AIMC Topic: Periodontal Diseases
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Development of a machine learning multiclass screening tool for periodontal health status based on non-clinical parameters and salivary biomarkers.
Journal of clinical periodontology
Sep 11, 2023
AIM: To develop a multiclass non-clinical screening tool for periodontal disease and assess its accuracy for differentiating periodontal health, gingivitis and different stages of periodontitis.
A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level.
Journal of dentistry
Jun 26, 2022
OBJECTIVES: Bone level as measured by clinical attachment levels (CAL) are critical findings that determine the diagnosis of periodontal disease. Deep learning algorithms are being used to determine CAL which aid in the diagnosis of periodontal disea...
Evaluation of the Effect of Comprehensive Nursing Interventions on Plaque Control in Patients with Periodontal Disease in the Context of Artificial Intelligence.
Journal of healthcare engineering
Mar 23, 2022
Plaque is a bacterial biofilm that adheres to each other and exists on the tooth surface, and new plaque can continuously reform after removing it from the tooth surface. The pathogenesis of periodontal disease is related to the bacteria, the host an...
Emerging Technologies and Algorithms for Periodontal Screening and Risk of Disease Progression in Non-Dental Settings: A Scoping Review.
Journal of clinical periodontology
Aug 1, 2025
AIM: To evaluate different tools to screen for periodontal diseases and/or evaluate the risk for disease progression in non-dental clinical settings.
Consensus Report of the 20th European Workshop on Periodontology: Contemporary and Emerging Technologies in Periodontal Diagnosis.
Journal of clinical periodontology
Aug 1, 2025
BACKGROUND: This Consensus Workshop dealt with diagnostic methodologies in the context of surveillance, screening, assessment of stage and grade, prognosis, monitoring and prediction of periodontal status. Several elements provided the impetus for th...
Advancing periodontal diagnosis: harnessing advanced artificial intelligence for patterns of periodontal bone loss in cone-beam computed tomography.
Dento maxillo facial radiology
May 1, 2025
OBJECTIVES: The current study aimed to automatically detect tooth presence, tooth numbering, and types of periodontal bone defects from cone-beam CT (CBCT) images using a segmentation method with an advanced artificial intelligence (AI) algorithm.
[Research progress in the application of virtual artificial intelligence in risk assessment and diagnosis of periodontal disease].
Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
Nov 9, 2024
Periodontal disease is a common and frequently-occurring disease in China. Early detection, diagnosis, and treatment of periodontal disease are of great significance for achieving universal oral health and even systemic health. Artificial intelligenc...
Enhancing Periodontal Treatment Through the Integration of Deep Learning-Based Detection with Bayesian Network Models.
Studies in health technology and informatics
Aug 22, 2024
This study incorporated deep learning for periodontal disease detection into a Bayesian network (BN) clinical decision support model for comprehensive periodontal care. BN structure and probabilities were based on clinical data and Faster R-CNN-detec...
Automated Process Incorporating Machine Learning Segmentation and Correlation of Oral Diseases with Systemic Health.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Jul 1, 2019
Imaging fluorescent disease biomarkers in tissues and skin is a non-invasive method to screen for health conditions. We report an automated process that combines intraoral fluorescent porphyrin biomarker imaging, clinical examinations and machine lea...
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