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...
AIM: The aim of this analysis was to compare a clinical periodontal prognostic system and a developed and externally validated artificial intelligence (AI)-based model for the prediction of tooth loss in periodontitis patients under supportive period...
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...
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.
AIM: The aim of this study was to compare the validity of different machine learning algorithms to develop and validate predictive models for periodontitis.
AIM: To investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone-beam computed tomography.