Introduction of Artificial Intelligence in Pathology

SangYong Song

doi:10.7599/hmr.2017.37.2.77

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Song: Introduction of Artificial Intelligence in Pathology

Review

Hanyang Medical Reviews 2017; 37(2): 77-85.

Published online: 30 November 2017

DOI: https://doi.org/10.7599/hmr.2017.37.2.77

Introduction of Artificial Intelligence in Pathology

SangYong Song

Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.

Corresponding Author: SangYong. Song, M.D., Ph.D. Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 06351, Republic of Korea. Tel: +82-2-3410-2768, Fax: +82-2-3410-0025, yoda.song@samsung.com

Received 1 August 2017 Revised 6 October 2017 Accepted 7 November 2017

Hanyang University College of Medicine · Institute of Medical Science

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Pathology has a long history of artificial intelligence (AI) as much as any other field of medicine, and has used AI algorithms continuously. However, in Korea, pathology AI is unfamiliar even to the pathologists. In this article, I will summarize the terms and definitions, the basic elements of pathology AI, and the future direction. Digital pathology is a system or environment that digitizes glass slides into binary files, observes them through a monitor or any digital devices, interprets it, analyzes it, and maintains it. Computational pathology is a comprehensive concept of diagnosis support or research system that deals with image, text and omics data. Virtual microscopy is a method or technology that allows pathologists to view and share glass slides images from whole slide scanners. Image analysis is a technique or method that processes various digital images and quantifies features. The basic elements of pathology AI are as follows: environmental factors called digital pathology and technical elements such as AI, machine learning, and deep learning. Digital pathology workflow consists of three elements; acquisition or collection of data, data processing and data storage. The basic process of image analysis consists of preprocessing of image, identification of region of interest, and feature extraction. There is enormous potential for improvement of patient care through digital pathology and/or AI, and a harmonized discussion about activation of Korean digital pathology among government, academia and industry will be mandatory for future medicine and healthcare in Korea.

Keywords: Pathology, Digital Pathology, Artificial Intelligence, Machine Learning, Deep Learning

Figures and Tables

Fig. 1

Schematic illustration of new fields of pathology

Table 1

Summary of whole slide scanner characteristics

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