Development of a multi-scanner facility for data acquisition for digital pathology artificial intelligence

IF 5.6 2区 医学 Q1 ONCOLOGY
Matthew P Humphries, Danny Kaye, Gaby Stankeviciute, Jacob Halliwell, Alexander I Wright, Daljeet Bansal, David Brettle, Darren Treanor
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引用次数: 0

Abstract

Whole slide imaging (WSI) of pathology glass slides using high-resolution scanners has enabled the large-scale application of artificial intelligence (AI) in pathology, to support the detection and diagnosis of disease, potentially increasing efficiency and accuracy in tissue diagnosis. Despite the promise of AI, it has limitations. ‘Brittleness’ or sensitivity to variation in inputs necessitates that large amounts of data are used for training. AI is often trained on data from different scanners but not usually by replicating the same slide across scanners. The utilisation of multiple WSI instruments to produce digital replicas of the same slides will make more comprehensive datasets and may improve the robustness and generalisability of AI algorithms as well as reduce the overall data requirements of AI training. To this end, the National Pathology Imaging Cooperative (NPIC) has built the AI FORGE (Facilitating Opportunities for Robust Generalisable data Emulation), a unique multi-scanner facility embedded in a clinical site in the NHS to (1) compare scanner performance, (2) replicate digital pathology image datasets across WSI systems, and (3) support the evaluation of clinical AI algorithms. The NPIC AI FORGE currently comprises 15 scanners from nine manufacturers. It can generate approximately 4,000 WSI images per day (approximately 7 TB of image data). This paper describes the process followed to plan and build such a facility. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Abstract Image

开发用于数字病理学人工智能数据采集的多扫描仪设备。
使用高分辨率扫描仪对病理玻璃切片进行全切片成像(WSI),使人工智能(AI)得以在病理学领域大规模应用,从而为疾病的检测和诊断提供支持,并有可能提高组织诊断的效率和准确性。尽管人工智能大有可为,但它也有局限性。脆性 "或对输入变化的敏感性要求使用大量数据进行训练。人工智能通常根据不同扫描仪的数据进行训练,但通常不会在不同扫描仪上复制相同的切片。利用多台 WSI 仪器制作相同玻片的数字复制品,可以生成更全面的数据集,从而提高人工智能算法的稳健性和通用性,并减少人工智能训练所需的总体数据。为此,国家病理成像合作组织(NPIC)建立了 AI FORGE(促进稳健通用数据仿真的机会),这是一个独特的多扫描仪设施,嵌入到英国国家医疗服务系统(NHS)的一个临床站点中,用于(1)比较扫描仪性能,(2)在 WSI 系统中复制数字病理图像数据集,以及(3)支持临床 AI 算法的评估。NPIC AI FORGE 目前由来自 9 家制造商的 15 台扫描仪组成。它每天可生成约 4,000 张 WSI 图像(约 7 TB 图像数据)。本文介绍了规划和建设此类设施的过程。© 2024 作者。病理学杂志》由 John Wiley & Sons Ltd 代表大不列颠及爱尔兰病理学会出版。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
The Journal of Pathology
The Journal of Pathology 医学-病理学
CiteScore
14.10
自引率
1.40%
发文量
144
审稿时长
3-8 weeks
期刊介绍: The Journal of Pathology aims to serve as a translational bridge between basic biomedical science and clinical medicine with particular emphasis on, but not restricted to, tissue based studies. The main interests of the Journal lie in publishing studies that further our understanding the pathophysiological and pathogenetic mechanisms of human disease. The Journal of Pathology welcomes investigative studies on human tissues, in vitro and in vivo experimental studies, and investigations based on animal models with a clear relevance to human disease, including transgenic systems. As well as original research papers, the Journal seeks to provide rapid publication in a variety of other formats, including editorials, review articles, commentaries and perspectives and other features, both contributed and solicited.
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