精子形态评估标准化培训工具的研制。

IF 2.5 Q3 BIOCHEMICAL RESEARCH METHODS
Biology Methods and Protocols Pub Date : 2025-04-12 eCollection Date: 2025-01-01 DOI:10.1093/biomethods/bpaf029
Katherine R Seymour, Jessica P Rickard, Kelsey R Pool, Taylor Pini, Simon P de Graaf
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引用次数: 0

摘要

提高生物科学主观评价标准化的培训是提高和保持准确性的关键。然而,在生殖科学中,没有标准化的培训工具来评估精子形态。精子形态通常在几个物种中进行主观评估,并经常被用作拒绝或保留样品以供出售或人工授精的理由。与所有主观测试一样,精子形态评估容易受到人为偏见的影响,如果没有适当的标准化,这些评估是不可靠的。这项概念验证研究旨在开发一种标准化的精子形态评估培训工具,可以在每个精子的基础上对学生进行培训和测试。以下手稿概述了用于开发培训工具的方法,该工具具有考虑不同显微镜光学,形态分类系统和评估精子物种的能力。描述了图像的生成、分类、组织和集成到web界面,以及它的设计和输出。简单地说,精子图像是通过在DIC光学器件上以40倍放大率拍摄视场(FOV)图像生成的,总共有3600幅来自72只公羊(50 FOV/公羊)的FOV图像。使用一种新的机器学习算法,这些FOV图像被裁剪成每张图像只显示一个精子。由此产生的9365张图片由三名经验丰富的评估员标记,对所有标签(4821/9365)有100%共识的图像被整合到一个网络界面中,能够提供(i)用于培训目的的正确/不正确标签的即时反馈,以及(ii)对用户熟练程度的评估。未来的研究将测试训练工具在教育学生应用各种形态分类系统方面的有效性。如果被证明是有效的,它将是第一个在精子形态评估方面训练个体的标准化方法,并有助于提高对应该如何进行训练的理解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development of a sperm morphology assessment standardization training tool.

Training to improve the standardization of subjective assessments in biological science is crucial to improve and maintain accuracy. However, in reproductive science there is no standardized training tool available to assess sperm morphology. Sperm morphology is routinely assessed subjectively across several species and is often used as grounds to reject or retain samples for sale or insemination. As with all subjective tests, sperm morphology assessment is liable to human bias and without appropriate standardization these assessments are unreliable. This proof-of-concept study aimed to develop a standardized sperm morphology assessment training tool that can train and test students on a sperm-by-sperm basis. The following manuscript outlines the methods used to develop a training tool with the capability to account for different microscope optics, morphological classification systems, and species of spermatozoa assessed. The generation of images, their classification, organization, and integration into a web interface, along with its design and outputs, are described. Briefly, images of spermatozoa were generated by taking field of view (FOV) images at 40× magnification on DIC optics, amounting to a total of 3,600 FOV images from 72 rams (50 FOV/ram). These FOV images were cropped to only show one sperm per image using a novel machine-learning algorithm. The resulting 9,365 images were labelled by three experienced assessors, and those with 100% consensus on all labels (4821/9365) were integrated into a web interface able to provide both (i) instant feedback to users on correct/incorrect labels for training purposes, and (ii) an assessment of user proficiency. Future studies will test the effectiveness of the training tool to educate students on the application of a variety of morphology classification systems. If proven effective, it will be the first standardized method to train individuals in sperm morphology assessment and help to improve understanding of how training should be conducted.

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来源期刊
Biology Methods and Protocols
Biology Methods and Protocols Agricultural and Biological Sciences-Agricultural and Biological Sciences (all)
CiteScore
3.80
自引率
2.80%
发文量
28
审稿时长
19 weeks
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