Open AccessDigital Pathology and Artificial Intelligence Applications in Pathology
Author(s) -
Heounjeong Go
Publication year - 2022
Publication title -
brain tumor research and treatment
Language(s) - English
Resource type - Journals
eISSN - 2288-2413
pISSN - 2288-2405
DOI - 10.14791/btrt.2021.0032
Subject(s) - digital pathology , telepathology , artificial intelligence , workflow , computer science , standardization , pathological , digital image analysis , medicine , big data , pathology , segmentation , deep learning , pattern recognition (psychology) , data mining , computer vision , telemedicine , health care , database , economics , economic growth , operating system
Digital pathology is revolutionizing pathology. The introduction of digital pathology made it possible to comprehensively change the pathology diagnosis workflow, apply and develop pathological artificial intelligence (AI) models, generate pathological big data, and perform telepathology. AI algorithms, including machine learning and deep learning, are used for the detection, segmentation, registration, processing, and classification of digitized pathological images. Pathological AI algorithms can be helpfully utilized for diagnostic screening, morphometric analysis of biomarkers, the discovery of new meanings of prognosis and therapeutic response in pathological images, and improvement of diagnostic efficiency. In order to develop a successful pathological AI model, it is necessary to consider the selection of a suitable type of image for a subject, utilization of big data repositories, the setting of an effective annotation strategy, image standardization, and color normalization. This review will elaborate on the advantages and perspectives of digital pathology, AI-based approaches, the applications in pathology, and considerations and challenges in the development of pathological AI models.