Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
Rapid Identification of Pathogens01:25

Rapid Identification of Pathogens

MALDI-TOF MS has transformed clinical microbiology by offering a rapid and reliable method for pathogen identification. The traditional approach to microbial identification typically involves time-consuming culture techniques and biochemical tests, which can delay the initiation of appropriate antimicrobial therapy. MALDI-TOF MS avoids these delays by using characteristic ribosomal protein mass patterns of microbial cells, enabling accurate species-level identification within minutes.Principle...
Automated Microbial Diagnostics01:24

Automated Microbial Diagnostics

Automated diagnostic analyzers have transformed clinical microbiology by providing rapid and reliable methods for pathogen identification and antibiotic susceptibility testing. Among these systems, the Vitek 2 is widely used because it automates the traditionally labor-intensive processes of microbial identification (ID) and antibiotic susceptibility testing (AST), delivering standardized and timely results that are essential for effective patient care.Microbial Identification with ID CardsThe...
Candidiasis01:20

Candidiasis

Candidiasis is a fungal infection caused by opportunistic species of Candida. It can affect various anatomical sites, including the skin, oral cavity, nails, and genitourinary tract. Among its forms, vaginal candidiasis is the most common type of mucosal infection. It typically results from the overgrowth of Candida albicans in the vaginal mucosa. Under normal conditions, C. albicans exists as a commensal organism within the vaginal microbiota, regulated by the dominance of lactobacilli, which...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Frequency-Guided Cross-Modal Interaction for Multimodal Yeast Classification Based on Light-Scattering and Microscopy Images.

Journal of imaging·2026
Same author

Peering Inside the Black Box: Explainable AI to Interpret Advanced Computer Vision Fungal Pathogen Prediction.

Scientific reports·2026
Same author

Machine Learning-Assisted Classification of Pathogenic Yeasts Using Laser Light Scattering and Conventional Microscopy.

Journal of imaging·2026
Same author

Fitness effects of a demography-dispersal trade-off in expanding<i>Saccharomyces cerevisiae</i>mats.

Physical biology·2024
Same author

Cladosporium halotolerans: Exploring an Unheeded Human Pathogen.

Mycopathologia·2023
Same author

Quantitative systems-based prediction of antimicrobial resistance evolution.

NPJ systems biology and applications·2023

相关实验视频

Updated: Jul 16, 2026

Candida albicans Biofilm Chip CaBChip for High-throughput Antifungal Drug Screening
08:05

Candida albicans Biofilm Chip CaBChip for High-throughput Antifungal Drug Screening

Published on: July 18, 2012

13.7K

机器学习用于识别临床相关的Candida酵母菌种类.

Shamanth A Shankarnarayan1, Daniel A Charlebois1,2

  • 1Department of Physics, University of Alberta, Edmonton, Alberta, T6G-2E1, Canada.

Medical mycology
|December 22, 2023
PubMed
概括

机器学习可以从显微镜图像中准确识别Candida物种. 发明V3模型表现最好,改善了关键真菌病原体的识别率.

科学领域:

  • 医学真菌学 医学真菌学
  • 计算生物学 计算生物学
  • 医疗信息学 医疗信息学

背景情况:

  • 菌感染的发病率上升,特别是Candida物种.
  • 在快速和准确的识别多药耐药性Candida auris.挑战.
  • 机器学习在医疗保健和医学成像中的应用越来越多.

研究的目的:

  • 评估六个卷积神经网络 (CNN) 的有效性,以识别四种临床意义上的Candida物种.
  • 用显微镜图像来比较不同CNN架构的性能.
  • 为了确定最佳的机器学习方法来识别Candida物种.

主要方法:

  • 获取Candida物种的湿装显微镜图像.
  • 将图像分为单细胞,芽细胞和细胞组类别.
  • 应用六种机器学习算法 (定制CNN,VGG16,ResNet50,InceptionV3,EfficientNetB0,EfficientNetB7) 来进行物种预测.

主要成果:

  • InceptionV3在从显微镜图像中预测Candida物种方面表现出卓越的表现.
  • 所有模型在原始,未经处理的图像上表现不佳,但在单细胞和芽细胞图像上有所改善.
  • InceptionV3在识别C. albicans,C. auris,C. glabrata和C. haemulonii的芽和单细胞方面取得了很高的准确率.
关键词:
在Candida种类的Candida物种中.深度神经网络是一个神经网络.菌性感染 菌性感染 菌性感染机器学习是机器学习.医学人工智能诊断AI诊断

更多相关视频

Use of Image Cytometry for Quantification of Pathogenic Fungi in Association with Host Cells
07:58

Use of Image Cytometry for Quantification of Pathogenic Fungi in Association with Host Cells

Published on: June 19, 2013

13.0K
Whole Genome Sequencing of Candida glabrata for Detection of Markers of Antifungal Drug Resistance
08:45

Whole Genome Sequencing of Candida glabrata for Detection of Markers of Antifungal Drug Resistance

Published on: December 28, 2017

13.8K

相关实验视频

Last Updated: Jul 16, 2026

Candida albicans Biofilm Chip CaBChip for High-throughput Antifungal Drug Screening
08:05

Candida albicans Biofilm Chip CaBChip for High-throughput Antifungal Drug Screening

Published on: July 18, 2012

13.7K
Use of Image Cytometry for Quantification of Pathogenic Fungi in Association with Host Cells
07:58

Use of Image Cytometry for Quantification of Pathogenic Fungi in Association with Host Cells

Published on: June 19, 2013

13.0K
Whole Genome Sequencing of Candida glabrata for Detection of Markers of Antifungal Drug Resistance
08:45

Whole Genome Sequencing of Candida glabrata for Detection of Markers of Antifungal Drug Resistance

Published on: December 28, 2017

13.8K

结论:

  • 湿装幻灯片的显微镜图像可以有效地利用机器学习来快速准确地识别Candida酵母物种.
  • InceptionV3模型显示了在真菌诊断中临床应用的巨大潜力.
  • 进一步开发机器学习模型可以提高识别具有挑战性的真菌病原体的速度和准确性.