Background

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 histopathologic slides. They also must classify colony growth on a variety of agar plates for triage and assessment. 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.

Objectives

To review current AI-based image analysis as applied to clinical microbiology; and to discuss future trends in the field.

Sources

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.

Content

We describe application of machine learning towards analysis of different types of microbiologic image data. Specifically, we outline progress in smear and plate interpretation as well as the potential for AI diagnostic applications in the clinical microbiology laboratory.

Implications

Combined with automation, we predict that AI algorithms will be used in the future to prescreen and preclassify image data, thereby increasing productivity and enabling more accurate diagnoses through collaboration between the AI and the microbiologist. Once developed, image-based AI analysis is inexpensive and amenable to local and remote diagnostic use.

中文翻译：

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传染病诊断中的图像分析和人工智能。

背景

微生物学家因其在图像分析方面久经磨练的技能而受到重视，包括在革兰氏染色、卵子和寄生虫制剂、血涂片和组织病理学切片中识别病原体和炎症背景。他们还必须对各种琼脂平板上的菌落生长进行分类，以进行分类和评估。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。

目标

审查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。

来源

本次审查的材料包括 PubMed 或 Google Scholar 数据库中注释的同行评审文献以及来自 bioRxiv 的预印本文章。审查了描述使用人工智能分析传染病诊断中使用的图像的文章。

内容

我们描述了机器学习在分析不同类型微生物图像数据方面的应用。具体而言，我们概述了涂片和平板解释的进展以及临床微生物学实验室中 AI 诊断应用的潜力。

影响

结合自动化，我们预测未来将使用人工智能算法对图像数据进行预筛选和预分类，从而通过人工智能和微生物学家之间的协作提高生产力并实现更准确的诊断。一旦开发，基于图像的 AI 分析成本低廉，并且适合本地和远程诊断使用。