例非肝病患者的临床数据，比较了贝叶斯点击及神经网络等数据挖掘算法在肝脏疾病预测方面的性能及精度，并指出为提高肝脏疾病预测结果的准确性，可将混合方法应用于今后的工作中。
2.2 AI在NAFLD中的应用
NAFLD现已取代慢性乙型肝炎成为我国第一大慢性肝病。利用AI技术细化疾病分型及判断预后可以优化临床实践，提高临床诊治效率。美国的Optum®研究纳入了超过8000万例患者的记录，针对非酒精性脂肪性肝炎(non-alcoholic steatohepatitis，NASH)及健康(非NASH)人群创建ML分类器，并用其预测NAFLD患者队列中的NASH。结果显示，所有ML模型(逻辑回归、决策树、随机森林及XGBoost)在识别NASH方面均表现良好，与现有非侵入性检测相比，敏感度提高[受试者工作特征曲线下面积(AUC)为0.83~0.88]，且可使用纵向临床数据来识别个体的NASH风险[11]。NASHMap©ML模型纳入了两个真实数据集：美国国家糖尿病、消化及肾脏疾病研究所注册的一个子集(共704例经组织学证实为NASH及非NASH患者)及Optum®验证模型，包含14个临床及实验室参数，并对每个参数进行单独评估，以预测其在NASH诊断方面的强度，结果显示，NASHMap©模型能识别出879 269例未被Optum®模型诊断的NASH患者[11]。在识别NASH高危人群的基础上，有研究进一步开发了一种ML算法来识别NAFLD亚型，该研究纳入13 290例患者，通过评估临床、影像学及组织学等多项指标，使用无监督聚类算法将患者进一步划分为5种亚型，结果显示，占比较大的两个大组(占全体患者的87%)主要为拉美裔及非洲裔美国人群，这两组患者并发症较少，肝纤维化程度更低，疾病进展较慢，而占比较小的3组则表现为更严重的并发症以及更差的预后[12]。
2.3 AI在肝纤维化及肝细胞癌中的应用
肝活检是诊断肝纤维化的金标准，但由于其具有麻醉并发症、有创性、出血风险及取样错误等缺点，适用性受到限制。超声弹性成像诊断肝纤维化的准确性良好，但其对硬度的测量受多种因素的影响，如肝静脉充血、胆汁淤积、炎症、饮食、肥胖、腹水及观察者经验等，并最终导致弹性成像结果出现误差。因此迫切需要一种准确可靠的无创技术来诊断肝纤维化。Pu等[13]提出了一种优化后的朴素贝叶斯模型，该模型评估了1023例HBV患者的55个常规实验室及临床参数，其预测肝纤维化的准确性可与肝活检媲美(AUC为0.982)。Altay等[14]将基于关联分析的进化智能MOPNAR用于挖掘肝纤维化的规则，该算法可自行修改及调整，自动发现数值的关联规则，且不需要修改或更改数据，在敏感性、平均置信度、覆盖记录数等方面均优于比较方法。一项回顾性多中心研究收集了埃及71 806例HCV感染患者的实验室及组织病理学数据，构建了决策树算法来评估糖尿病合并HCV患者肝纤维化进展的预测因素，结果显示在16个预测因素中，甲胎蛋白是最具决定性的因素，其临界值为5.25 ng/ml，其次为年龄及血小板计数[15]。
Omran等[16]基于315例HCV相关慢性肝病患者的临床数据构建了一种预测肝细胞癌的决策树学习模型，通过数据挖掘发现隐藏的模式，以利用常规数据替代计算机断层摄影(computed tomography，CT)及肝活检。杨俭等[17]基于肝癌患者的真实数据建立了肝癌AI临床决策支持系统，采用多分类器融合模型计算治疗方案推荐系数，并分析受试者工作特征曲线，采用DeepSurv算法实现对生存风险及复发风险的预测，并进一步对比低、中、高风险组的Kaplan-Meier生存曲线，结果显示各风险组间差异显著，提示该系统能较准确地进行肝癌治疗方案推荐及预后预测。Rau等[18]在2200万例电子病历数据的基础上，建立了可预测2型糖尿病确诊后6年内肝癌发展的模型，并通过验证证实该模型对2型糖尿病合并肝癌患者的诊断正确率达75.7%，对不合并肝癌患者的诊断正确率达75.5%。
2.4 终末期肝病患者的病情管理及生存期预测
终末期肝病指各种原因所致肝病的晚期阶段，其病死率高，预后差，为人类健康带来了沉重的负担。应用AI技术可以帮助医务人员对终末期肝病患者进行病情评估、随访、生存期预测，以及筛选更合适的肝移植候选者。Lin等[19]开发的ML监测系统涉及多层面分析，包括评估及诊断的各个方面，该系统的可视化界面提供了更易于理解的综合评估患者病情的方法，且有助于对急性死亡患者及姑息治疗患者进行分类，可帮助医务人员管理终末期肝病患者。Schoenberg等[20]采用随机森林算法对181例肝癌患者的26个临床参数进行处理，在此基础上根据风险剖面特征对测试数据进行分层，该模型对肝癌切除术后的无病生存期预测值为0.788，可用于筛选出哪些患者适合施行肝癌切除术而非肝移植。Kazemi等[21]采用多种ML算法(包括支持向量机、贝叶斯网络、决策树、多层感知器神经网络等)对902例肝移植患者的临床数据进行分析，以提取6个月生存期的有效特征，此模型的AUC及敏感度分别为0.90及0.81，识别出的影响因素的顺序接近临床试验。Hu等[22]采用北美多中心终末期肝病研究联盟队列，对肝硬化住院患者出院后90 d的再入院及死亡情况进行随访，评估多种AI技术预测其预后的能力，发现AI模型很难预测肝硬化患者30 d及90 d的再入院及死亡情况，其准确性等同于仅使用血清钠与终末期肝病模型联合评分生成的模型，提示尚需要更多的生物标志物来提高模型的预测能力。
2.5 AI在疑难肝脏疾病中的应用
药物性肝损伤是一种少见但重要的肝病，如何在电子病历系统中诊断及识别药物性肝损伤是一大难点。Heidemann等[23]提取并整理了一种从电子病历中识别约14个肝损伤术语及200个字符文本的搜索算法，通过计算机提取口述文本，然后对文本片段进行人工审查，可以快速识别出药物性肝损伤案例。
利用AI技术将临床数据与下一代基因测序分析整合有助于疑难肝病的诊断。有研究将在线人类孟德尔遗传数据库(online mendelian inheritance in man，OMIM)中单基因疾病的临床特征映射到国际疾病分类(international classification of diseases，ICD)编码库ICD-10，通过在患者电子病历中调用ICD-10，寻找OMIM中可能的疾病；同时将肝病相关致病基因突变进行标记，通过基因测序的结果筛查可能致病的突变，再返回OMIM寻找最佳的临床表型-基因型的配对，以此来发现罕见变异与孟德尔病表型之间的关联[24]。
3 AI在肝脏疾病影像诊断中的应用
收起
影像组学与AI的融合可实现对疾病病理发展情况的整体性分析，展现细微的病理变化，在肝纤维化严重程度评估、肝脂肪变定性定量诊断、肝脏局灶性病变的鉴别及分类等方面具有明显优势。
3.1 疾病诊断及肝纤维化程度的评估
一项纳入19项研究的Meta分析探讨了AI辅助超声、弹性成像、CT、磁共振成像(magnetic resonance imaging，MRI)及临床参数在肝纤维化及脂肪变性诊断中的敏感度及特异度，结果表明，AI辅助诊断肝纤维化的综合敏感度、特异度分别为0.78、0.89，诊断肝脂肪变性的敏感度、特异度分别为0.97、0.91[25]。Gatos等[26]将基于硬度评估与支持向量机分类算法的计算机辅助诊断系统应用于超声剪切波弹性成像，该模型对85张超声图像(54张健康图像、31张慢性肝病图像)进行了量化，结果显示其准确率为87.0%，敏感度为83.3%，特异度为89.1%。有研究进一步采用优化后的神经网络算法识别并区分超声检查下不同的硬度值区域，结果发现该方法可将慢性肝病纤维化分期诊断准确率提高到95.5%[27]。Chen等[28]采用了4种经典分类器(支持向量机、朴素贝叶斯算法、随机森林、k-近邻)建立了一个决策支持系统，结果显示上述分类器明显优于以往的肝纤维化指数方法，其中随机森林分类器的平均准确率最高。
3.2 肝脏局灶性病变的鉴别及分类
肝脏局灶性病变的影像鉴别诊断和分类是临床的重点及难点，在超声、超声造影、CT及MRI图像中采用ML技术鉴别病变性质已取得明显进展[29-30]。Yasaka等[31]对460例患者的增强CT图像进行了回顾性研究，利用有肝脏肿块的3个CT时相(平扫、动脉期、延迟期)共55 536个影像图像进行了卷积神经网络模型监督训练，随后用100个肝脏肿块影像进行测试，结果显示其对肝脏肿块的鉴别诊断准确率达到84%。Schmauch等[32]采用367张超声图像及放射学报告通过监督训练构建了深度学习模型，其检测肝脏局灶性病变的AUC为0.935，判定病灶性质(良性/恶性)的AUC为0.916，但该模型仍需在大型独立队列中进一步验证。Guo等[33]提出了一种两阶段多视图学习框架，用于超声造影的肝肿瘤计算机辅助诊断：在第一阶段，分别对动脉期与门静脉期、动脉期与延迟期、门静脉期与延迟期3对图像进行深度典型相关分析，共生成6个视图特征；在第二阶段，将这些多视图特征输入到基于多核学习的分类器中。结果显示该框架的分类精度、敏感度、特异度分别为0.904、0.935、0.869。
3.3 监测肝癌的复发及疗效
肝癌术后早期复发是肝病治疗中的重点及难点，及时识别并对患者进行相应干预具有重要意义。AI辅助技术可以从放射图像中提取客观的量化数据，并揭示其与潜在生物过程的关联，在监测肿瘤复发方面发挥重要作用[34]。Vivanti等[35]收集并整合了病程中肿瘤的初始表现、CT影像及肿瘤负荷量，在此基础上设计出的肿瘤复发自动检测模型准确率达到86%。Morshid等[36]使用随机森林分类器预测了105例肝癌患者对经动脉化疗栓塞术的治疗反应，结果表明，结合了CT定量图像特征的模型预测准确率高于单独使用巴塞罗那临床肝癌分期模型(74.2% vs. 62.9%)。
4 AI在肝脏组织病理学中的应用
收起
AI辅助病理已应用于肝纤维化、脂肪肝、肝细胞癌等患者。在NASH患者中，AI辅助病理工具(qFIBS)可用于识别及量化病理改变，包括脂肪变性、小叶炎症、气球样变及纤维化[37]。PathAI是一种基于ML的肝组织学评估方法，该方法使用3个随机对照试验样本来构建并验证深度卷积神经网络，以评估NASH的关键组织学特征，可准确地描述疾病的严重程度及异质性，并敏感地量化NASH的治疗反应，其预测结果与病理学专家的判断高度一致，并能够检测到手工病理分期未检测到的抗纤维化治疗效果，且与组织学进展一致[38]。Histindex是一种基于AI的二次谐波新技术，采用多光子成像技术，可用于定量评价肝脂肪变性[39]。Roy等[40]提出了一种基于深度学习的区域-边界集成网络，用于精确量化整个肝脏组织切片病理图像的脂肪变性，采用此方法进行脂肪变性测量，在像素水平及脂肪变性水平上与病理学家注释、影像学指标及临床数据均有很强的相关性。
在肝癌患者中，利用极限学习机联合多重全连接卷积神经网络进行肝细胞癌核分级，在不同分化阶段的肿瘤细胞分类方面表现出色[41]。Chen等[42]研究了卷积神经网络Inception V3用于肝癌切除后全视野数字切片的自动分类及基因突变预测，结果显示，其预测肿瘤恶性程度(良性/恶性)的准确率为96.0%，预测分化程度的准确率为89.6%。有研究利用深度迁移学习对28种癌症类型的17 355张苏木精-伊红染色的组织病理学切片图像进行组织病理学模式量化，并将其与匹配的基因组、转录组及生存数据进行关联，这种方法能准确地区分肿瘤类型，并能在空间上分辨肿瘤组织与正常组织[43]。这些发现显示了计算机视觉在表征肿瘤组织病理学分子基础方面的巨大潜力。
5 总结与展望
收起
AI技术推广到临床实践尚面临以下主要问题：(1)缺乏用于模型开发及验证的高质量训练及验证数据集，尚需要建立高质量且可及的慢性肝病患者的研究队列；(2)大多数针对肝病开发的模型及算法尚缺乏在临床实践中的长期评估以及与传统诊断方法的直接比较，其在真实世界中的表现如何有待证实；(3)算法开发者与临床工作者之间的技术壁垒有待突破，AI输出结论的可解释性及透明度尚有待提高；(4)在医学伦理方面，如果AI应用过程中出现错误，其后果应由谁来承担，以及该如何保证患者的最大获益。这些问题尚需要进一步解决[44]。
由于AI技术自身不断进步以及生物医学问题本身固有的复杂性，AI在慢性肝病领域中发挥着越来越重要的作用，可以辅助肝病的诊断分类，预测慢性肝病患者纤维化风险，客观评估肝脏成像，以及进一步完善肝脏组织学评估等。未来AI技术将被用于开发更精确的模型，以预测及监测肝病进展及潜在的并发症。此外，AI可应用于药物研发、处理芯片数据及检测肿瘤微环境等，帮助肝脏肿瘤患者实现更加精准、个体化的治疗。
文献
收起
参考文献 引证文献
排序方式：
[1]
Asrani SK, Devarbhavi H, Eaton J, et al. Burden of liver diseases in the world[J]. J Hepatol, 2019, 70(1): 151-171.
[2]
Gao MY, Yang YK, Yin YN, et al. Research advances in the pathogenesis of non-alcoholic fatty liver mediated by intestinal flora[J]. Med J Chin PLA, 2020, 45(9): 990-995.
[高明月, 杨雅坤, 殷雅楠, 等. 肠道菌群介导的非酒精性脂肪性肝病发病机制研究进展[J]. 解放军医学杂志, 2020, 45(9): 990-995.]
[3]
Su TH, Wu CH, Kao JH. Artificial intelligence in precision medicine in hepatology[J]. J Gastroenterol Hepatol, 2021, 36(3): 569-580.
[4]
Liu ZN, Yang JJ, Chen YD. Application and progress of machine learning in coronary computed tomography angiography[J]. Med J Chin PLA, 2021, 46(3): 286-293.
[刘子暖, 杨俊杰, 陈韵岱. 机器学习在冠状动脉计算机断层扫描领域的应用及进展[J]. 解放军医学杂志, 2021, 46(3): 286-293.]
[5]
Jiao LC, Yang SY, Liu F, et al. Seventy years beyond neural networks: Retrospect and prospect[J]. Chin J Comput, 2016, 39(8): 1697-1716.
[焦李成, 杨淑媛, 刘芳, 等. 神经网络七十年：回顾与展望[J]. 计算机学报, 2016, 39(8): 1697-1716.]
[6]
Shi QH, Zhang ZF, Hu B, et al. Recent advances in the use of deep learning and artificial intelligence in the diagnosis and treatment of cervical and lumbar spine degenerative diseases[J]. Med J Chin PLA, 2021, 46(10): 1034-1039.
[施强慧, 张子凡, 胡博, 等. 深度学习与人工智能在颈腰椎退变性疾病诊断及治疗中的应用研究进展[J]. 解放军医学杂志, 2021, 46(10):1034-1039.]
[7]
LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[8]
Langarizadeh M, Orooji A. A novel method for fuzzy diagnostic system design[J]. Med J Islam Repub Iran, 2018, 32: 85.
[9]
Peng JF, Zou KQ, Zhou M, et al. An explainable artificial intelligence framework for the deterioration risk prediction of hepatitis patients[J]. J Med Syst, 2021, 45(5): 61.
[10]
Razali N, Mustapha A, Wahab MHA, et al. A data mining approach to prediction of liver diseases[J]. J Phys Conf Ser, 2020, 1529: 032002.
[11]
Fialoke S, Malarstig A, Miller MR, et al. Application of machine learning methods to predict non-alcoholic steatohepatitis(NASH) in non-alcoholic fatty liver (NAFL) patients[J]. AMIA Annu Symp Proc, 2018, 2018: 430-439.
[12]
Vandromme M, Jun T, Perumalswami P, et al. Automated phenotyping of patients with non-alcoholic fatty liver disease reveals clinically relevant disease subtypes[J]. Pac Symp Biocomput, 2020, 25: 91-102.
[13]
Pu XK, Deng DN, Chu CY, et al. High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis[J]. Sci Rep, 2021, 11(1): 5081.
[14]
Altay EV, Alatas B. A novel clinical decision support system for liver fibrosis using evolutionary multi-objective method based numerical association analysis[J]. Med Hypotheses, 2020, 144:110028.
[15]
Saad Y, Awad A, Alakel W, et al. Data mining of routine laboratory tests can predict liver disease progression in Egyptian diabetic patients with hepatitis C virus (G4) infection: a cohort study of 71 806 patients[J]. Eur J Gastroenterol Hepatol, 2018, 30(2): 201-206.
[16]
Omran DAEH, Awad AH, Mabrouk MAER, et al. Application of data mining techniques to explore predictors of HCC in Egyptian patients with HCV-related chronic liver disease[J]. Asian Pac J Cancer Prev, 2015, 16(1): 381-385.
[17]
Yang J, Guo F, Lyu T, et al. Research of artificial intelligence-based clinical decision support system for primary hepatocellular carcinoma[J]. Natl Med J China, 2020, 100(48): 3870-3873.
[杨俭, 郭飞, 吕涛, 等. 肝癌人工智能临床决策支持系统研究[J]. 中华医学杂志, 2020, 100(48): 3870-3873.]
[18]
Rau HH, Hsu CY, Lin Y, et al. Development of a web-based liver cancer prediction model for type II diabetes patients by using an artificial neural network[J]. Comput Methods Programs Biomed, 2016, 125: 58-65.
[19]
Lin YJ, Chen RJ, Tang JH, et al. Machine-learning monitoring system for predicting mortality among patients with noncancer end-stage liver disease: retrospective study[J]. JMIR Med Inform, 2020, 8(10): e24305.
[20]
Schoenberg MB, Bucher JN, Koch D, et al. A novel machine learning algorithm to predict disease free survival after resection of hepatocellular carcinoma[J]. Ann Transl Med, 2020, 8(7):434.
[21]
Kazemi A, Kazemi K, Sami A, et al. Identifying factors that affect patient survival after orthotopic liver transplant using machine-learning techniques[J]. Exp Clin Transplant, 2019, 17(6): 775-783.
[22]
Hu C, Anjur V, Saboo K, et al. Low predictability of readmissions and death using machine learning in cirrhosis[J]. Am J Gastroenterol, 2021, 116(2): 336-346.
[23]
Heidemann L, Law J, Fontana RJ. A text searching tool to identify patients with idiosyncratic drug-induced liver injury[J]. Dig Dis Sci, 2017, 62(3): 615-625.
[24]
Bastarache L, Hughey JJ, Hebbring S, et al. Phenotype risk scores identify patients with unrecognized Mendelian disease patterns[J]. Science, 2018, 359(6381): 1233-1239.
[25]
Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, et al. Application of artificial intelligence in chronic liver diseases: a systematic review and meta-analysis[J]. BMC Gastroenterol, 2021, 21(1): 10.
[26]
Gatos I, Tsantis S, Spiliopoulos S, et al. A new computer aided diagnosis system for evaluation of chronic liver disease with ultrasound shear wave elastography imaging[J]. Med Phys, 2016, 43(3): 1428-1436.
[27]
Gatos I, Tsantis S, Spiliopoulos S, et al. Temporal stability assessment in shear wave elasticity images validated by deep learning neural network for chronic liver disease fibrosis stage assessment[J]. Med Phys, 2019, 46(5): 2298-2309.
[28]
Chen Y, Luo Y, Huang W, et al. Machine-learning-based classification of real-time tissue elastography for hepatic fibrosis in patients with chronic hepatitis B[J]. Comput Biol Med, 2017, 89: 18-23.
[29]
Alirr OI. Deep learning and level set approach for liver and tumor segmentation from CT scans[J]. J Appl Clin Med Phys, 2020, 21(10): 200-209.
[30]
Esses SJ, Lu XG, Zhao TJ, et al. Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture[J]. J Magn Reson Imaging, 2018, 47(3): 723-728.
[31]
Yasaka K, Akai H, Abe O, et al. Deep learning with convolutional neural network for differentiation of liver masses at dynamic contrast-enhanced CT: a preliminary study[J]. Radiology, 2018, 286(3): 887-896.
[32]
Schmauch B, Herent P, Jehanno P, et al. Diagnosis of focal liver lesions from ultrasound using deep learning[J]. Diagn Interv Imaging, 2019, 100(4): 227-233.
[33]
Guo LH, Wang D, Qian YY, et al. A two-stage multi-view learning framework based computer-aided diagnosis of liver tumors with contrast enhanced ultrasound images[J]. Clin Hemorheol Microcirc, 2018, 69(3): 343-354.
[34]
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
[35]
Vivanti R, Szeskin A, Lev-Cohain N, et al. Automatic detection of new tumors and tumor burden evaluation in longitudinal liver CT scan studies[J]. Int J Comput Assist Radiol Surg, 2017, 12(11): 1945-1957.
[36]
Morshid A, Elsayes KM, Khalaf AM, et al. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolization[J]. Radiol Artif Intell, 2019, 1(5): e180021.
[37]
Liu F, Goh GB, Tiniakos D, et al. qFIBS: an automated technique for quantitative evaluation of fibrosis, inflammation, ballooning, and steatosis in patients with nonalcoholic steatohepatitis[J]. Hepatology, 2020, 71(6): 1953-1966.
[38]
Taylor-Weiner A, Pokkalla H, Han L, et al. A machine learning approach enables quantitative measurement of liver histology and disease monitoring in NASH[J]. Hepatology, 2021, 74(1):133-147.
[39]
Goh GB, Leow WQ, Liang S, et al. Quantification of hepatic steatosis in chronic liver disease using novel automated method of second harmonic generation and two-photon excited fluorescence[J]. Sci Rep, 2019, 9(1): 2975.
[40]
Roy M, Wang F, Vo H, et al. Deep-learning-based accurate hepatic steatosis quantification for histological assessment of liver biopsies[J]. Lab Invest, 2020, 100(10): 1367-1383.
[41]
Li SQ, Jiang HY, Pang WB. Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading[J]. Comput Biol Med, 2017, 84: 156-167.
[42]
Chen M, Zhang B, Topatana W, et al. Classification and mutation prediction based on histopathology H&E images in liver cancer using deep learning[J]. NPJ Precis Oncol, 2020, 4: 14.
[43]
Fu Y, Jung AW, Torne RV, et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis[J]. Nat Cancer, 2020, 1(8): 800-810.
[44]
Cao JS, Lu ZY, Chen MY, et al. Artificial intelligence in gastroenterology and hepatology: Status and challenges[J]. World J Gastroenterol, 2021, 27(16): 1664-1690.
2022年第47卷第8期
PDF下载
179
80
引用本文
BibTeX
文章信息
doi: 10.11855/j.issn.0577-7402.2022.08.0845
  • 接收时间：2021-07-29
  • 首发时间：2025-12-15
  • 出版时间：2022-08-28
补充材料
相关文章
文章信息
作者
出版历史
  • 收稿日期：2021-07-29
  • 录用日期：2021-11-01
基金
作者信息
    1陆军军医大学第一附属医院感染科，重庆 400038
    2重庆大学自动化学院，重庆 400044

通讯作者:

郭茂耘，E-mail：
参考文献
分享链接
https://castjournals.cast.org.cn/joweb/jfjyxzz/CN/10.11855/j.issn.0577-7402.2022.08.0845
分享至
全文二维码

扫描看全文

引用本文
BibTeX
本文的引用情况
2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
关闭全屏
相关链接
论文
最新文章
热读文章
热点专题
内参
蓝皮书
产业发展报告
传播力报告
期刊分级目录
资讯
学术新闻
行业新闻
学术会议
行业会议
关于
关于我们
联系我们
北京市海淀区学院南路86号
100081
010-62199257
qkjq@cast.org.cn

中国科学技术协会版权所有 京ICP 备10216604号-4 海淀分局备案 1101084647
科技导报社主办 中国科协信息中心技术支持