International Journal of Computerized Dentistry最新文献

{"title":"Effect of Thermal Aging on Glass Matrix Ceramic and Resin Matrix Ceramic CAD/CAM Blocks: Flexural Strength and Vicker's Microhardness Assessment.","authors":"Mustafa Çopun, Bengü Doğu Kaya, Pınar Yılmaz Atalı, Erkut Kahramanoglu","doi":"10.3290/j.ijcd.b6329245","DOIUrl":"https://doi.org/10.3290/j.ijcd.b6329245","url":null,"abstract":"<p><strong>Background: </strong>This study aimed to evaluate and compare the flexural strength, elastic modulus, and Vicker's microhardness values of glass matrix ceramic and resin matrix ceramic CAD/CAM blocks, and to examine the differences after thermal aging.</p><p><strong>Methods: </strong>This in vitro study involved seven different CAD/CAM blocks: Amber Mill (Hassbio, Korea), LiSi (GC, Japan), Celtra Duo (Dentsply Sirona, Germany), Tessera (Dentsply Sirona, Germany), IPS e.max CAD (Ivoclar, Liechtenstein), Cerasmart (GC, Japan), and Grandio Blocs (VOCO, Germany). Bar-shaped samples (1.2x4x14mm) for the three-point bending test and rectangular samples (2mm-thickness) for the Vicker's microhardness test were prepared using a precision cutting device under water. Half of the samples underwent 50,000 thermal cycles between 5-55°C, while the other half were kept in distilled water at 37°C for 24 hours. Flexural strength/FS and elastic modulus/EM values were measured using a universal testing machine, and Vicker's microhardness/VHN values were measured using a microhardness tester. Statistical analyses were performed using Kruskal-Wallis and Mann- Whitney U tests, with significance set at p<0.05.</p><p><strong>Results: </strong>Tessera showed the highest median values for FS (570 MPa), EM (66.2 GPa), and VHN (717.7) after 24 hours, whereas Cerasmart had the lowest values (FS: 260 MPa; EM: 12.8 GPa; VHN: 99.9). Significant differences were found between the materials' FS, EM, and VHN values after 24 hours and after thermal aging (p<0.001). All materials showed a significant decrease in FS values following thermal aging (p<0.05).</p><p><strong>Conclusion: </strong>In the selection of CAD/CAM materials, it is critical to consider that mechanical properties vary with structure and tend to change over time due to aging.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"0"},"PeriodicalIF":1.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486731","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accuracy and Efficiency of Artificial Intelligence and Manual Virtual Segmentation for Generation of 3D Printed Tooth Replicas.","authors":"Ignacio Pedrinaci, Anita Nasseri, Javier Calatrava, Emilio Couso-Queiruga, William V Giannobile, German O Gallucci, Mariano Sanz","doi":"10.3290/j.ijcd.b6329209","DOIUrl":"https://doi.org/10.3290/j.ijcd.b6329209","url":null,"abstract":"<p><strong>Aims: </strong>The primary aim of this in vitro study was to compare methods for generating 3D-printed replicas through virtual segmentation, utilizing artificial intelligence (AI) or manual processes, by assessing accuracy in terms of volumetric and linear discrepancies. The secondary aims were the assessment of time efficiency with both segmentation methods, and the effect of post-processing on 3D-printed replicas.</p><p><strong>Methods: </strong>Thirty teeth were scanned through Cone Beam Computed Tomography (CBCT), capturing the region of interest from human subjects. DICOM files underwent virtual segmentation through both AI and manual methods. Replicas were fabricated with a stereolithography 3D printer. After surface scanning of pre-processed replicas and extracted teeth, STL files were superimposed to compare linear and volumetric differences using the extracted teeth as the reference. Post-processed replicas were scanned to assess the effect of post-processing on linear and volumetric changes.</p><p><strong>Results: </strong>AI-driven segmentation resulted in statistically significant mean linear and volumetric differences of -0.709mm (SD 0.491, P< 0.001) and -4.70%, respectively. Manual segmentation showed no statistically significant differences in mean linear, -0.463mm (SD 0.335, P<0.001) and volumetric (-1.20%) measures. Comparing manual and AI-driven segmentations, AI-driven segmentation displayed mean linear and volumetric differences of -0.329mm (SD 0.566, p=0.003) and -2.23%, respectively. Additionally, AI segmentation reduced the mean time by 21.8 minutes. When comparing post-processed to pre-processed replicas, there was a volumetric reduction of -4.53% and a mean linear difference of -0.151mm (SD 0.564, p=0.042).</p><p><strong>Conclusion: </strong>Both segmentation methods achieved acceptable accuracy, with manual segmentation slightly more accurate but AI-driven segmentation more time-efficient. Continuous improvement in AI offers the potential for increased accuracy, efficiency, and broader application in the future.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"0"},"PeriodicalIF":1.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dynamic Navigation System Accuracy in Orthodontic Miniscrew Insertion in the Palatine Vault: A Prospective Single- Arm Clinical Study.","authors":"Davide Brilli, Matteo Giansanti, Serena Bertoldo, Isabella Cauli, Michele Cassetta","doi":"10.3290/j.ijcd.b6329377","DOIUrl":"https://doi.org/10.3290/j.ijcd.b6329377","url":null,"abstract":"<p><strong>Aim: </strong>The aim of this study was to evaluate the accuracy of Dynamic Navigation (DN) in miniscrew insertion in the palatine vault by comparing the deviation between planned and placed miniscrew positions. The secondary outcome was to evaluate the correlation between the number of miniscrews inserted and both the accuracy variables and the duration of interventions.</p><p><strong>Materials and methods: </strong>Twenty-three subjects were included in the study and each had 2 miniscrews inserted using a DN system (46 miniscrews inserted overall). Pre-operative STL and DICOM files were matched to planned miniscrews insertion and a post-operative CBCT was performed immediately afterwards to measure the deviations between the planned and placed miniscrews, evaluating four variables: Entry-3D, Apex-3D, Apex-vertical and angular deviation. The duration of intervention was also calculated. Descriptive statistics and Pearson correlation coefficient were performed; P-value was set at ≤ 0.05.</p><p><strong>Results: </strong>The mean Entry-3D deviation was 2.55 mm; the mean Apex-3D deviation was 3.11 mm; the mean Apex-point deviation was 0.87 mm; the mean angular deviation was 8.50°. The mean duration of the intervention was 2:75 min:sec. A statistically significant correlation was found only between the number of surgeries and the duration of the interventions, which decreased over time.</p><p><strong>Conclusion: </strong>It is possible to conclude that DN is a viable alternative to miniscrew insertion in the palatal vault even if there is a wide deviation range. There is no significant correlation between the accuracy variables and the number of surgeries. In contrast, the duration of the interventions shortens over time.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"0"},"PeriodicalIF":1.8,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of the effectiveness of intraoral scanning in patients with maxillary defects: a clinical study.","authors":"Deger Ongul, Bilge Gokcen-Rohlig","doi":"10.3290/j.ijcd.b6198699","DOIUrl":"https://doi.org/10.3290/j.ijcd.b6198699","url":null,"abstract":"<p><strong>Aim: </strong>To evaluate the accuracy of the intraoral scanner (IOS) for obtaining digital scans of maxillary defects and adjacent tissues to fabricate obturator prosthesis.</p><p><strong>Material and methods: </strong>Ten randomly selected patients who required an obturator prosthesis after maxillary resection underwent both conventional (CI) and digital (DI) impressions. CI was obtained using traditional methods, and cast models were prepared. The cast models were digitized using a reference scanner in standard tessellation language (STL) format. For Group DI, an intraoral scanner (Trios 5; 3Shape TRIOS®Copenhagen, Denmark) was used for digital scans and the virtual models were created. The data of both groups were analysed by superimposing predetermined points using unreversed engineering software (Geomagic GmbH, 3D Systems, Rock Hill, USA).</p><p><strong>Results: </strong>No significant deviations from normality (P > 0.05) were found in the predetermined points of the residual hard tissue in the STLs. No interaction was observed between the hard tissue components and the impression techniques used. However, accuracy significantly varied between overlapped points in defect parts across the two impression methods (P= 0 .028).</p><p><strong>Conclusion: </strong>The evaluation of IOS for digitally scanning maxillary defects shows that they are less effective than CI methods, performing adequately with shallow defects but facing challenges with deeper cavities. It is essential to advance these techniques and conduct clinical studies with larger patient groups to gain broader acceptance of DI methods for routine clinical use, particularly for deep defects.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"0"},"PeriodicalIF":1.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143992146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial Intelligence in Panoramic Radiography Interpretation: A Glimpse into the State-of-the-Art Radiologic Examination Method.","authors":"Ibrahim Sevki Bayrakdar, Elif Bilgir, Alican Kuran, Ozer Celik, Kaan Orhan","doi":"10.3290/j.ijcd.b6173229","DOIUrl":"https://doi.org/10.3290/j.ijcd.b6173229","url":null,"abstract":"<p><strong>Aim: </strong>Panoramic radiography is a frequently utilized imaging technique in standard dental examinations and provides many advantages. In this context, studies have been conducted to develop tools to assist physicians in clinical practice by using deep learning models to interpret panoramic radiography images. However, studies in the existing literature have generally addressed these conditions separately and studies that develop a multiclass diagnostic charting model that can detect and segment all these conditions are very limited. Therefore, the aim of this study to develop a deep learning model that can accurately evaluate and segment various dental issues and anatomical structures in panoramic radiographs obtained from different radiography devices and settings.</p><p><strong>Materials and methods: </strong>Panoramic radiographs were labelled for 33 different conditions in the categories of dental problems, dental restorations, dental implants, anatomical landmarks, periodontal conditions, jaw pathologies and periapical lesions. A YOLO-v8 model was employed to develop an artificial intelligence model for each labelling. A confusion matrix was utilised to successfully evaluate the developed models.</p><p><strong>Results: </strong>The algorithm achieved a precision value of 0.99-1 in accurately detecting various dental features, such as adult tooth numbering, filling, dental implants, dental pulp, root canal filling, mandibular canal, mandibular condyle, mandible, and pharyngeal airway. With respect to sensitivity, the adult tooth numbering, dental implants, mandibular canal, maxillary sinus, mandibular condyle, angulus mandible, nasal septum, mandible, and hard palate showed the highest values of 0.99-1. The F1-score reached the highest value of 0.99-1 for the root canal filling, adult tooth numbering, dental implants, mandibular canal, mandibular condyle, angulus mandible, mandible, and pharyngeal airway.</p><p><strong>Conclusion: </strong>Artificial intelligence based on convolutional neural networks has a remarkable ability to detect different conditions observed in regular clinical evaluations in panoramic radiographs, displaying excellent performance. Based on these findings, it can be confidently stated that deep learning-based models has great potential to improve routine clinical practices for physicians.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"0"},"PeriodicalIF":1.8,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preparation Design and Milling Unit Impact on CAD/CAM Endocrown Fit.","authors":"Henry B Heeter, Gisele De Faria Neiva, Neville J McDonald, Dennis J Fasbinder","doi":"10.3290/j.ijcd.b6173215","DOIUrl":"https://doi.org/10.3290/j.ijcd.b6173215","url":null,"abstract":"<p><strong>Aim: </strong>The study evaluated the impact of tooth preparation design and chairside milling unit on the internal adaptation and marginal fit of CAD/CAM fabricated endocrowns.</p><p><strong>Materials and methods: </strong>Endodontically treated molars (N=60) from a clinical research project were divided into four groups. 30 molars received an onlay endocrown preparation and 30 molars received a shoulder endocrown preparation. The preparations were scanned and designed with the Primescan (Dentsply Sirona) and 15 of each group were milled with the MC X mill (Dentsply Sirona) or Primemill (Dentsply Sirona) to create four clinical groups (N=15). One in vitro group was added using the shoulder endocrown preparation and Primemill (N=15). Polyvinylsiloxane (PVS) replicas were fabricated and sectioned. Specimens were measured under a microscope at the margins and pre-determined segments of the internal surface.</p><p><strong>Results: </strong>No significant differences in margin fit or internal adaptation were noted in the pulpal floor, horizontal walls, or external vertical walls for the typodont shoulder endocrowns compared to clinical shoulder endocrowns. The MC X mill had significantly smaller vertical wall gaps compared to the Primemill shoulder endocrown group in the clinical model. No significant differences were found for endocrown onlays when comparing milling units. Significantly smaller marginal and pulpal floor gaps existed for shoulder endocrown preparation groups while significantly smaller horizontal wall gaps existed for onlay endocrown preparation groups.</p><p><strong>Conclusion: </strong>The fit of endocrown restorations obtained on a typodont preparation translated to the fit observed clinically. Both preparations and milling units are acceptable options to be used clinically with the differences likely having no clinical impact on longevity of the restorations.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"0"},"PeriodicalIF":1.8,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability of Measuring Mandibular Movement and Condylar Position Using an Optical Jaw Registration System.","authors":"Marvin Nebe, Fabian Fenske, Elisabeth Grau, Oliver Schierz, Lisa Brinkmann, Daniel R Reißmann","doi":"10.3290/j.ijcd.b6173234","DOIUrl":"https://doi.org/10.3290/j.ijcd.b6173234","url":null,"abstract":"<p><strong>Aim: </strong>The aim of this study was to evaluate the reliability of a digital jaw registration system for measuring mandibular movement and condylar position.</p><p><strong>Materials and methods: </strong>Data on mandibular movement and condylar position were collected from a consecutively recruited sample of 20 functionally healthy adults (age range: 19-77 years; 45% female) using an optoelectronic registration system (Tizian JMA optic, zebris Medical GmbH, Isny, Germany). Participants were randomly assigned to one of two calibrated examiners, who exclusively performed the measurements for the entire study period. Condylar position was measured five times on two separate days, approximately one week apart, while mandibular movements were recorded once per appointment. The measurement results were analyzed within (intra-appointment) and compared across both measurement appointments (inter-appointment). Analysis of condylar position involved comparing the condylar positions in the resting position to the maximum intercuspation. Reliability was assessed using the intraclass correlation coefficients (ICCs).</p><p><strong>Result: </strong>For mandibular movement, ICCs indicated high reliability for mouth opening (0.95) and protrusion (0.94), with slightly lower values for laterotrusion (0.89 to 0.87), without significant differences between appointments. The condylar position showed an overall intra-appointment ICC value of 0.98. No significant differences were found within or between the examiners, intraappointment as well as inter-appointment.</p><p><strong>Conclusion: </strong>The evaluation of mandibular movements and condylar position relative to habitual occlusion using the tested digital registration system proved to be a reliable procedure. The measurements were not significantly influenced by different examiners. The results confirm the applicability of the device in clinical settings, particularly in functional diagnostics and the digital prosthetic workflow. Although the actual benefits of the application for patients have yet to be established, this study provides a scientific foundation for future research considering more vulnerable cohorts, particularly patients with a higher average age, reduced dentition, or the presence of temporomandibular disorders.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"0"},"PeriodicalIF":1.8,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Retrospective Evaluation of the 'crown-guide': An Innovative Approach for Guided Immediate Implant Placement and Restoration.","authors":"Valentin Vervack, Guillaume De Moyer, Lotte Callebaut, Line Vanduffel, Lenz Surdiacourt, Maarten Glibert","doi":"10.3290/j.ijcd.b6158646","DOIUrl":"https://doi.org/10.3290/j.ijcd.b6158646","url":null,"abstract":"<p><strong>Aim: </strong>The aim of this study was to retrospectively evaluate whether the use of the Crown-Guide - a prefabricated provisional restoration which is incorporated into a surgical pilot guide - results in a predictable esthetic outcome in patients undergoing immediate implant placement.</p><p><strong>Materials and methods: </strong>Patients (n=18) in need of a single implant restoration in the anterior region underwent immediate implant placement and loading using the Crown-Guide. Soft tissue grafting was performed in most cases, while bone xenografts were placed in all cases. Photographs, X-rays, and STL-files were collected at specific time points, making it possible to compare clinical, esthetic and radiographic outcomes at these points.</p><p><strong>Results: </strong>The mean value for the Pink Esthetic Score (PES) measure was 10.73 immediately after implant placement (T1) and 11.46 at least one year after implant placement (T3), while the White Esthetic Score (WES) mean value increased from 5.27 to 8.81 across the same interval. Importantly, for both PES and WES the increases were found to be statistically significant (p<0.01 and p<0.001, respectively). In the cases with scarring (n=5), the MSI (Mucosal Scarring Index) score ranged from 3.5 to 5.25. At time T3, an average bone loss of 0.12mm and an average soft tissue loss of 0.041mm were observed, but both were found to be statistically indistinguishable from zero. The survival rate was 94.4%.</p><p><strong>Conclusion: </strong>Immediate implant placement using the Crown-Guide offers predictable esthetic and clinical treatment outcomes, making it a practical and viable method. However, larger sample sizes are needed to confirm these findings.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"0"},"PeriodicalIF":1.8,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052554","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Surface evaluation of milled chairside CAD/CAM materials based on manufacturing speed.","authors":"Dennis J Fasbinder, Geetha Duddanahalli Siddanna","doi":"10.3290/j.ijcd.b4673355","DOIUrl":"10.3290/j.ijcd.b4673355","url":null,"abstract":"<p><strong>Aim: </strong>The aim of the present study was to measure the surface roughness of monolithic chairside CAD/CAM zirconia materials to evaluate the influence of milling speed on the ability to create a clinically smooth surface. The null hypothesis was that there would be no significant difference in the surface roughness of different zirconia materials based on the speed of milling.</p><p><strong>Materials and methods: </strong>All test samples were milled from four different monolithic CAD/CAM zirconia blocks: Cerec Zirconia, Cerec Zirconia+, Cerec MTL Zirconia (all three Dentsply Sirona), and Katana Zirconia (Kuraray Noritake). Four different dry milling speeds - Super Fast/Good, Super Fast/Very Good, Fast, and Fine - were used to dry mill the specimens in a Cerec Primemill (Dentsply Sirona) milling unit. A 3D measuring laser microscope (OLS4100 LEXT; Olympus) was used to measure surface roughness.</p><p><strong>Results: </strong>Analysis of variance (ANOVA) was used to analyze the surface roughness data for each material and milling speed. There was a significant difference for milling speed (P 0.05) but not between the zirconia materials (P > 0.05).</p><p><strong>Conclusions: </strong>Based on the limitations of the present study, the milling speed was found to influence the surface roughness of dry milled and sintered zirconia, with slower speeds resulting in smoother surfaces. The largest improvement in surface roughness occurred between the Super Fast and Fast milling speeds, with a smaller incremental improvement in surface roughness with Fine milling in the Primemill. All recorded surface roughness values were within the expected range of values to be able to efficiently hand polish a clinically acceptable surface finish.</p>","PeriodicalId":48666,"journal":{"name":"International Journal of Computerized Dentistry","volume":"0 0","pages":"47-55"},"PeriodicalIF":2.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138177613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"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":"10.3290/j.ijcd.b4784721","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":2.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138812869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}