The Effect of Resin Infiltration Technique on the Calculated Color Change of Demineralized Lesions of Different Severities.

OBJECTIVE: To assess the color change of demineralized enamel lesions of different severities after resin infiltration using both clinical spectrophotometry and digital photography. METHODS AND MATERIALS: Sixty sound human premolars were randomly divided into 3 groups according to the demineralization level. All the teeth were immersed in a demineralizing solution of a pH adjusted to 4.4 at 37°C. Three levels of demineralization were obtained (D1 shallow, D2 moderate, D3 deep) according to the demineralization time. The demineralized area was then infiltrated by low-viscosity resin (ICON, DMG, Germany). Two instrumental methods were utilized to assess the color difference, a clinical spectrophotometer and digital photography at three time points (sound, demineralized, and infiltrated enamel) to calculate the color difference between sound and demineralized enamel (ΔE1) and between sound and infiltrated enamel (ΔE2). Statistical analysis was performed by ANOVA, followed by Tukey's post hoc test. The correlation was analyzed using linear regression. RESULTS: Two-way ANOVA showed statistically significant differences for both levels of the study (p≤0.05). The color change (ΔE1) and (ΔE2) for different demineralization levels showed statistically significant differences between all groups. For both clinical spectrophotometry and digital photography, D3 showed the highest difference followed by D2 and then D1. As for (ΔE1) calculations, digital photography had a significantly higher difference than spectrophotometry for the D1 group (5.47±0.93 vs 2.78±0.58). As for (ΔE2) digital photography had a statistically significantly lower difference than spectrophotometry (5.55±1.05 vs 6.48±0.76) for the D3 group. CONCLUSIONS: Color correction after resin infiltration is affected by the demineralization level of enamel. Clinical spectrophotometry and digital photography can detect similarly the color change of demineralized enamel after resin infiltration in shallow and moderate demineralization. However, in deep demineralization clinical spectrophotometry tends to exaggerate the color change compared to digital photography.

Teledentistry accuracy for caries diagnosis: a systematic review of in-vivo studies using extra-oral photography methods.

BACKGROUND: Dental caries is a global public health concern, and early detection is essential. Traditional methods, particularly visual examination, face access and cost challenges. Teledentistry, as an emerging technology, offers the possibility to overcome such barriers, and it must be given high priority for assessment to optimize the performance of oral healthcare systems. The aim of this study was to systematically review the literature evaluating the diagnostic accuracy of teledentistry using photographs taken by Digital Single Lens Reflex (DSLR) and smartphone cameras against visual clinical examination in either primary or permanent dentition. METHODS: The review followed PRISMA-DTA guidelines, and the PubMed, Scopus, and Embase databases were searched through December 2022. Original in-vivo studies comparing dental caries diagnosis via images taken by DSLR or smartphone cameras with clinical examination were included. The QUADAS-2 was used to assess the risk of bias and concerns regarding applicability. Meta-analysis was not performed due to heterogeneity among the studies. Therefore, the data were analyzed narratively by the research team. RESULTS: In the 19 studies included, the sensitivity and specificity ranged from 48 to 98.3% and from 83 to 100%, respectively. The variability in performance was attributed to factors such as study design and diagnostic criteria. Specific tooth surfaces and lesion stages must be considered when interpreting outcomes. Using smartphones for dental photography was common due to the convenience and accessibility of these devices. The employment of mid-level dental providers for remote screening yielded comparable results to those of dentists. Potential bias in patient selection was indicated, suggesting a need for improvements in study design. CONCLUSION: The diagnostic accuracy of teledentistry for caries detection is comparable to that of traditional clinical examination. The findings establish teledentistry's effectiveness, particularly in lower income settings or areas with access problems. While the results of this review is promising, conducting several more rigorous studies with well-designed methodologies can fully validate the diagnostic accuracy of teledentistry for dental caries to make oral health care provision more efficient and equitable. REGISTRATION: This study was registered with PROSPERO (CRD42023417437).

Evaluation of transfer ensemble learning-based convolutional neural network models for the identification of chronic gingivitis from oral photographs.

BACKGROUND: To evaluate the performances of several advanced deep convolutional neural network models (AlexNet, VGG, GoogLeNet, ResNet) based on ensemble learning for recognizing chronic gingivitis from screening oral images. METHODS: A total of 683 intraoral clinical images acquired from 134 volunteers were used to construct the database and evaluate the models. Four deep ConvNet models were developed using ensemble learning and outperformed a single model. The performances of the different models were evaluated by comparing the accuracy and sensitivity for recognizing the existence of gingivitis from intraoral images. RESULTS: The ResNet model achieved an area under the curve (AUC) value of 97%, while the AUC values for the GoogLeNet, AlexNet, and VGG models were 94%, 92%, and 89%, respectively. Although the ResNet and GoogLeNet models performed best in classifying gingivitis from images, the sensitivity outcomes were not significantly different among the ResNet, GoogLeNet, and Alexnet models (p>0.05). However, the sensitivity of the VGGNet model differed significantly from those of the other models (p < 0.001). CONCLUSION: The ResNet and GoogLeNet models show promise for identifying chronic gingivitis from images. These models can help doctors diagnose periodontal diseases efficiently or based on self-examination of the oral cavity by patients.

Designing an artificial intelligence system for dental occlusion classification using intraoral photographs: A comparative analysis between artificial intelligence-based and clinical diagnoses.

INTRODUCTION: This study aimed to design an artificial intelligence (AI) system for dental occlusion classification using intraoral photographs. Moreover, the performance of this system was compared with that of an expert clinician. METHODS: This study included 948 adult patients with permanent dentition who presented to the Department of Orthodontics, School of Dentistry, Mashhad University of Medical Sciences, during 2022-2023. The intraoral photographs taken from the patients in left, right, and frontal views (3 photographs for each patient) were collected and underwent augmentation, and about 7500 final photographs were obtained. Moreover, the patients were clinically examined by an expert orthodontist for malocclusion, overjet, and overbite and were classified into 6 groups: Class I, Class II, half-cusp Class II, Super Class I, Class III, and unclassifiable. In addition, a multistage neural network system was created and trained using the photographs of 700 patients. Then, it was used to classify the remaining 248 patients using their intraoral photographs. Finally, its performance was compared with that of the expert clinician. All statistical analyses were performed using the Stata software (version 17; Stata Corp, College Station, Tex). RESULTS: The accuracy, precision, recall, and F1 score of the AI system in the malocclusion classification of molars were calculated to be 93.1%, 88.6%, 91.2%, and 89.7%, respectively, whereas the AI system had an accuracy, precision, recall, and F1 score of 89.1%, 88.8%, 91.42%, and 89.8% for malocclusion classification of canines, respectively. Moreover, the mean absolute error of the AI system accuracy was 1.98 ± 2.11 for overjet and 1.28 ± 1.60 for overbite classifications. CONCLUSIONS: AI exhibited remarkable performance in detecting all classes of malocclusion, which was higher than that of orthodontists, especially in predicting angle classification. However, its performance was not acceptable in overjet and overbite measurement compared with expert orthodontists.

Application of deep learning in isolated tooth identification.

BACKGROUND: Teeth identification has a pivotal role in the dental curriculum and provides one of the important foundations of clinical practice. Accurately identifying teeth is a vital aspect of dental education and clinical practice, but can be challenging due to the anatomical similarities between categories. In this study, we aim to explore the possibility of using a deep learning model to classify isolated tooth by a set of photographs. METHODS: A collection of 5,100 photographs from 850 isolated human tooth specimens were assembled to serve as the dataset for this study. Each tooth was carefully labeled during the data collection phase through direct observation. We developed a deep learning model that incorporates the state-of-the-art feature extractor and attention mechanism to classify each tooth based on a set of 6 photographs captured from multiple angles. To increase the validity of model evaluation, a voting-based strategy was applied to refine the test set to generate a more reliable label, and the model was evaluated under different types of classification granularities. RESULTS: This deep learning model achieved top-3 accuracies of over 90% in all classification types, with an average AUC of 0.95. The Cohen's Kappa demonstrated good agreement between model prediction and the test set. CONCLUSIONS: This deep learning model can achieve performance comparable to that of human experts and has the potential to become a valuable tool for dental education and various applications in accurately identifying isolated tooth.

Simultaneous detection of dental caries and fissure sealant in intraoral photos by deep learning: a pilot study.

BACKGROUND: Deep learning, as an artificial intelligence method has been proved to be powerful in analyzing images. The purpose of this study is to construct a deep learning-based model (ToothNet) for the simultaneous detection of dental caries and fissure sealants in intraoral photos. METHODS: A total of 1020 intraoral photos were collected from 762 volunteers. Teeth, caries and sealants were annotated by two endodontists using the LabelMe tool. ToothNet was developed by modifying the YOLOX framework for simultaneous detection of caries and fissure sealants. The area under curve (AUC) in the receiver operating characteristic curve (ROC) and free-response ROC (FROC) curves were used to evaluate model performance in the following aspects: (i) classification accuracy of detecting dental caries and fissure sealants from a photograph (image-level); and (ii) localization accuracy of the locations of predicted dental caries and fissure sealants (tooth-level). The performance of ToothNet and dentist with 1year of experience (1-year dentist) were compared at tooth-level and image-level using Wilcoxon test and DeLong test. RESULTS: At the image level, ToothNet achieved an AUC of 0.925 (95% CI, 0.880-0.958) for caries detection and 0.902 (95% CI, 0.853-0.940) for sealant detection. At the tooth level, with a confidence threshold of 0.5, the sensitivity, precision, and F1-score for caries detection were 0.807, 0.814, and 0.810, respectively. For fissure sealant detection, the values were 0.714, 0.750, and 0.731. Compared with ToothNet, the 1-year dentist had a lower F1 value (0.599, p < 0.0001) and AUC (0.749, p < 0.0001) in caries detection, and a lower F1 value (0.727, p = 0.023) and similar AUC (0.829, p = 0.154) in sealant detection. CONCLUSIONS: The proposed deep learning model achieved multi-task simultaneous detection in intraoral photos and showed good performance in the detection of dental caries and fissure sealants. Compared with 1-year dentist, the model has advantages in caries detection and is equivalent in fissure sealants detection.

Detecting white spot lesions on post-orthodontic oral photographs using deep learning based on the YOLOv5x algorithm: a pilot study.

BACKGROUND: Deep learning model trained on a large image dataset, can be used to detect and discriminate targets with similar but not identical appearances. The aim of this study is to evaluate the post-training performance of the CNN-based YOLOv5x algorithm in the detection of white spot lesions in post-orthodontic oral photographs using the limited data available and to make a preliminary study for fully automated models that can be clinically integrated in the future. METHODS: A total of 435 images in JPG format were uploaded into the CranioCatch labeling software and labeled white spot lesions. The labeled images were resized to 640 × 320 while maintaining their aspect ratio before model training. The labeled images were randomly divided into three groups (Training:349 images (1589 labels), Validation:43 images (181 labels), Test:43 images (215 labels)). YOLOv5x algorithm was used to perform deep learning. The segmentation performance of the tested model was visualized and analyzed using ROC analysis and a confusion matrix. True Positive (TP), False Positive (FP), and False Negative (FN) values were determined. RESULTS: Among the test group images, there were 133 TPs, 36 FPs, and 82 FNs. The model's performance metrics include precision, recall, and F1 score values of detecting white spot lesions were 0.786, 0.618, and 0.692. The AUC value obtained from the ROC analysis was 0.712. The mAP value obtained from the Precision-Recall curve graph was 0.425. CONCLUSIONS: The model's accuracy and sensitivity in detecting white spot lesions remained lower than expected for practical application, but is a promising and acceptable detection rate compared to previous study. The current study provides a preliminary insight to further improved by increasing the dataset for training, and applying modifications to the deep learning algorithm. CLINICAL REVELANCE: Deep learning systems can help clinicians to distinguish white spot lesions that may be missed during visual inspection.

Comparing modified USPHS and FDI criteria for the assessment of glass ionomer restorations in primary molars utilising clinical and photographic evaluation.

PURPOSE: To compare the applicability of modified US Public Health Service (USPHS) and FDI criteria for evaluating glass ionomer cement (GIC) restorations in primary posterior teeth through digital image analysis. METHODS: This comparative analytic study was conducted at the Children's Dental Clinic RSKGM FKG UI, involving 40 GIC restorations on lower first primary molars in children aged 4-9 years. After cleaning, the restorations were assessed clinically using modified USPHS and FDI criteria before taking digital images, then the collected images were re-evaluated using both sets of criteria, and the clinical assessment results were compared to the digital image assessment results. RESULTS: Statistical analysis revealed significant differences between the clinical evaluation of GIC restorations in primary teeth and their corresponding digital photographs when using the modified USPHS criteria, and although the use of FDI criteria yielded different results, these differences were not statistically significant. CONCLUSION: The assessment of GIC restorations through digital images aligns more closely with clinical assessments using the FDI criteria compared to the modified USPHS criteria.

Digital plaque monitoring: An evaluation of different intraoral scanners.

OBJECTIVES: Intraoral scanners (IOS) display disclosed plaque, and the scientific literature has reported that plaque levels can be monitored on intraoral scans using one IOS system (Dexis 3800; control IOS). This study aimed to investigate whether this is also possible with other IOS systems (i700, Primescan, Trios 5; test IOS). MATERIALS AND METHODS: Ten participants (29.6 ± 5.5 years) were enrolled. After plaque accumulation and subsequent toothbrushing, intraoral scans were performed with the control IOS and the three test IOS. All scans were aligned and the vestibular/oral surfaces of the Ramfjord teeth (16, 21, 24, 36, 41, 44) were analysed with automated planimetry using a predefined threshold value. The proportion of pixels assigned to plaque-covered areas was expressed as a percentage of the total number of pixels (P%). We then assessed whether the planimetrically determined plaque-covered areas corresponded to those identified visually. This revealed that a threshold correction (P%corr) was required for approximately 20 % (i700 and Trios 5) to over 65 % (Primescan) of the images. RESULTS: Bland-Altman analysis showed no significant systematic bias and limits of agreement ranging from approximately -20 to +20 P% units, with a tendency towards lower values at higher plaque coverage. Manual correction improved the agreement and halved the limits of agreement. All test IOS could detect a reduction in plaque after brushing, as well as the typical site-dependant plaque distribution patterns. CONCLUSIONS: All test IOS appeared to be suitable for plaque monitoring. Planimetric methods must be adapted to the colour representation of the IOS. CLINICAL SIGNIFICANCE: Plaque monitoring using IOS opens a new field of application in preventive dentistry.

Evaluation metric of smile classification by peri-oral tissue segmentation for the automation of digital smile design.

OBJECTIVES: This study aimed to develop and validate evaluation metric for an automated smile classification model termed the "smile index." This innovative model uses computational methods to numerically classify and analyze conventional smile types. METHODS: The datasets used in this study consisted of 300 images to verify, 150 images to validate, and 9 images to test the evaluation metric. Images were annotated using Labelme. Computational techniques were used to calculate smile index values for the study datasets, and the resulting values were evaluated in three stages. RESULTS: The smile index successfully classified smile types using cutoff values of 0.0285 and 0.193. High accuracy (0.933) was achieved, along with an F1 score greater than 0.09. The smile index successfully reclassified smiles into six types (low, low-to-medium, medium, medium-to-high, high, and extremely high smiles), thereby providing a clear distinction among different smile characteristics. CONCLUSION: The smile index is a novel dimensionless parameter for classifying smile types. The index acts as a robust evaluation tool for artificial intelligence models that automatically classify smile types, thereby providing a scientific basis for largely subjective aesthetic elements. CLINICAL SIGNIFICANCE: The computational approach employed by the smile index enables quantitative numerical classification of smile types. This fosters the application of computerized methods in quantifying and analyzing real smile characteristics observed in clinical practice, paving the way for a more objective evidence-based approach to aesthetic dentistry.

Comparative Evaluation of Various Lens and Ring Flash Combination for Intraoral Photography.

BACKGROUND: Extraoral and intraoral dental photographs serve as preoperative records and document the entire treatment. Correctly composed orthodontic photographs are crucial for remote diagnosis and may serve as a bulwark against medicolegal challenges. MATERIALS AND METHODS: In this prospective study, intraoral frontal photographs of patients with ideal occlusion were taken using two types of lenses (EF-S 18-55 mm f/3.5-5.6 IS STM lens (Canon, Tokyo, JP), SP 90 mm F/2.8 MACRO VC lens (Model F017 Tamron, NY, USA)) and two different ring flash systems (Meike FC-100 Macro Ring LED Light (Meike, China), Macro Ring flash Lite YN-14EX (Yongnuo digital, China)). The combination of lens and flash used was grouped into four groups. Twenty-eight intraoral photographs of patients were taken. An image quality assessment survey was distributed among two groups - 50 orthodontists and 50 other dental specialists. RESULTS: The participants were asked to assess all the intraoral images and subjectively score them on a scale of one to ten, with one being very poor and ten being excellent, considering the sharpness, color, brightness, contrast, and overall quality of the image. The general dentists rated the images taken with a 90-mm macro lens and ring flash as the best quality photographs. Images obtained using an 18-55 mm lens and ring LED received significantly lesser scores and were graded good by dentists. CONCLUSION: This combination of lens and flash may prove a valuable investment in the long-term aiding in excellent dental images for diagnosis and treatment monitoring.

Telediagnosis of dental caries: Possible or impossible? A pilot cross-sectional study.

OBJECTIVES: The aims of this study were to evaluate the effectiveness of teledentistry (based on a home intraoral imaging protocol) in detecting dental caries and to assess the accuracy of this method compared to clinical examination. METHODS: Forty-three patients were recruited for the study. Using a protocol for taking intraoral photographs at home with a smartphone proposed by the Dental School of Verona, a remote diagnosis of dental caries (TD) was performed by an experienced dentist. The same caries sites were also assessed by clinical diagnosis (CD) by a second experienced dentist. Ten photos were taken at home in five different perspectives, with and without flash, and emailed to one of the authors. The best five photos were selected for telediagnosis. The International Caries Detection and Assessment System (ICDAS II) score was used for caries diagnosis. Statistical tests were performed: Sensitivity and specificity of TD, the positive and negative predictive value of TD (PPV-NPV), and Spearman correlation to evaluate the relationship between the scores of TD and CD. RESULTS: A total of 430 photographs were submitted; TD was performed on 215 photographs and 43 patients were visited. A total of 1201 teeth were analyzed. The sensitivity of TD was 74.0, the specificity was 99.1, the PPV of TD was 91.7, and the NPV was 96.4. The Spearman correlation was 0.816, showing a very strong correlation between the values obtained with TD and CD. CONCLUSIONS: The study showed good potential for TD, which proved to be a feasible method to combine with routine caries diagnosis in daily preventive dentistry practice.

A Standardized Approach to Extra-Oral and Intra-Oral Digital Photography.

Contemporary dentistry mandates a more comprehensive and personalized analysis of each patient. Technological advances in digital photography have played vital roles in diagnostic accuracy, treatment planning, execution of therapies, and outcome evaluations, including esthetic enhancement. Digital photography also provides an excellent platform for patient education, communication, and co-management of cases with other healthcare providers. However, intra-oral photography often faces challenges such as inaccessibility of areas to be captured, different moveable vs. fixed tissues involved, contamination with saliva or blood, and differing illumination needs on various locations. Thus, a more standardized and systematic approach is proposed for intra- and extra-oral documentation via digital photography to overcome the existing technical challenges. The current work will outline the appropriate equipment specifications (camera bodies, macro lens, and flashes), positions and postures of the operator and patients, proper techniques of tissue retraction, the use of appropriate intra-oral mirrors, and the essential elements such as aperture settings (F-stop), ISO, shutter speed, and white balance. This article aims to provide all dental professionals with an approachable linear array of guidelines to produce simplified and standardized visual tools for more efficient and effective documentation.

Automated detection of posterior restorations in permanent teeth using artificial intelligence on intraoral photographs.

OBJECTIVES: Intraoral photographs might be considered the machine-readable equivalent of a clinical-based visual examination and can potentially be used to detect and categorize dental restorations. The first objective of this study was to develop a deep learning-based convolutional neural network (CNN) for automated detection and categorization of posterior composite, cement, amalgam, gold and ceramic restorations on clinical photographs. Second, this study aimed to determine the diagnostic accuracy for the developed CNN (test method) compared to that of an expert evaluation (reference standard). METHODS: The whole image set of 1761 images (483 of unrestored teeth, 570 of composite restorations, 213 of cements, 278 of amalgam restorations, 125 of gold restorations and 92 of ceramic restorations) was divided into a training set (N = 1407, 401, 447, 66, 231, 93, and 169, respectively) and a test set (N = 354, 82, 123, 26, 47, 32, and 44). The expert diagnoses served as a reference standard for cyclic training and repeated evaluation of the CNN (ResNeXt-101-32 × 8d), which was trained by using image augmentation and transfer learning. Statistical analysis included the calculation of contingency tables, areas under the receiver operating characteristic curve and saliency maps. RESULTS: After training was complete, the CNN was able to categorize restorations correctly with the following diagnostic accuracy values: 94.9% for unrestored teeth, 92.9% for composites, 98.3% for cements, 99.2% for amalgam restorations, 99.4% for gold restorations and 97.8% for ceramic restorations. CONCLUSIONS: It was possible to categorize different types of posterior restorations on intraoral photographs automatically with a good diagnostic accuracy. CLINICAL SIGNIFICANCE: Dental diagnostics might be supported by artificial intelligence-based algorithms in the future. However, further improvements are needed to increase accuracy and practicability.

Mobile photographic screening for dental caries in children: Diagnostic performance compared to unaided visual dental examination.

OBJECTIVES: This study was conducted to compare the use of intraoral photographs with the unaided visual dental examination as a means of dental caries detection in children. METHODS: Children aged 4- to 14-year-olds were visually examined at their schools. Following dental examinations, children had five photographs of their teeth taken using a smartphone camera. Four dental reviewers, who are different from those who visually examined the children, assessed intraoral photographs for dental caries. Sensitivity, specificity, and inter-rater reliability agreement were estimated to assess the diagnostic performance of the photographic method relative to the benchmark visual dental assessments. Caries prevalence was measured using dft/DFT (decayed and filled teeth) index. RESULTS: One hundred thirty-eight children (67 male and 71 female) were enrolled and had a mean age of 7.8 ± 2.1 years. The caries prevalence (dft/DFT > 0) using photographic dental assessments ranged from 30 percent to 39 percent but was not significantly different from the prevalence (42 percent) estimated with the visual dental examination (P ≥ 0.07). The sensitivity and specificity of the photographic method for detection of dental caries compared to visual dental assessments were 58-80 percent and 99.7-99.9 percent, respectively. The sensitivity for the photographic assessments was high in the primary dentition (63-82 percent) and children ≤7-year-olds (67-78 percent). The inter-rater reliability for the photographic assessment versus the benchmark ranged from substantial to almost perfect agreement (Kappa = 0.72-0.87). CONCLUSIONS: The photographic approach to dental screening, used within the framework of its limitations, yielded an acceptable diagnostic level of caries detection, particularly in younger children with primary dentition.

Dental Whitening: Self-Referred Needs versus Professional Indication

Abstract Objective: To analyze the self-reported need of patients compared to professional indications for tooth whitening. Material and Methods: Initially, 58 undergraduate students responded to a form that highlighted the question: "Do you think you need to have your teeth whitened?" Among those who answered positively to the previous question, ten individuals were photographed with their smiles. In addition, they were asked to point out, on the Vita 3D-Master scale, which color they believed their teeth had, a value that was compared to the actual color obtained by a spectrophotometer. Finally, the photographs were presented to dentists, who were asked about the indication or not of the whitening treatment. Results: Most interviewees (63.8%) self-reported the need for whitening, as well as there was a greater incidence of a positive indication among professionals (53.9%). Pearson's Chi-square test revealed a relationship between patient gender and the training course on the desire to have teeth whitened. Among the professionals, the specialty, as well as time since graduation, interfered in the indication for whitening. Conclusion: Professionals and patients share the aesthetic ideal directly related to light teeth; most patients self-perceive the color of their teeth darker than it actually is; the opinion about the color of the teeth has an extremely subjective character and varies greatly from one professional to another (AU).

A novel method quantifying caries following orthodontic treatment.

This retrospective pilot study used a newly developed evaluation tool to assess the prevalence and incidence of White Spot Lesions (WSL) before and after multibracket appliance (MB) therapy. Digital photographs of 121 adolescent patients (63 ♂, 58 ♀) with metal brackets were analyzed retrospectively before and after MB therapy. The labial surfaces of anterior teeth, canine teeth, and premolars in the upper (UJ) and lower jaws (LJ) were evaluated using the Enamel Decalcification Index (EDI) by Banks and Richmond (Eur J Orthod, 16(1):19-25, 1994, levels 0-3) and a specially developed digitally scaled graticule with concentric circles to quantify the extent of WSL (in %). The statistical data analysis was based on crosstabulations and logistic regression. Before MB, 69.4% of the patients presented at least one WSL and 97.5% after, an increase of 28.1%. Before MB, 18.4% of the tooth surfaces (TS) showed an EDI level of 1-3. After MB, 51.8% of the TS featured WSL. 18.2% of the TS showed a WSL to the extent of ≥ 20-100% before and 52.3% after MB. The incidence in the UJ (71-79%) as well as the LJ (64-76%) was highest for the first and second premolars and lowest for LJ incisors (22-35%). The probability for developing a new distal WSL is higher than developing gingival, mesial or occlusal WSL. Labial MB therapy drastically increases the risk of developing WSL. We verified a concise quantification of the extent of labial WSL with the evaluation index.

Effectiveness of 2D radiographs in detecting CBCT-based incidental findings in orthodontic patients.

Some craniofacial diseases or anatomical variations are found in radiographic images taken for other purposes. These incidental findings (IFs) can be detected in orthodontic patients, as various radiographs are required for orthodontic diagnosis. The radiographic data of 1020-orthodontic patients were interpreted to evaluate the rates of IFs in three-dimensional (3D) cone-beam-computed tomography (CBCT) with a large field of view (FOV) and investigate the effectiveness and accuracy of two-dimensional (2D) radiographs for detecting IFs compared to CBCT. Prevalence and accuracy in five areas was measured for sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV). The accuracies of various 2D-radiograph were compared through a proportion test. A total of 709-cases (69.5%) of 1020-subjects showed one or more IFs in CBCT images. Nasal cavity was the most affected area. Based on the CBCT images as a gold standard, different accuracies of various 2D-radiographs were observed in each area of the findings. The highest accuracy was confirmed in soft tissue calcifications with comprehensive radiographs. For detecting nasal septum deviations, postero-anterior cephalograms were the most accurate 2D radiograph. In cases the IFs were not determined because of its ambiguity in 2D radiographs, considering them as an absence of findings increased the accuracy.

Novel methodology for measuring intraoral wear in enamel and dental restorative materials.

OBJECTIVES: To test the hypotheses that (a) the chairside/handheld dental scanner combined with a metrology software will measure clinical wear in vivo in agreement with measurements from X-ray computed microtomography and; (b) polished monolithic zirconia does not cause accelerated wear of opposing enamel. MATERIALS AND METHODS: Thirty single crowns were randomized to receive a monolithic zirconia or metal-ceramic crown. Two non-restored opposing teeth in the same quadrants were identified to serve as enamel controls. After cementation, quadrants were scanned using an intraoral dental scanner. Patients were recalled at 6-months and 1-year for re-scanning. Scanned images were compared using a metrology software to determine maximum vertical wear of teeth. The accuracy of the scanning measurements from this new method was compared with X-ray computed microtomography (micro-CT) measurements. Statistical analysis was performed using Mann-Whitney U test to determine significant differences between wear of enamel against zirconia, metal-ceramic or enamel. Linear regression analysis determined agreement between measurements obtained using intraoral scanning and micro-CT. RESULTS: Regression analysis demonstrated that there is a quantitative agreement between depth and volume measurements produced using intraoral scanning and the micro-CT methodologies. There was no significant difference between the wear of enamel against polished monolithic zirconia crowns and enamel against enamel. CONCLUSIONS: Intraoral scanning combined with a matching software can accurately quantify clinical wear to verify that monolithic zirconia exhibited comparable wear of enamel compared with metal-ceramic crowns and control enamel. Agreement between the intraoral scanner and the micro-CT was 99.8%. Clinical Trials.gov NCT02289781.

Planejamento Digital do Sorriso / Planificación digital de la sonrisa / Digital Smile Planning

RESUMO Introdução: Avanços no campo da Odontologia Estética têm proporcionado métodos cada vez mais inovadores na construção de um sorriso funcional e harmonioso. O elevado grau de exigência do paciente por detalhes e por sorrisos personalizados evidencia a necessidade do clínico em lançar mão de meios que facilitem a comunicação para o melhor entendimento de seus pacientes em relação ao tratamento proposto. Objetivo: Realizar uma revisão de literatura sobre Planejamento Digital do Sorriso em Odontologia. Métodos: Com caráter atual e integrativo, esta revisão de literatura foi realizada utilizando os descritores estética dentária, sorriso e fotografia dentária, em portugês, inglês e espanhol, obtendo 302 artigos, dos quais foram utilizados 51 artigos e 1 livro como base científica para o estudo, obedecendo os critérios de inclusão e exclusão. Análise e integração da informação: Os avanços na informática, fotografia digital e processamento de imagens, bem como a redução dos custos envolvidos, têm proporcionado que um planejamento tradicional em odontologia para tratamentos estéticos tenha evoluído para um planejamento digital, fornecendo uma visão ampla do diagnóstico, melhorando a comunicação entre paciente e equipe profissional envolvida, tornando o tratamento mais previsível, facilitando a compreensão e permitindo sua análise crítica e participação mais ativa no planejamento. Considerações finais: As técnicas utilizadas para realização do Planejamento Digital do Sorriso tornam os procedimentos mais previsíveis, melhorando a visualização e compreensão das etapas a serem realizadas. O conceito de Visagismo não apresenta embasamento científico suficiente. Aplicações de conceitos de proporção áurea na odontologia têm sido bastante estudadas, porém seu uso não é consensual por existirem outros parâmetros(AU)

RESUMEN Introducción: Avances en el campo de la Odontología Estética han proporcionado métodos cada vez más innovadores en la construcción de una sonrisa funcional y armoniosa. El alto grado de exigencia del paciente por detalles y por sonrisas personalizadas evidencia la necesidad de que el clínico use medios que faciliten la comunicación para una mejor comprensión de sus pacientes con respecto al tratamiento propuesto. Objetivo: Realizar una revisión de literatura sobre planificación digital de la sonrisa en odontología. Métodos: Esta revisión de literatura se realizó utilizando los descriptores "estética dental", "sonrisa" y "fotografía dentaria", en portugués, inglés y español. Se recuperaron un libro y 302 artículos, de los cuales, el libro y 51 artículos fueron utilizados como base científica para el estudio. Análisis e integración de la información: Los avances en la informática, fotografía digital y procesamiento de imágenes, así como la reducción de los costos involucrados, han permitido que una planificación dental tradicional en odontología para tratamientos estéticos evolucione hacia la planificación digital y proporcione una visión amplia del diagnóstico. Esto ha mejorado la comunicación entre los pacientes y los equipos de profesionales involucrados y ha hecho el tratamiento más previsible. Así se ha facilitado la comprensión del paciente, se ha permitido su análisis crítico y su participación más activa en la planificación. Consideraciones finales: Las técnicas utilizadas para realizar la planificación digital de la sonrisa hacen los procedimientos más previsibles y mejora la visualización y comprensión de cada etapa. El concepto de Visagismo no presenta un fundamento científico suficiente. Las aplicaciones de conceptos de proporción áurea en la odontología han sido bastante estudiadas, pero, por existir otros parámetros, su uso no es consensual(AU)

ABSTRACT Introduction: Advances in the field of aesthetic dentistry have provided increasingly innovative methods for building a functional and harmonious smile. The high degree of patient demand for details and for personalized smiles shows the need for clinicians to use means that facilitate communication for better understanding of their patients regarding the proposed treatment. Objective: To carry out a literature review on digital smile planning in dentistry. Methods: This literature review was performed using the descriptors estética dental [dental aesthetics], sonrisa [smile], and fotografía dental [dental photography], in Portuguese, English and Spanish. A book and 302 articles were retrieved, of which the book and 51 articles were used as scientific basis for the study. Information analysis and integration: Advances in computer science, digital photography and image processing, as well as the reduction of costs involved, have allowed traditional dental planning in dentistry for aesthetic treatments to evolve towards digital planning and to provide comprehensive diagnostic insight. This has improved communication between patients and professional teams involved and has made treatment more predictable. Thus, the patient's understanding has been facilitated, as well as is has permitted his or her critical analysis and more active participation in planning. Final considerations: The techniques used to carry out digital smile planning make the procedures more predictable and improve the visualization and understanding in each stage. The concept of visagism does not present a sufficient scientific basis. The applications of golden ratio concepts into dentistry have been extensively studied, but, due to the existence of other parameters, their use is not consensual(AU)