TY - JOUR
T1 - Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers
AU - Wong, Alex Ngai Nick
AU - He, Zebang
AU - Leung, Ka Long
AU - To, Curtis Chun Kit
AU - Wong, Chun Yin
AU - Wong, Sze Chuen Cesar
AU - Yoo, Jung Sun
AU - Chan, Cheong Kin Ronald
AU - Chan, Angela Zaneta
AU - Lacambra, Maribel D.
AU - Yeung, Martin Ho Yin
N1 - Funding Information:
This study was supported by Departmental Seed Fund for M.H.Y.Y. from The Hong Kong Polytechnic University. Grant Number: DDP.
Publisher Copyright:
© 2022 by the authors.
PY - 2022/8
Y1 - 2022/8
N2 - 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.
AB - 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.
KW - algorithms
KW - artificial intelligence
KW - cancer diagnosis
KW - computational pathology
KW - deep learning
KW - digital pathology
KW - gastrointestinal tract
KW - histopathology
KW - machine learning
KW - whole-slide imaging
UR - http://www.scopus.com/inward/record.url?scp=85136823427&partnerID=8YFLogxK
U2 - 10.3390/cancers14153780
DO - 10.3390/cancers14153780
M3 - Review article
AN - SCOPUS:85136823427
SN - 2072-6694
VL - 14
JO - Cancers
JF - Cancers
IS - 15
M1 - 3780
ER -