数字病理学的可重复性和可解释性:让黑盒人工智能系统更加透明的必要性。

IF 1.6 Q3 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH
Journal of Public Health Research Pub Date : 2024-10-29 eCollection Date: 2024-10-01 DOI:10.1177/22799036241284898
Gavino Faa, Matteo Fraschini, Luigi Barberini
{"title":"数字病理学的可重复性和可解释性:让黑盒人工智能系统更加透明的必要性。","authors":"Gavino Faa, Matteo Fraschini, Luigi Barberini","doi":"10.1177/22799036241284898","DOIUrl":null,"url":null,"abstract":"<p><p>Artificial intelligence (AI), and more specifically Machine Learning (ML) and Deep learning (DL), has permeated the digital pathology field in recent years, with many algorithms successfully applied as new advanced tools to analyze pathological tissues. The introduction of high-resolution scanners in histopathology services has represented a real revolution for pathologists, allowing the analysis of digital whole-slide images (WSI) on a screen without a microscope at hand. However, it means a transition from microscope to algorithms in the absence of specific training for most pathologists involved in clinical practice. The WSI approach represents a major transformation, even from a computational point of view. The multiple ML and DL tools specifically developed for WSI analysis may enhance the diagnostic process in many fields of human pathology. AI-driven models allow the achievement of more consistent results, providing valid support for detecting, from H&E-stained sections, multiple biomarkers, including microsatellite instability, that are missed by expert pathologists.</p>","PeriodicalId":45958,"journal":{"name":"Journal of Public Health Research","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528586/pdf/","citationCount":"0","resultStr":"{\"title\":\"Reproducibility and explainability in digital pathology: The need to make black-box artificial intelligence systems more transparent.\",\"authors\":\"Gavino Faa, Matteo Fraschini, Luigi Barberini\",\"doi\":\"10.1177/22799036241284898\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Artificial intelligence (AI), and more specifically Machine Learning (ML) and Deep learning (DL), has permeated the digital pathology field in recent years, with many algorithms successfully applied as new advanced tools to analyze pathological tissues. The introduction of high-resolution scanners in histopathology services has represented a real revolution for pathologists, allowing the analysis of digital whole-slide images (WSI) on a screen without a microscope at hand. However, it means a transition from microscope to algorithms in the absence of specific training for most pathologists involved in clinical practice. The WSI approach represents a major transformation, even from a computational point of view. The multiple ML and DL tools specifically developed for WSI analysis may enhance the diagnostic process in many fields of human pathology. AI-driven models allow the achievement of more consistent results, providing valid support for detecting, from H&E-stained sections, multiple biomarkers, including microsatellite instability, that are missed by expert pathologists.</p>\",\"PeriodicalId\":45958,\"journal\":{\"name\":\"Journal of Public Health Research\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-10-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11528586/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Public Health Research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/22799036241284898\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/10/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Public Health Research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/22799036241284898","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/10/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0

摘要

近年来,人工智能(AI),特别是机器学习(ML)和深度学习(DL),已经渗透到数字病理学领域,许多算法被成功应用为分析病理组织的新型先进工具。在组织病理学服务中引入高分辨率扫描仪对病理学家来说是一场真正的革命,使他们可以在屏幕上分析数字全切片图像(WSI),而无需使用显微镜。然而,这意味着大多数参与临床实践的病理学家在缺乏专门培训的情况下,需要从显微镜过渡到算法。即使从计算的角度来看,WSI 方法也是一个重大转变。专为 WSI 分析开发的多种 ML 和 DL 工具可增强人类病理学许多领域的诊断过程。人工智能驱动的模型可以获得更一致的结果,为从 H&E 染色切片中检测包括微卫星不稳定性在内的多种生物标记物提供有效支持,而这些标记物是病理专家所遗漏的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reproducibility and explainability in digital pathology: The need to make black-box artificial intelligence systems more transparent.

Artificial intelligence (AI), and more specifically Machine Learning (ML) and Deep learning (DL), has permeated the digital pathology field in recent years, with many algorithms successfully applied as new advanced tools to analyze pathological tissues. The introduction of high-resolution scanners in histopathology services has represented a real revolution for pathologists, allowing the analysis of digital whole-slide images (WSI) on a screen without a microscope at hand. However, it means a transition from microscope to algorithms in the absence of specific training for most pathologists involved in clinical practice. The WSI approach represents a major transformation, even from a computational point of view. The multiple ML and DL tools specifically developed for WSI analysis may enhance the diagnostic process in many fields of human pathology. AI-driven models allow the achievement of more consistent results, providing valid support for detecting, from H&E-stained sections, multiple biomarkers, including microsatellite instability, that are missed by expert pathologists.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Journal of Public Health Research
Journal of Public Health Research PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
3.70
自引率
4.30%
发文量
116
审稿时长
10 weeks
期刊介绍: The Journal of Public Health Research (JPHR) is an online Open Access, peer-reviewed journal in the field of public health science. The aim of the journal is to stimulate debate and dissemination of knowledge in the public health field in order to improve efficacy, effectiveness and efficiency of public health interventions to improve health outcomes of populations. This aim can only be achieved by adopting a global and multidisciplinary approach. The Journal of Public Health Research publishes contributions from both the “traditional'' disciplines of public health, including hygiene, epidemiology, health education, environmental health, occupational health, health policy, hospital management, health economics, law and ethics as well as from the area of new health care fields including social science, communication science, eHealth and mHealth philosophy, health technology assessment, genetics research implications, population-mental health, gender and disparity issues, global and migration-related themes. In support of this approach, JPHR strongly encourages the use of real multidisciplinary approaches and analyses in the manuscripts submitted to the journal. In addition to Original research, Systematic Review, Meta-analysis, Meta-synthesis and Perspectives and Debate articles, JPHR publishes newsworthy Brief Reports, Letters and Study Protocols related to public health and public health management activities.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信