Wong Alex Ngai Nick, He Zebang, Leung Ka Long, To Curtis Chun Kit, Wong Chun Yin, Wong Sze Chuen Cesar, Yoo Jung Sun, Chan Cheong Kin Ronald, Chan Angela Zaneta, Lacambra Maribel D, Yeung Martin Ho Yin
Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China.
Department of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, Prince of Wales Hospital, Shatin, Hong Kong SAR, China.
Cancers (Basel). 2022 Aug 3;14(15):3780. doi: 10.3390/cancers14153780.
The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.
数字病理（DP）的实施将为病理学家提供额外的工具和算法，以改善工作流程，从而彻底改变当前的实践。此外，数字病理将为基于人工智能的工具的开发创造机会，通过计算病理学实现更精确和可重复的诊断。人工智能的一个关键特性是其能够产生超越人类感官的感知并识别模式。因此，将人工智能融入数字病理可以揭示额外的形态学特征和信息。以目前人工智能的发展速度和数字病理的采用情况来看，预计对计算病理学的兴趣将同步上升。在前列腺癌检测中，基于人工智能的解决方案已经取得了令人鼓舞的进展；然而，在胃肠道领域，需要开发更复杂的算法，以促进对胃肠道标本进行组织学评估，实现早期准确诊断。在这篇综述中，我们旨在概述当前解剖病理学实验室的组织学实践，涉及图像预处理中面临的挑战，介绍现有的基于人工智能的算法，讨论其局限性，并就人工智能在胃肠道癌症早期检测和诊断中的应用提供临床见解。
