Aim: To develop a periodontal disease prediction software and a patient-based gingival recession simulator for clinical practice aiming at improving oral hygiene motivation of patients with periodontal problems.
Materials and Methods: Periodontal Disease Prediction (PDP) software has three components: a) Data Loading Window (DLW) b) Three-Dimensional Mouth Model (3DM) and c) Periodontal Attachment Loss Indicator (PLI). Demographic and clinical examinations of 1057 volunteers were recorded to DLW. An unsupervised machine learning K means clustering analysis was used to categorize the data obtained from the study population and identified the periodontal risk groups. An intraoral scanner was utilized to capture direct optical intraoral data of a patient and transferred to the 3DM. The intraoral model went under two algorithm steps for obtaining a recessed model. First, gingival curves separating gingiva and tooth were extracted using a Dijkstra’s algorithm. Limit curves determining boundaries of recessed regions in the intraoral model were then obtained using gingival curves.
Results: Study participants were divided into three different periodontal risk categories defined as low risk (n=462), medium risk (n=336) and high risk (n=259). Gingival curves separating gingiva and tooth were extracted, and recessed models were obtained given inputs for the expected amount of recession via the proposed method. Furthermore, the user can also demonstrate the gingival recession gradually via the slider method attached to the developed programme.
Conclusions: User-friendly computer-based periodontal risk estimation tool and patient-specific gingival recession simulator was developed and presented for clinical usage of dentists.