|
KMID : 1035520220100020076
|
|
Brain Tumor Research and Treatment : BTRT
2022 Volume.10 No. 2 p.76 ~ p.82
|
|
|
Digital Pathology and Artificial Intelligence Applications in Pathology
|
|
Go Heoun-Jeong
|
|
|
Abstract
|
|
|
|
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, in- cluding machine learning and deep learning, are used for the detection, segmentation, registration, processing, and classification of digitized pathological images. Pathological AI algorithms can be help- fully utilized for diagnostic screening, morphometric analysis of biomarkers, the discovery of new meanings of prognosis and therapeutic response in pathological images, and improvement of diag- nostic 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 pa- thology, and considerations and challenges in the development of pathological AI models.
|
|
|
KEYWORD
|
|
|
Pathology, Digital technology, Workflow, Artificial intelligence, Deep learning
|
|
|
FullTexts / Linksout information
|
|
|
|
|
|
Listed journal information
|
|
 
|
|