Colorectal cancer (CRC) ranks among the top causes of cancer-related fatalities globally. Recent progress in genomics, proteomics, and bioinformatics has greatly improved our comprehension of the molecular underpinnings of CRC, paving the way for targeted therapies and immunotherapies. Nonetheless, obstacles such as tumor heterogeneity and drug resistance persist, hindering advancements in treatment efficacy. In this context, the integration of artificial intelligence (AI) and organoid technology presents promising new avenues. AI can analyze genetic and clinical data to forecast disease risk, prognosis, and treatment responses, thereby expediting drug development and tailoring treatment plans. Organoids replicate the genetic traits and biological behaviors of tumors, acting as platforms for drug testing and the formulation of personalized treatment approaches. Despite notable strides in CRC research and treatment - from genetic insights to therapeutic innovations - numerous challenges endure, including the intricate tumor microenvironment, tumor heterogeneity, adverse effects of immunotherapies, issues related to AI data quality and privacy, and the need for standardization in organoid culture. Future initiatives should concentrate on clarifying the pathogenesis of CRC, refining AI algorithms and organoid models, and creating more effective therapeutic strategies to alleviate the global impact of CRC.

• Colorectal cancer
• Molecular
• Treatment
• Artificial intelligence
• Organoid

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.

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.

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.

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.

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.

• Colorectal cancer
• Deep learning
• Artificial intelligence
• Bibliometric analysis
• Visualization

• Biomarkers
• CRC
• Convolutional Neural Networks
• Deep learning
• Histopathology
• Hybrid model
• Neural Architecture Search
• Vision Transformer

• precision medicine
• colorectal cancer
• biomarkers
• immunotherapy
• personalized treatment