Yang Yuanqing, Sun Kai, Gao Yanhua, Wang Kuansong, Yu Gang
Department of Biomedical Engineering, School of Basic Medical Sciences, Central South University, Changsha 410013, China.
Department of Biomedical Engineering, School of Medical, Tsinghua University, Beijing 100084, China.
Diagnostics (Basel). 2023 Oct 3;13(19):3115. doi: 10.3390/diagnostics13193115.
The pathology is decisive for disease diagnosis but relies heavily on experienced pathologists. In recent years, there has been growing interest in the use of artificial intelligence in pathology (AIP) to enhance diagnostic accuracy and efficiency. However, the impressive performance of deep learning-based AIP in laboratory settings often proves challenging to replicate in clinical practice. As the data preparation is important for AIP, the paper has reviewed AIP-related studies in the PubMed database published from January 2017 to February 2022, and 118 studies were included. An in-depth analysis of data preparation methods is conducted, encompassing the acquisition of pathological tissue slides, data cleaning, screening, and subsequent digitization. Expert review, image annotation, dataset division for model training and validation are also discussed. Furthermore, we delve into the reasons behind the challenges in reproducing the high performance of AIP in clinical settings and present effective strategies to enhance AIP's clinical performance. The robustness of AIP depends on a randomized collection of representative disease slides, incorporating rigorous quality control and screening, correction of digital discrepancies, reasonable annotation, and sufficient data volume. Digital pathology is fundamental in clinical-grade AIP, and the techniques of data standardization and weakly supervised learning methods based on whole slide image (WSI) are effective ways to overcome obstacles of performance reproduction. The key to performance reproducibility lies in having representative data, an adequate amount of labeling, and ensuring consistency across multiple centers. Digital pathology for clinical diagnosis, data standardization and the technique of WSI-based weakly supervised learning will hopefully build clinical-grade AIP.
病理学对疾病诊断起决定性作用，但很大程度上依赖经验丰富的病理学家。近年来，人们越来越关注在病理学中使用人工智能（AIP）以提高诊断准确性和效率。然而，基于深度学习的AIP在实验室环境中的出色表现往往在临床实践中难以复制。由于数据准备对AIP很重要，本文回顾了2017年1月至2022年2月在PubMed数据库中发表的与AIP相关的研究，共纳入118项研究。对数据准备方法进行了深入分析，包括病理组织切片的获取、数据清理、筛选及后续数字化。还讨论了专家评审、图像标注、用于模型训练和验证的数据集划分。此外，我们深入探讨了在临床环境中再现AIP高性能面临挑战的原因，并提出了提高AIP临床性能的有效策略。AIP的稳健性取决于随机收集具有代表性的疾病切片，包括严格的质量控制和筛选、数字差异校正、合理标注以及足够的数据量。数字病理学是临床级AIP的基础，数据标准化技术和基于全切片图像（WSI）的弱监督学习方法是克服性能再现障碍的有效途径。性能再现性的关键在于拥有代表性数据、足够的标注量以及确保多个中心之间的一致性。用于临床诊断的数字病理学、数据标准化以及基于WSI的弱监督学习技术有望构建临床级AIP。
