{"title":"Patient-specific gingival recession system based on periodontal disease prediction.","authors":"Sadiye Gunpinar, Ayse Sinem Sevinc, Zeynep Akgül, A Alper Tasmektepligil, Erkan Gunpinar","doi":"10.3290/j.ijcd.b4784721","DOIUrl":null,"url":null,"abstract":"<p><strong>Aim: </strong>To develop a periodontal disease prediction (PDP) software program and a patient-based gingival recession simulator for clinical practice with the aim of improving the oral hygiene motivation of patients with periodontal problems.</p><p><strong>Materials and methods: </strong>The developed PDP software has three components: a) A data loading window (DLW), b) A three-dimensional mouth model (3DM), and c) a periodontal attachment loss indicator (PLI). The demographic and clinical examination details of 1057 volunteers were recorded to the DLW. An unsupervised machine learning K means clustering analysis was used to categorize the data obtained from the study population and to identify the periodontal risk groups. An intraoral scanner was utilized to capture the direct optical intraoral data of the patients, which was transferred to the 3DM. The intraoral model underwent two algorithm steps to obtain a recessed model: First, the gingival curves separating the gingiva and tooth were extracted using a Dijkstra's algorithm. Then, the limit curves determining the boundaries of the recessed regions in the intraoral model were obtained using the gingival curves.</p><p><strong>Results: </strong>Study participants were divided into three different periodontal risk categories: low- (n = 462), medium- (n = 336), and high-risk (n = 259) groups. The gingival curves separating the gingiva and tooth were extracted, and recessed models were obtained and given inputs for the expected amount of recession via the here-proposed method/algorithm. Furthermore, the user can also demonstrate the gingival recession gradually via the slider method incorporated into the developed program.</p><p><strong>Conclusions: </strong>A user-friendly computer-based periodontal risk estimation tool that is also a patient-specific gingival recession simulator was developed and presented for clinical use by dentists.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"35-45"},"PeriodicalIF":1.8000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Computerized Dentistry","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3290/j.ijcd.b4784721","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"DENTISTRY, ORAL SURGERY & MEDICINE","Score":null,"Total":0}
引用次数: 0
Abstract
Aim: To develop a periodontal disease prediction (PDP) software program and a patient-based gingival recession simulator for clinical practice with the aim of improving the oral hygiene motivation of patients with periodontal problems.
Materials and methods: The developed PDP software has three components: a) A data loading window (DLW), b) A three-dimensional mouth model (3DM), and c) a periodontal attachment loss indicator (PLI). The demographic and clinical examination details of 1057 volunteers were recorded to the DLW. An unsupervised machine learning K means clustering analysis was used to categorize the data obtained from the study population and to identify the periodontal risk groups. An intraoral scanner was utilized to capture the direct optical intraoral data of the patients, which was transferred to the 3DM. The intraoral model underwent two algorithm steps to obtain a recessed model: First, the gingival curves separating the gingiva and tooth were extracted using a Dijkstra's algorithm. Then, the limit curves determining the boundaries of the recessed regions in the intraoral model were obtained using the gingival curves.
Results: Study participants were divided into three different periodontal risk categories: low- (n = 462), medium- (n = 336), and high-risk (n = 259) groups. The gingival curves separating the gingiva and tooth were extracted, and recessed models were obtained and given inputs for the expected amount of recession via the here-proposed method/algorithm. Furthermore, the user can also demonstrate the gingival recession gradually via the slider method incorporated into the developed program.
Conclusions: A user-friendly computer-based periodontal risk estimation tool that is also a patient-specific gingival recession simulator was developed and presented for clinical use by dentists.
期刊介绍:
This journal explores the myriad innovations in the emerging field of computerized dentistry and how to integrate them into clinical practice. The bulk of the journal is devoted to the science of computer-assisted dentistry, with research articles and clinical reports on all aspects of computer-based diagnostic and therapeutic applications, with special emphasis placed on CAD/CAM and image-processing systems. Articles also address the use of computer-based communication to support patient care, assess the quality of care, and enhance clinical decision making. The journal is presented in a bilingual format, with each issue offering three types of articles: science-based, application-based, and national society reports.