@article{Song2025, 
author = {Haonan Song and Zhen Chai and Yiru Yu and Zhen Wang and Ang Li},
title = {Data-driven precision diagnosis of periodontal disease: from biological features analysis to clinical applications},
year = {2025},
journal = {Oral Science and Homeostatic Medicine},
volume = {1},
number = {2},
pages = {9610031},
keywords = {data-driven, precision medicine, periodontal diagnosis, omics analysis, artificial intelligence},
url = {https://www.sciopen.com/article/10.26599/OSHM.2025.9610031},
doi = {10.26599/OSHM.2025.9610031},
abstract = {Periodontal disease is a prevalent chronic inflammatory condition, and its heterogeneity and complex pathophysiology pose significant diagnostic challenges. Traditional methods remain limited in precision and early detection. With the advancement of precision medicine, data-driven diagnostics are shifting periodontal assessment from a “one-size-fits-all” model to a more precise approach. Moreover, numerous studies highlight the vast potential of artificial intelligence (AI) in periodontal risk assessment and diagnosis. Integrating AI with data-driven diagnostic models enables deeper analysis, potentially surpassing the limitations of conventional empirical medicine and establishing a new paradigm for precision oral healthcare. This review explores recent advances in data-driven and AI-based strategies for periodontal disease diagnosis, emphasizing the multidimensional integration of biological features analysis and providing new insights into periodontal precision diagnostics.}
}