人工智能时代的计算病理学——拥抱而不是恐惧

IF 3.7 2区 医学 Q1 PATHOLOGY
Alfonso Tan-Garcia, Tzy Harn Chua, Wei-Qiang Leow
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引用次数: 0

摘要

解剖病理学传统上依赖于训练有素的病理学家在光学显微镜下对组织形态学特征的解释进行诊断。技术的进步使得组织切片的数字化能够产生高分辨率的全切片图像,预示着数字病理学(DP)的时代。世界各地的许多实验室已经将DP纳入他们的日常工作流程,因为它在促进肿瘤委员会讨论,远程报告,教学和研究方面提供了无数的应用程序。最重要的是,DP产生了计算病理学领域,这是一个结合人工智能(AI)模型的组织病理学的新分支。由于计算病理学具有提高诊断准确性、个性化治疗和简化工作流程的潜力,因此它已被用于组织形态学定量和诊断、预测和预后应用。在这里,我们重点介绍近年来发表在该杂志上的Meier等人、Shen等人和Lee等人的工作,他们分别应用AI模型预测胃癌、乳腺癌和弥漫性大b细胞淋巴瘤的生存和治疗反应。总的来说,这些研究说明了将人工智能纳入DP管道的各种方法及其潜在的临床应用。与诊断准确性、成本、患者保密性和监管伦理相关的问题仍然需要在该领域得到解决。尽管如此,病理学家的整体情绪是谨慎乐观的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Computational pathology in the age of artificial intelligence – embrace not fear

Computational pathology in the age of artificial intelligence – embrace not fear

Computational pathology in the age of artificial intelligence – embrace not fear

Computational pathology in the age of artificial intelligence – embrace not fear

Anatomical pathology has traditionally relied on the interpretation of histomorphological features under a light microscope by trained pathologists for diagnosis. Technological advancements have enabled the digitisation of tissue slides to produce high-resolution whole slide images, heralding the era of digital pathology (DP). Many laboratories around the world have incorporated DP into their routine workflows owing to the myriad applications it offers in facilitating tumour board discussions, remote reporting, teaching, and research. Most significantly, DP has engendered the field of computational pathology, a novel branch of histopathology incorporating artificial intelligence (AI) models. Computational pathology has been utilised in histomorphological quantification and diagnostic, predictive, and prognostic applications due to its potential to improve diagnostic accuracy, personalise treatment, and streamline workflows. Here, we highlight the work of Meier et al, Shen et al, and Lee et al, published in this journal in recent years, as they apply AI models to predict survival and treatment responses in gastric cancer, breast cancer, and diffuse large B-cell lymphoma, respectively. Collectively, these studies illustrate various approaches to incorporating AI into the DP pipeline and their potential clinical applications. Issues related to diagnostic accuracy, cost, patient confidentiality, and regulatory ethics still need to be addressed within the field. Despite this, the overall sentiment among pathologists is one of cautious optimism.

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来源期刊
Journal of Pathology Clinical Research
Journal of Pathology Clinical Research Medicine-Pathology and Forensic Medicine
CiteScore
7.40
自引率
2.40%
发文量
47
审稿时长
20 weeks
期刊介绍: The Journal of Pathology: Clinical Research and The Journal of Pathology serve as translational bridges between basic biomedical science and clinical medicine with particular emphasis on, but not restricted to, tissue based studies. The focus of The Journal of Pathology: Clinical Research is the publication of studies that illuminate the clinical relevance of research in the broad area of the study of disease. Appropriately powered and validated studies with novel diagnostic, prognostic and predictive significance, and biomarker discover and validation, will be welcomed. Studies with a predominantly mechanistic basis will be more appropriate for the companion Journal of Pathology.
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