Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

Alex Ngai Nick Wong, Zebang He, Ka Long Leung, Curtis Chun Kit To, Chun Yin Wong, Sze Chuen Cesar Wong, Jung Sun Yoo, Cheong Kin Ronald Chan, Angela Zaneta Chan, Maribel D. Lacambra, Martin Ho Yin Yeung

Research output: Journal article publicationReview articleAcademic researchpeer-review

19 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number3780
JournalCancers
Volume14
Issue number15
DOIs
Publication statusPublished - Aug 2022

Keywords

  • algorithms
  • artificial intelligence
  • cancer diagnosis
  • computational pathology
  • deep learning
  • digital pathology
  • gastrointestinal tract
  • histopathology
  • machine learning
  • whole-slide imaging

ASJC Scopus subject areas

  • Oncology
  • Cancer Research

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