A visual analysis of machine learning in periodontal disease research, 2001-2023

doi:10.19439/j.sjos.2025.06.019

Abstract

Abstract: PURPOSE: To analyze the literatures related to machine learning in periodontal diseases to understand its applications and research hotspots. METHODS: Literatures related to machine learning in periodontal disease researches included in the Web of Science Core Collection from January 1, 2001 to December 31, 2023 were visualized and analyzed using Bibliometrix 4.4.1, CiteSpace 6.3.R1, and VOSviewer 1.16.18. RESULTS: A total of 127 papers that met the inclusion and exclusion criteria were included, and the number of publications and citation frequency of each year after 2018 showed a rapid upward trend. The high-frequency keywords of related literatures suggested: periodontitis, machine learning, deep learning, and so on. The hotspot of machine learning in periodontal clinical research in the past 2 years was segmentation and feature extraction based on images. The clinical research of machine learning in periodontics focused on combining images to assist in the diagnosis of periodontal disease and peri-implantitis; the basic research of machine learning in periodontics focused on screening biomarkers between periodontal diseases and systemic diseases. CONCLUSIONS: The literature visualization analysis shows the application and hotspots of machine learning in periodontal disease research, which can provide a reference for future research direction.

Key words: Machine learning, Periodontal disease, Bibliometrics, Visual analysis

CLC Number:

R781.4

Lin Huili, Chen Jun, Li Wenjie. A visual analysis of machine learning in periodontal disease research, 2001-2023[J]. Shanghai Journal of Stomatology, 2025, 34(6): 660-667.

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