Abstract
BACKGROUND: Colorectal cancer (CRC) is the third-most prevalent cancer and the cancer with the second-highest mortality rate worldwide, representing a high public health burden. Deep learning (DL) offers advantages in the diagnosis, identification, localization, classification and prognosis of CRC patients. However, few bibliometric analyses of research hotspots and trends in the field have been performed. AIM: To use bibliometric approaches to analyze and visualize the current research state and development trend of DL in CRC as well as to anticipate future research directions and hotspots. METHODS: Datasets were retrieved from the Web of Science Core Collection for the period January 2011 to December 2023. Scimago Graphica (1.0.45), VOSviewer (1.6.20) and CiteSpace (6.3.1) were used to analyze and visualize the nation, institution, journal, author, reference and keyword indicators. Origin (2022) was utilized for plotting, and Excel (2021) was used to construct the tables. RESULTS: A total of 1275 publications in 538 journals from 74 countries and 2267 institutions were collected. The number of annual publications has increased over time. China (371, 29.1%), the United States (265, 20.8%) and Japan (155, 12.2%) contributed significantly to the number of articles published, accounting for 62.1% of the total publications. The United States had the strongest ties to other nations. Sun Yat-sen University in China had the highest number of publications (32). The journal with the most publications was Scientific Reports (34, Q2), whereas Gastrointestinal Endoscopy had the most co-citations (1053, Q1). Kather JN, was the author with the most articles (12) and co-citations (287). The most frequently cited reference was Deep Residual Learning for Image Recognition. Keywords were divided into six clusters, with "colorectal cancer" (12.34) having the highest outbreak intensity. CONCLUSION: This study highlights the current status and most active directions of the use of DL in CRC. This approach has important applications in the identification, diagnosis, localization, classification and prognosis of the disease and will remain a central focus in the future.