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机器学习用于图像分析的使用临床微生物学中的人工智能

Bethany L Burns¹, Daniel D Rhoads^1,2,3, Anisha Misra¹

¹Department of Laboratory Medicine, Cleveland Clinic, Cleveland, Ohio, USA.

Journal of clinical microbiology

|July 3, 2023

概括

人工智能 (AI) 图像分析工具正在改变临床微生物学. 这些工具提高了效率和质量,但目前增加,而不是取代,在实验室的人类专业知识.

科学领域:

临床微生物学临床微生物学
数字病理学数字病理学
医疗保健中的人工智能

背景情况:

临床实验室的数字化转型使得基于软件的图像解释成为可能.
人工智能 (AI) 和机器学习 (ML) 越来越多地融入临床微生物学.
图像分析人工智能 (IAAI) 工具正在进入常规实践,范围和影响正在扩大.

研究的目的:

审查图像分析AI (IAAI) 在临床微生物学中的应用.
探索IAAI工具的好处,挑战,开发和实施.
将IAAI的应用分类为罕见事件检测和基于分数的分类.

主要方法:

审查现有的文献和IAAI在临床微生物学中的应用.
将IAAI工具分为两个主要类别:罕见事件检测/分类和基于分数/分类的分类.
探索IAAI的好处,挑战,发展和实施战略.

主要成果:

IAAI的应用程序分为罕见事件检测 (例如,识别菌根菌,细菌殖民地,寄生虫) 和基于分数的分类 (例如,细菌性阴道炎的Nugent分数,尿液培养解释).
IAAI的工具开始影响常规的临床微生物学实践.
IAAI可以提高临床微生物学实践的效率和质量.

结论:

IAAI是临床微生物学的一个有前途的技术,提高了效率和质量.
目前的IAAI工具增强了人类的专业知识,而不是取代它.
预计IAAI在日常实践中的整合将会越来越多.

关键词:

人工智能的人工智能是人工智能.临床微生物学临床微生物学机器学习是机器学习.

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