利用数字病理学和人工智能在胃活检中诊断幽门螺杆菌。

IF 4.4 Q1 PATHOLOGY
PATHOLOGICA Pub Date : 2022-08-01 DOI:10.32074/1591-951X-751
Daniel S Liscia, Mariangela D'Andrea, Elena Biletta, Donata Bellis, Kejsi Demo, Franco Ferrero, Alberto Petti, Roberto Butinar, Enzo D'Andrea, Giuditta Davini
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引用次数: 8

摘要

目的:对数字病理学(DP)的一个共同关注来源是有限的分辨率可能是医疗事故风险增加的原因。关于这项技术,人们经常提出的一个问题是,它能否可靠地用于胃活检中检测幽门螺杆菌(HP),这可能是日常工作中的一个重大负担。这项工作的主要目的是表明,即使在低倍率下,也可以通过DP可靠地诊断HP感染。第二个目标是证明人工智能(AI)算法可以足够准确地诊断虚拟载玻片上的HP感染。方法:我们提出的方法是基于Warthin-Starry (W-S)银染色,可以更快地检测虚拟载玻片中的HP。一个基于正则表达式的软件工具执行了一个特定的搜索,以选择679个活检组织进行W-S染色。从该数据集中选择185个虚拟载玻片进行WSI评估,并与显微镜载玻片读数进行比较。确定HP感染是否可以用机器学习准确诊断。人工智能作为一种服务(AIaaS)在一个基于神经网络的web平台上使用,该平台训练了468张图像。使用210张图像的测试数据集来评估分类器的性能。结果:在185例胃活检中,我们记录了4例假阳性和4例假阴性,总体一致性为95.6%。与被定义为HP感染诊断“金标准”的镜检相比,WSI的敏感性和特异性分别为0.95和0.96。我们的AI分类器在210张图像的测试数据集上生成的ROC曲线AUC为0.938。结论:本研究表明,DP和AI可以在20X分辨率下可靠地识别HP。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies.

Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies.

Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies.

Use of digital pathology and artificial intelligence for the diagnosis of Helicobacter pylori in gastric biopsies.

Objective: A common source of concern about digital pathology (DP) is that limited resolution could be a reason for an increased risk of malpractice. A frequent question being raised about this technology is whether it can be used to reliably detect Helicobacter pylori (HP) in gastric biopsies, which can be a significant burden in routine work. The main goal of this work is to show that a reliable diagnosis of HP infection can be made by DP even at low magnification. The secondary goal is to demonstrate that artificial intelligence (AI) algorithms can diagnose HP infections on virtual slides with sufficient accuracy.

Methods: The method we propose is based on the Warthin-Starry (W-S) silver stain which allows faster detection of HP in virtual slides. A software tool, based on regular expressions, performed a specific search to select 679 biopsies on which a W-S stain was done. From this dataset 185 virtual slides were selected to be assessed by WSI and compared with microscopy slide readings. To determine whether HP infections could be accurately diagnosed with machine learning. AI was used as a service (AIaaS) on a neural network-based web platform trained with 468 images. A test dataset of 210 images was used to assess the classifier performance.

Results: In 185 gastric biopsies read with DP we recorded only 4 false positives and 4 false negatives with an overall agreement of 95.6%. Compared with microscopy, defined as the "gold standard" for the diagnosis of HP infections, WSI had a sensitivity and specificity of 0.95 and 0.96, respectively. The ROC curve of our AI classifier generated on a testing dataset of 210 images had an AUC of 0.938.

Conclusions: This study demonstrates that DP and AI can be used to reliably identify HP at 20X resolution.

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来源期刊
PATHOLOGICA
PATHOLOGICA PATHOLOGY-
CiteScore
5.90
自引率
5.70%
发文量
108
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