A narrative review of digital pathology and artificial intelligence: focusing on lung cancer.
A narrative review of digital pathology and artificial intelligence: focusing on lung cancer.
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DOI：
10.21037/tlcr-20-591
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发表时间：
2020-10
影响因子：
4
通讯作者：
Fukuoka J
中科院分区：
文献类型：
--
作者：
Sakamoto T;Furukawa T;Lami K;Pham HHN;Uegami W;Kuroda K;Kawai M;Sakanashi H;Cooper LAD;Bychkov A;Fukuoka J
全玻片成像技术的出现使得可以在计算机屏幕上进行病理诊断。数字病理学的应用正在扩大，从支持病理学家短缺的偏远机构到日常诊断（包括肺癌）的常规使用。通过实践和研究，已经开发了数字病理图像的大型档案数据库，这将促进图像分析人工智能（AI）方法的发展。目前，已有多项人工智能在肺癌领域的应用报道；这些包括癌灶的分割、淋巴结转移的检测、肿瘤细胞的计数以及基因突变的预测。尽管将人工智能算法整合到临床实践中仍然是一个重大挑战，但我们已经实现了肿瘤细胞计数用于遗传分析，这是一个对常规使用很有帮助的应用。我们的经验表明，病理学家经常高估肿瘤细胞的含量，而使用基于人工智能的分析可以提高准确性并使任务变得不那么乏味。然而，人工智能在临床诊断中的实际应用遇到了一些困难。其中包括缺乏足够的注释数据用于人工智能系统的开发和验证，黑盒人工智能模型的可解释性，例如基于深度学习的模型，提供最有前途的性能，以及由于大多数应用程序固有的模糊性而难以定义用于训练和验证的地面实况数据。所有这些共同对病理学实践中人工智能方法的开发和临床转化提出了重大挑战。对这些问题的进一步研究将有助于解决人工智能临床应用的障碍。帮助病理学家了解人工智能的工作原理和局限性将有利于人工智能在诊断和研究中的使用。
The emergence of whole slide imaging technology allows for pathology diagnosis on a computer screen. The applications of digital pathology are expanding, from supporting remote institutes suffering from a shortage of pathologists to routine use in daily diagnosis including that of lung cancer. Through practice and research large archival databases of digital pathology images have been developed that will facilitate the development of artificial intelligence (AI) methods for image analysis. Currently, several AI applications have been reported in the field of lung cancer; these include the segmentation of carcinoma foci, detection of lymph node metastasis, counting of tumor cells, and prediction of gene mutations. Although the integration of AI algorithms into clinical practice remains a significant challenge, we have implemented tumor cell count for genetic analysis, a helpful application for routine use. Our experience suggests that pathologists often overestimate the contents of tumor cells, and the use of AI-based analysis increases the accuracy and makes the tasks less tedious. However, there are several difficulties encountered in the practical use of AI in clinical diagnosis. These include the lack of sufficient annotated data for the development and validation of AI systems, the explainability of black box AI models, such as those based on deep learning that offer the most promising performance, and the difficulty in defining the ground truth data for training and validation owing to inherent ambiguity in most applications. All of these together present significant challenges in the development and clinical translation of AI methods in the practice of pathology. Additional research on these problems will help in resolving the barriers to the clinical use of AI. Helping pathologists in developing knowledge of the working and limitations of AI will benefit the use of AI in both diagnostics and research.
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影响因子：
4.6
作者：
Bychkov D;Linder N;Turkki R;Nordling S;Kovanen PE;Verrill C;Walliander M;Lundin M;Haglund C;Lundin J
通讯作者：
Lundin J
影响因子：
10.9
作者：
Althammer, Sonja;Tan, Tze Heng;Steele, Keith E.
通讯作者：
Steele, Keith E.
影响因子：
2.6
作者：
Huang, Yingxin;Lei, Yan;Deng, Yongjian
通讯作者：
Deng, Yongjian
影响因子：
78.8
作者：
Bera, Kaustav;Schalper, Kurt A.;Madabhushi, Anant
通讯作者：
Madabhushi, Anant
影响因子：
4.6
作者：
Antonio Retamero, Juan;Aneiros-Fernandez, Jose;del Moral, Raimundo G.
通讯作者：
del Moral, Raimundo G.