Aim: This study investigated the accuracy of intraoral scanner (IOS) based on different image acquisition technologies in the field of presurgical-orthopedictreatment (PSOT) in neonates with cleft.
Methods: Dental cast models of clinical situations representing unilateral cleft-lip-palate(UCLP), bilateral cleft-lippalate( BCLP) and cleft-palate(CP) with reference PEEK-scanbodies (Cares RC Mono-Scankörper, Straumann, Switzerland) were scanned utilizing four IOS systems: CareStream-CS3600®(CS), Medit-i500®(MD), Cerec-Omnicam®(SO), 3Shape-Trios-3®(TS). One calibrated operator made 5 scans from each model using each IOS (N=60). Reference digital impressions were obtained by an industrialgrade laboratory scanner (Sirona inEos-X5) and superimposed using best fit algorithm. The divergence measure was extracted and the scanners were compared in view of their accuracy using generalized least squares statistical models that account for variance heterogeneity. Additionally, comparative 3D analysis of scans was performed using the reverse engineering software (Geomagic-ControlX) in order to measure the discrepancy between intraoral scans and the reference scan in different anatomic regions of interest: alveolar-crest(AC), cleft(CL), palate(PL), vestibulum(VS), premaxilla(PM).
Results: The four IOS showed relevant and significant differences in estimated trueness (P<0.001) and precision (P=0.009). Among all anatomical models and analysed area of interest TS had the best accuracy (trueness: -1.57μm; precision: 9.41μm), followed by MD (trueness: - 20.63μm; precision: 29.18μm), CS (trueness: -40.43μm; precision: 16.52μm) and SO (trueness: 81.27μm; precision: 40.32μm).
Conclusions: Impression of the maxilla in cleft lip and palate patients is challenging for the operator. Relevant and significant differences in trueness and precision were found between the four IOS. TS showed the best accuracy and was least influenced IOS under different anatomical situations.
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http://dx.doi.org/10.3290/j.ijcd.b5886430 | DOI Listing |
Clin Oral Investig
January 2025
Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Clinical Research Center for Oral Diseases of Zhejiang Province, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, 310006, China.
Objectives: To evaluate recent advances in the automatic multimodal registration of cone-beam computed tomography (CBCT) and intraoral scans (IOS) and their clinical significance in dentistry.
Methods: A comprehensive literature search was conducted in October 2024 across the PubMed, Web of Science, and IEEE Xplore databases, including studies that were published in the past decade. The inclusion criteria were as follows: English-language studies, randomized and nonrandomized controlled trials, cohort studies, case-control studies, cross-sectional studies, and retrospective studies.
Cureus
December 2024
Department of Orthodontics, School of Dentistry, Shahid Beheshti University of Medical Sciences, Tehran, IRN.
Background Orthodontic diagnostic workflows often rely on manual classification and archiving of large volumes of patient images, a process that is both time-consuming and prone to errors such as mislabeling and incomplete documentation. These challenges can compromise treatment accuracy and overall patient care. To address these issues, we propose an artificial intelligence (AI)-driven deep learning framework based on convolutional neural networks (CNNs) to automate the classification and archiving of orthodontic diagnostic images.
View Article and Find Full Text PDFInt Dent J
January 2025
Department of Stomatology, Beijing Tongren Hospital, Capital Medical University, Beijing, China. Electronic address:
Introduction And Aim: The assessment of gingival inflammation surface features mainly depends on subjective judgment and lacks quantifiable and reproducible indicators. Therefore, it is a need to acquire objective identification information for accurate monitoring and diagnosis of gingival inflammation. This study aims to develop an automated method combining intraoral scanning (IOS) and deep learning algorithms to identify the surface features of gingival inflammation and evaluate its accuracy and correlation with clinical indicators.
View Article and Find Full Text PDFJ Dent
January 2025
Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University; Clinical Research Center of Shaanxi Province for Dental and Maxillofacial Disease, College of Stomatology, Xi'an Jiaotong University; Department of Implant Dentistry, College of Stomatology, Xi'an Jiaotong University. Electronic address:
Objective: The study aimed to evaluate the accuracy and safety of the semi-active robotic system for implant placement in atrophic posterior maxilla.
Methods: Patients underwent robot-assisted implant placement in atrophic posterior maxilla were identified and included. Cone-beam computed tomography (CBCT) was performed before surgery.
J Dent Sci
January 2025
First Clinical Division, Peking University School and Hospital of Stomatology & National Center for Stomatology & National Clinical Research Center for Oral Diseases & National Engineering Research Center of Oral Biomaterials and Digital Medical Devices & Beijing Key Laboratory of Digital Stomatology & NHC Key Laboratory of Digital Stomatology & NMPA Key Laboratory for Dental Materials, Beijing, China.
Background/purpose: Artificial intelligence (AI) can assist in medical diagnosis owing to its high accuracy and efficiency. This study aimed to develop a diagnostic system for automatically determining the degree of tooth wear (TW) using intraoral photographs with deep learning.
Materials And Methods: The study included 388 intraoral photographs.
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