Research Progress on the Integration of Artificial Intelligence and Periodontitis Risk Prediction: A Review
Periodontitis is a chronic inflammatory disorder with multiple contributing factors, and conventional risk assessment remains limited in early prediction and personalized management. Artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), permits the integration of clinical, imaging, laboratory, microbiome, and genomic information for risk stratification. This review outlines the technical basis of AI-assisted periodontitis prediction, covering ML algorithms, deep-learning radiomics, multidimensional data integration, feature engineering, preprocessing, model training, validation, interpretability, and clinical translation. Available evidence shows encouraging predictive performance; however, data heterogeneity, inadequate external and prospective validation, privacy, fairness, and workflow integration continue to impede wider application.
[1] Thornton-Evans G, Eke P, Wei L, et al., 2013, Periodontitis Among Adults Aged ≥30 Years—United States, 2009–2010. MMWR Supplements, 62(3): 129–135.
[2] Shimpi N, McRoy S, Zhao H, et al., 2020, Development of a Periodontitis Risk Assessment Model for Primary Care Providers in an Interdisciplinary Setting. Technology and Health Care, 28(2): 143–154.
[3] LaMonte M, Hovey K, Millen A, et al., 2014, Accuracy of Self-Reported Periodontal Disease in the Women’s Health Initiative Observational Study. Journal of Periodontology, 85(8): 1006–1018.
[4] Deng K, Zonta F, Yang H, et al., 2024, Development of a Machine Learning Multiclass Screening Tool for Periodontal Health Status Based on Non-Clinical Parameters and Salivary Biomarkers. Journal of Clinical Periodontology, 51(12): 1547–1560.
[5] Atieh M, Shah M, Hakam A, et al., 2026, Diagnostic Accuracy of Artificial Intelligence-Based Deep Learning Models in Detecting Furcation Involvement: A Systematic Review and Meta-Analysis. Journal of Periodontology, 97(7): 1559–1574.
[6] Cheng Y, Yang H, Mo H, et al., 2026, Machine Learning-Based Transcriptomic Diagnosis of Periodontitis. International Dental Journal, 76(1): 104028.
[7] You F, Lin P, Huang C, et al., 2025, Artificial Intelligence with Counseling on the Treatment Outcomes and Quality of Life in Periodontitis Patients. Journal of Periodontology, 96(7): 781–793.
[8] Chatzopoulos G, Wolff L, 2026, Unsupervised Phenotyping of the Periodontal Architecture Through High-Dimensional Clustering of Electronic Health Records: A Multicenter Study. Journal of Periodontology, 97: 1746–1757.
[9] Wang H, Tian S, Bai S, et al., 2025, Associations Between Thyroid Function and Periodontitis: A Machine Learning Approach Using NHANES. International Dental Journal, 75(5): 100921.
[10] Kurt-Bayrakdar S, Bayrakdar I, Yavuz M, et al., 2024, Detection of Periodontal Bone Loss Patterns and Furcation Defects from Panoramic Radiographs Using Deep Learning Algorithm: A Retrospective Study. BMC Oral Health, 24(1): 155.
[11] Vigil M, Gowri V, Ramesh S, et al., 2024, ADGRU: Adaptive DenseNet with Gated Recurrent Unit for Automatic Diagnosis of Periodontal Bone Loss and Stage Periodontitis with Tooth Segmentation Mechanism. Clinical Oral Investigations, 28(11): 614.
[12] Alotaibi G, Awawdeh M, Farook F, et al., 2022, Artificial Intelligence (AI) Diagnostic Tools: Utilizing a Convolutional Neural Network (CNN) to Assess Periodontal Bone Level Radiographically—A Retrospective Study. BMC Oral Health, 22(1): 399.
[13] Jundaeng J, Chamchong R, Nithikathkul C, 2024, Artificial Intelligence-Powered Innovations in Periodontal Diagnosis: A New Era in Dental Healthcare. Frontiers in Medical Technology, 6: 1469852.
[14] Kurt-Bayrakdar S, Bayrakdar I, Kuran A, et al., 2025, Advancing Periodontal Diagnosis: Harnessing Advanced Artificial Intelligence for Patterns of Periodontal Bone Loss in Cone-Beam Computed Tomography. Dentomaxillofacial Radiology, 54(4): 268–278.
[15] Bardhan S, Nagar G, Adapala K, et al., 2025, Validation of an AI Model for Automated Detection of Alveolar Bone Changes Post-Orthodontics Using Cone-Beam Computed Tomography. Cureus, 17(10): e94809.
[16] Erturk M, Öziç M, Tassoker M, 2025, Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-Wing Radiographs: An Eigen-CAM Explainability Mapping Approach. Journal of Imaging Informatics in Medicine, 38(1): 556–575.
[17] Nagarajan R, Miller C, Ebersole J, 2019, Biologic Modelling of Periodontal Disease Progression. Journal of Clinical Periodontology, 46(2): 160–169.
[18] Chatzopoulos G, Wolff L, 2026, A Predictive Model for Significant Periodontal Disease Progression: A Large-Scale Cohort Study. Medicina Oral, Patologia Oral y Cirugia Bucal, 31(2): e243–e250.
[19] Yu C, Tao D, Cai M, et al., 2024, Analysis of Factors Influencing the Prognosis of Periodontal Disease Treated with Host Modulation and the Development of a Predictive Model. Alternative Therapies in Health and Medicine, 30(5): 202–208.
[20] Chen W, Chang S, Tang C, et al., 2018, Composition Analysis and Feature Selection of the Oral Microbiota Associated with Periodontal Disease. BioMed Research International, 2018: 3130607.
[21] Lee J, Kim H, Kim E, et al., 2026, Prediction Model for Periodontitis Stage Based on the Salivary Microbiome. mSystems, 11(4): e0110325.
[22] Tomás I, Regueira-Iglesias A, López M, et al., 2017, Quantification by qPCR of Pathobionts in Chronic Periodontitis: Development of Predictive Models of Disease Severity at Site-Specific Level. Frontiers in Microbiology, 8: 1443.
[23] Zheng X, Fu W, Li C, et al., 2026, Development and Validation of Machine Learning-Based Risk Prediction Models of Coronary Artery Disease Using Porphyromonas gingivalis Concentration. BMC Oral Health, 26(1): 556.
[24] Zhu Z, Wu X, Zhu L, et al., 2025, Development of a Machine Learning Tool for Home-Based Assessment of Periodontitis. medRxiv, 2025.03.10.25323689.
[25] Tichy A, Werner N, Dujic H, et al., 2026, Artificial Intelligence in Periodontology: A Systematic Review. Journal of Periodontal Research, 61(6): 639–661.
[26] Teza H, Pattanateepapon A, Lertpimonchai A, et al., 2023, Development of Risk Prediction Models for Severe Periodontitis in a Thai Population: Statistical and Machine Learning Approaches. JMIR Formative Research, 7: e48351.
[27] Rebeiz T, Lawand G, Martin W, et al., 2025, Development of an Artificial Intelligence Model for Assisting Periodontal Therapy Decision-Making: A Retrospective Longitudinal Cohort Study. Journal of Dentistry, 159: 105780.
[28] Shi S, Meng Y, Jiao J, et al., 2024, A Nomogram-Based Predictive Model for Tooth Survival in Chinese Patients with Periodontitis: An 11-Year Retrospective Cohort Study. Journal of Clinical Periodontology, 51(10): 1384–1394.
[29] Lü C, Ge S, 2025, Applications and Perspectives of Artificial Intelligence in Periodontology. Hua Xi Kou Qiang Yi Xue Za Zhi = Huaxi Kouqiang Yixue Zazhi = West China Journal of Stomatology, 43(5): 620–627.
[30] Teza H, Pattanateepapon A, Lertpimonchai A, et al., 2023, Development of Risk Prediction Models for Severe Periodontitis in a Thai Population: Statistical and Machine Learning Approaches. JMIR Formative Research, 7: e48351.
[31] Patel J, Tellez M, Katiyar R, et al., 2026, Periodontitis Prediction Model Using Linked Electronic Health and Dental Records. JDR Clinical and Translational Research, 2026: 23800844251408849.
