Digital Pathology and Artificial Intelligence Applications in Pathology

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, 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.

Revolutionizing Breast Cancer Diagnosis: A Concatenated Precision through Transfer Learning in Histopathological Data Analysis

“…The advent of digital pathology has ushered in a transformative era, revolutionizing the entire diagnostic workflow in pathology [5]. This technological shift has facilitated the application and advancement of AI models in pathology, leading to the generation of extensive pathological big data and the implementation of telepathology.…”

“…These pathological AI algorithms find valuable applications in diagnostic screening, conducting morphometric analyses of biomarkers, uncovering new prognostic and therapeutic insights within pathological images, and enhancing diagnostic efficiency. This review delved into the advantages and prospects offered by digital pathology, explored AI-based approaches, examined their diverse applications in pathology, and addressed the crucial considerations and challenges entailed in the development of pathological AI models [5].…”

Robust autofocus method based on patterned active illumination and image cross-correlation analysis

“…Examination of pathological images is the golden standard for diagnosing, but the scarcity and uneven distribution of pathologists limit the diagnostic efficiency [ 1 ]. Therefore, the development of an intelligent auxiliary diagnosis system to improve the efficiency of pathological diagnosis has heavy scientific and medical needs [ 2 – 4 ]. However, the performance of an intelligent diagnosis system is highly dependent on related to the quality of WSI images [ 5 ].…”

Computational Pathology: A Survey Review and The Way Forward

“…The first focuses on the design and applications of smart diagnosis tools. 15 , 16 , 17 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 These works focus on designing novel architectures for artificial intelligence (AI) models with regards to specific clinical tasks, although they may briefly discuss clinical challenges and limitations. A second group of works focus on clinical barriers for AI integration discussing specific certifications and regulations required for the development of medical devices under clinical settings.…”