Research Progress in Digital Pathology: a bibliometric and visual analysis based on Web of Science
Authors: Zhao Jinjin , Han Zhengqi , Ma Yixun , Liu Hongxia , Yang Tiantong 
Published: June 2022

Background:The development of Whole Slide Image (WSI) and deep neural network technologies has contributed to the paradigm shift in diagnostic pathology and has received a lot of attention from researchers, with related publications increasing year by year and "exploding" in recent years. However, few studies have systematically reviewed "digital pathology" using bibliometric tools. In this study, we will use multiple approaches to visualize and analyze "digital pathology" in order to provide a comprehensive and objective picture of the historical evolution and future development of the field.Methods: We use the term "subject=((“Artificial Intelligence” OR AI OR “Computational Intelligence” OR “Machine Intelligence” OR “Computer Vision” OR “Machine Learning” OR “Deep Learning” OR “Neural Network*” OR “Neural Net*” OR “Computer Assisted” OR “Artificial Intelligence Assisted” OR ”AI Assisted” OR “Digital Microscopy”) AND (*patholog* OR Pathomics OR“Whole Slide Imag*” OR WSI*)) OR “Digital *patholog*” OR “Computational *patholog*”, document type = ARTICLE OR PROCEEDINGS PAPER OR REVIEW, LANGUAGE=English, YEAR=2001 to 2021" was used to search the Web of Science core database and download the results. VOSviewer, CiteSpace, Gephi, and R were used to analyze the authors, institutional and national collaboration networks, keyword co-occurrence, and co-citation analysis to visualize the current status of global digital pathology (DP) research.Results: DP-related research is active in molecular, biological, and immunology journal groups, pharmaceutical, medical, and clinical journal groups, and psychology, education, and health journal groups, and other citation groups; in addition to "digital pathology," "diagnosis," "deep learning," "histopathology," and "surgical pathology" are also active research topics. In addition to "digital pathology," "diagnosis," "deep learning," "histopathology," and "surgical pathology" are also active research topics; the U.S. has significant research results in the field of DP, with the top 10 publishing institutions all coming from the U.S. In the past two decades, global DP-related research can be divided into two major research areas. One is about the system verification and optimization of WSI, and the other is about the application and development of artificial intelligence technology in the field of DP. Among them, based on the development of computer technology and the update of machine learning concept, the research results for deep neural network technology have been more concentrated in recent years. The powerful performance of deep neural networks in feature extraction and image analysis provides a new research direction for the improvement of digital pathology-aided diagnosis system, which is where the research hotspots are in recent years.Conclusion: The bibliometric analysis may help to better understand the current status of research within the field of DP and provide references and lessons for future related research.