OBJECTIVES: This study assessed the ability of ChatGPT, an artificial intelligence(AI) language model, to determine the stage, grade, and extent of periodontitis based on the 2018 classification.
BACKGROUND: Acute myocardial infarction (AMI) risk correlates with C-reactive protein (CRP) levels, suggesting systemic inflammation is present well before AMI. Studying different types of periodontal disease (PD), extremely common in individuals at ...
OBJECTIVES: This study focuses on artificial intelligence (AI)-assisted analysis of alveolar bone for periodontitis in a mouse model with the aim to create an automatic deep-learning segmentation model that enables researchers to easily examine alveo...
AIM: Treatment of periodontitis, a chronic inflammatory disease driven by biofilm dysbiosis, remains challenging due to patients' poor performance and adherence to the necessary oral hygiene procedures. Novel, artificial intelligence-enabled multimod...
JDR clinical and translational research
Apr 8, 2024
INTRODUCTION: Periodontitis is the main cause of tooth loss and is related to many systemic diseases. Artificial intelligence (AI) in periodontics has the potential to improve the accuracy of risk assessment and provide personalized treatment plannin...
OBJECTIVES: This study aimed to identify predictors associated with the tooth loss phenotype in a large periodontitis patient cohort in the university setting.
OBJECTIVES: We aim to develop a deep learning model based on a convolutional neural network (CNN) combined with a classification algorithm (CA) to assist dentists in quickly and accurately diagnosing the stage of periodontitis.
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.
AIM: To evaluate if, and to what extent, machine learning models can capture clinically defined Stage III/IV periodontitis from self-report questionnaires and demographic data.
AIM: To develop and validate models based on logistic regression and artificial intelligence for prognostic prediction of molar survival in periodontally affected patients.
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