Image analysis and artificial intelligence in infectious disease diagnostics.
Image analysis and artificial intelligence in infectious disease diagnostics.
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DOI：
10.1016/j.cmi.2020.03.012
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发表时间：
2020-10
期刊：
影响因子：
--
通讯作者：
Kirby JE
中科院分区：
文献类型：
--
作者：
Smith KP;Kirby JE
微生物学家在图像分析方面的时间磨练技能很有价值，包括在革兰氏染色，卵和寄生虫制剂，血涂片和组织病理学切片中识别病原体和炎症背景。他们还必须在各种琼脂平板上对菌落生长进行分类，以便进行分类和检查。图像分析的最新进展，特别是人工智能（AI）的应用，有可能使这些过程自动化，并支持更及时和准确的诊断。回顾当前应用于临床微生物学的基于人工智能的图像分析，并讨论该领域的未来趋势。本综述的材料来源包括PubMed或Google Scholar数据库中注释的同行评审文献和bioRxiv的预印本文章。综述了描述使用AI分析传染病诊断中使用的图像的文章。我们描述了机器学习对不同类型的微生物图像数据的分析的应用。具体而言，我们概述了涂片和平板判读的进展以及临床微生物学实验室中AI诊断应用的潜力。结合自动化，我们预测人工智能算法将在未来用于预筛选和预分类图像数据，从而提高生产力，并通过人工智能和微生物学家之间的合作实现更准确的诊断。一旦开发出来，基于图像的人工智能分析是廉价的，并且适合本地和远程诊断使用。
Microbiologists are valued for their time-honed skills in image analysis including identification of pathogens and inflammatory context in Gram stains, ova and parasite preparations, blood smears, and histopathological slides. They also must classify colonial growth on a variety of agar plates for triage and workup. Recent advances in image analysis, in particular application of artificial intelligence (AI), have the potential to automate these processes and support more timely and accurate diagnoses. To review current artificial intelligence-based image analysis as applied to clinical microbiology and discuss future trends in the field. Material sourced for this review included peer-reviewed literature annotated in the PubMed or Google Scholar databases and preprint articles from bioRxiv. Articles describing use of AI for analysis of images used in infectious disease diagnostics were reviewed. We describe application of machine learning towards analysis of different types of microbiological image data. Specifically, we outline progress in smear and plate interpretation and potential for AI diagnostic applications in the clinical microbiology laboratory. Combined with automation, we predict that AI algorithms will be used in the future to pre-screen and pre-classify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.
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影响因子：
1.7
作者：
Smith, Kenneth P.;Kirby, James E.
通讯作者：
Kirby, James E.
影响因子：
9.4
作者：
Faron, Matthew L.;Buchan, Blake W.;Ledeboer, Nathan A.
通讯作者：
Ledeboer, Nathan A.
显微镜图像中重叠细菌的分割、分裂和分类，用于自动细菌性阴道病诊断
DOI：
10.1109/jbhi.2016.2594239
发表时间：
2017-07-01
影响因子：
7.7
作者：
Song, Youyi;He, Liang;Wang, Tianfu
通讯作者：
Wang, Tianfu
DOI：
10.1016/j.trsl.2017.12.004
发表时间：
2018-04
期刊：
Translational research : the journal of laboratory and clinical medicine
影响因子：
--
作者：
Poostchi M;Silamut K;Maude RJ;Jaeger S;Thoma G
通讯作者：
Thoma G
DOI：
10.1038/s41379-018-0073-z
发表时间：
2018-10
期刊：
Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
影响因子：
--
作者：
Ehteshami Bejnordi B;Mullooly M;Pfeiffer RM;Fan S;Vacek PM;Weaver DL;Herschorn S;Brinton LA;van Ginneken B;Karssemeijer N;Beck AH;Gierach GL;van der Laak JAWM;Sherman ME
通讯作者：
Sherman ME