The Value of Clinical Photographs in the Management of Traumatic Dental Injuries.

Purpose: The purpose of this study was to explore the perceived value of clinical photographs for traumatic dental injuries (TDIs). Methods: A survey was sent to members of the American Academy of Pediatric Dentistry (AAPD). The survey collected respondents' responses to case-based questions with and without photographs, and opinions about the value of photography for TDI. Results: A total of 496 respondents (5.8 percent response) completed the survey. Overall, no significant difference in correct answers was observed between cases with and without a photograph (P=0.09). The majority of respondents (82.2 percent) agreed that photographs should be taken for the management of TDIs, with 88.7 percent stating that the photographs aided in the diagnosis of TDIs. The majority of respondents acknowledged the time-saving (80.9 percent) and legal importance (77.0 percent) of photographs. Conclusion: Photographs should be taken in the management of traumatic dental injuries when possible for history and documentation purposes.

Evaluation of the Golden Proportion, Golden Percentage, and Recurring Esthetic Dental Proportion in Kenyans of African Descent.

OBJECTIVES: To evaluate the validity of the Golden Proportion, Golden Percentage, and Recurring Esthetic Dental (RED) Proportion among Kenyans of African descent with naturally well-aligned teeth. MATERIALS AND METHODS: Standardized frontal photographic images of the smiles of 175 participants aged 18-35 years were obtained, and Adobe Photoshop was used to analyze and measure the frontal widths of the maxillary central and lateral incisors and canines in triplicate. The average teeth widths were calculated to determine the existence of the Golden Proportion, Golden Percentage, and RED Proportion, and their validity using independent sample t-tests to compare the differences in the mean teeth widths at α < 0.05. RESULTS: The number of male and female participants was 107 (61.1%) and 68 (38.9%), respectively. The Golden Proportion between the maxillary central and lateral incisors was found in 4.0% on the right and 2.8% on the left of all the participants, but between the maxillary lateral incisors and canines was found in only 0.6% on the right of male participants (p < 0.0001). The RED Proportion between the maxillary lateral and central incisors was in the range of 67%-70%, and between the canines and lateral incisors was 82%-84% (p < 0.0001). The proportion of RED was not constant, and gradually increased distally. The Golden Percentage of 15% was observed in the lateral incisors bilaterally; however, in the central incisors and the canines, the Golden Percentage was 22% and 12%, respectively. CONCLUSION: The Golden and RED Proportions were invalid determinants of anterior teeth proportions. The Golden Percentage existed only in the lateral incisors. The Golden Proportion, RED Proportion, and Golden Percentage theories may not be applicable to all populations when designing smiles. Racial and ethnic backgrounds are important considerations to establish objective quantifiable values of anterior tooth proportions that are beneficial for esthetic restorations.

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.

External validation of an artificial intelligence-based method for the detection and classification of molar incisor hypomineralisation in dental photographs.

OBJECTIVES: This ex vivo diagnostic study aimed to externally validate an open-access artificial intelligence (AI)-based model for the detection, classification, localisation and segmentation of enamel/molar incisor hypomineralisation (EH/MIH). METHODS: An independent sample of web images showing teeth with (n = 277) and without (n = 178) EH/MIH was evaluated by a workgroup of dentists whose consensus served as the reference standard. Then, an AI-based model was used for the detection of EH/MIH, followed by automated classification and segmentation of the findings (test method). The accuracy (ACC), sensitivity (SE), specificity (SP) and area under the curve (AUC) were determined. Furthermore, the correctness of EH/MIH lesion localisation and segmentation was evaluated. RESULTS: An overall ACC of 94.3 % was achieved for image-based detection of EH/MIH. Cross-classification of the AI-based class prediction and the reference standard resulted in an agreement of 89.2 % for all diagnostic decisions (n = 594), with an ACC between 91.4 % and 97.8 %. The corresponding SE and SP values ranged from 81.7 % to 92.8 % and 91.9 % to 98.7 %, respectively. The AUC varied between 0.894 and 0.945. Image size had only a limited impact on diagnostic performance. The AI-based model correctly predicted EH/MIH localisation in 97.3 % of cases. For the detected lesions, segmentation was fully correct in 63.4 % of all cases and partially correct in 33.9 %. CONCLUSIONS: This study documented the promising diagnostic performance of an open-access AI tool in the detection and classification of EH/MIH in external images. CLINICAL SIGNIFICANCE: Externally validated AI-based diagnostic methods could facilitate the detection of EH/MIH lesions in dental photographs.

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.

Using patients' oral photographs for oral hygiene motivation promotes periodontal health: a prospective randomized controlled trial.

OBJECTIVES: The aim of this prospective, randomized, controlled, single-centered, examiner-blinded clinical trial was to evaluate the effectiveness of a personalized and visual oral health education program in addition to conventional oral hygiene education. MATERIALS AND METHODS: Fifty-six non-smoker, right-handed participants (aged 30.34 ± 11.46 years) without clinical signs of periodontitis were randomly grouped: the intervention group (n = 28) received a personalized visualized oral health education combined with conventional oral hygiene education, and the control group (n = 28) received conventional oral hygiene education only. All participants were assessed for improved periodontal parameters (PI, GI, BOP, and PPD) at baseline, first month, and third month. RESULTS: A significant reduction (p < 0.001) was observed in PI, GI, and BOP during two follow-up sessions compared to the baseline for the two groups. No differences were found for inter-group (p > 0.05) or intra-group (p = 1) comparison of PPD. PI (p = 0.012), GI (p = 0.032), and BOP (p = 0.024) scores were significantly reduced at the third-month follow-up assessment in the intervention group compared to the control group. CONCLUSIONS: Clinical outcomes of periodontal health were significantly enhanced by the personalized and visual oral health education program applied in this study compared to the conventional oral hygiene education program. CLINICAL RELEVANCE: Numerous studies reported additional interventions to the oral hygiene education program. However, we did not find any published studies investigating the role of patients' intra-oral photographs in oral care. This study's results demonstrated that a visually aided education program for oral hygiene motivation may help improve oral health. CLINICAL TRIAL REGISTRATION: Registration number is "NCT06316505" and date of registration is 18/03/2024.

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).

Study on the Association between anterior tooth Colour Preference based on the FAHP and individual factors.

BACKGROUND: The relationship between tooth colour and individual satisfaction in oral aesthetics has long been a topic of interest. In this study, we utilized the fuzzy analytic hierarchy process (FAHP) to investigate the impacts of sex and age on tooth colour preference. The findings of this study should provide a scientific basis for oral aesthetic practice. METHODS: In the current study, a random selection method was employed, and a survey was completed by 120 patients. To obtain tooth colour data, standard tooth colour charts were used. Smile photos were taken as template images using a single-lens reflex camera. The FAHP was utilized to conduct a weight analysis of tooth colour preferences among patients of different sexes and age groups. RESULTS: There were significant differences in tooth colour preference based on sex and age. Men tend to prefer the B1 colour, while women may prioritize the aesthetic effects of other colours. Additionally, as patients age, their preferences for tooth colour become more diverse. These findings offer valuable insights for oral aesthetics practitioners, enabling them to better address the aesthetic needs of patients across different sexes and ages. This knowledge can aid in the development of more personalized treatment plans that align with patients' expectations. CONCLUSION: In this study, we utilized scientific analysis methods to quantify the popularity of different tooth colours among various groups of people. By doing so, we established a scientific foundation for clinical practice. The findings of this study offer valuable insights for oral aesthetic research, enhancing our understanding of tooth colour. Additionally, these findings have practical applications in the field of oral medicine, potentially improving patients' quality of life and overall oral health.

Advancements in diagnosing oral potentially malignant disorders: leveraging Vision transformers for multi-class detection.

OBJECTIVES: Diagnosing oral potentially malignant disorders (OPMD) is critical to prevent oral cancer. This study aims to automatically detect and classify the most common pre-malignant oral lesions, such as leukoplakia and oral lichen planus (OLP), and distinguish them from oral squamous cell carcinomas (OSCC) and healthy oral mucosa on clinical photographs using vision transformers. METHODS: 4,161 photographs of healthy mucosa, leukoplakia, OLP, and OSCC were included. Findings were annotated pixel-wise and reviewed by three clinicians. The photographs were divided into 3,337 for training and validation and 824 for testing. The training and validation images were further divided into five folds with stratification. A Mask R-CNN with a Swin Transformer was trained five times with cross-validation, and the held-out test split was used to evaluate the model performance. The precision, F1-score, sensitivity, specificity, and accuracy were calculated. The area under the receiver operating characteristics curve (AUC) and the confusion matrix of the most effective model were presented. RESULTS: The detection of OSCC with the employed model yielded an F1 of 0.852 and AUC of 0.974. The detection of OLP had an F1 of 0.825 and AUC of 0.948. For leukoplakia the F1 was 0.796 and the AUC was 0.938. CONCLUSIONS: OSCC were effectively detected with the employed model, whereas the detection of OLP and leukoplakia was moderately effective. CLINICAL RELEVANCE: Oral cancer is often detected in advanced stages. The demonstrated technology may support the detection and observation of OPMD to lower the disease burden and identify malignant oral cavity lesions earlier.

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.

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.

[A two-dimensional photographic and three-dimensional digital dental model comparative analysis in maxillary anterior teeth].

Objective: To assess the accuracy of two-dimensional (2D) photographs in measuring esthetic parameters of the maxillary anterior teeth by comparing them with measurements obtained from three-dimensional (3D) dental models. Methods: A total of one hundred volunteers (49 males, 51 females, aged 18-23 years) were recruited from School and Hospital of Stomatology, Wuhan University from January to February 2024. 3D digital models of their dentitions were obtained using an intraoral scanner, and standardized frontal 2D intraoral photographs were captured with a digital camera. The lengths, widths and width/length ratio of the bilateral incisors, lateral incisors and canines were measured on both the 3D digital models and the 2D intraoral photographs. The width ratios of adjacent maxillary anterior were also calculated on the 2D intraoral photographs and the frontal view of 3D digital models. Results: The widths of lateral incisors [(5.85±0.60) mm] and canines [(4.73±0.71) mm] and the lengths of canines [(8.72±0.96) mm] in the 2D intraoral photographs were significantly lower than those in 3D digital models [(6.65±0.59), (7.76±0.60), (8.90±0.86) mm] (t=-18.24, P<0.001; t=-54.43, P<0.001; t=-4.40, P<0.001), while there were no significant differences in the lengths and widths of the other teeth (P>0.05). The width/length ratios measured from the 2D intraoral photographs for the lateral incisors and canines (0.74±0.08, 0.55±0.08) were significantly lower than those measured in the 3D digital models (0.84±0.09, 0.88±0.09) (t=-19.68, P<0.001; t=-50.21, P<0.001), and the width/length ratio of the central incisors showed no significant difference between the two groups (P>0.05). The width ratios of canines/lateral incisors and lateral incisors/central incisors measured on the 2D intraoral photographs (0.72±0.06, 0.85±0.11) were significantly smaller than those measured in the frontal view of 3D digital models (0.75±0.06, 0.89±0.11) (t=-9.31, P<0.001; t=-6.58, P<0.001). Conclusions: There is a difference between 2D and 3D measurement results of teeth in the esthetic area and the magnitude of the difference varies with their position in the dental arch. When analyzing the measurement of the anterior teeth, it is necessary to choose the appropriate method according to the target tooth position.

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.

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.

Parental use and acceptance of an accessible, commercially available intraoral camera for teledentistry in their children.

PURPOSE: The potential of combining teledentistry and engaging parents as underutilised resources to monitor paediatric dental health was emphasised during the COVID-19 pandemic and remains underexplored. This study aims to assess parental acceptance and use of a commercially available intraoral camera (IOC) for effective remote monitoring. METHODS: 47 child-parent dyads, where the parent was the main caregiver and the child was treated under general anaesthesia for early childhood caries, were recruited. Caregivers were trained to image their child's teeth on a commercially available IOC. Subsequently, submitted images were reviewed asynchronously by dentists for image quality, presence of dislodged fillings, abscesses, cavitation, and oral hygiene. Post-surgery monitoring was performed using teledentistry at 1 and 2 months and in-person at 4 months. A modified Telehealth Usability Questionnaire (TUQ) was used to record caregiver acceptance for study procedures. RESULTS: A mean TUQ of 6.09 out of 7 was scored by caregivers. Caregiver-reported issues were limited to problems with technique and child uncooperativeness. The number of clear images during the second teledentistry review was improved compared to the first (p = 0.007). 68% of children liked having images of their teeth taken. CONCLUSION: This study supports the feasibility of using an IOC as a clinically appropriate avenue for teledentistry with a high level of caregiver-child acceptance.

Exploring Digital Gingival Shade Matching in Students.

PURPOSE: To study the degree of accuracy in gingival shade matching of undergraduate students using a computer application. MATERIALS AND METHODS: In total, 76 undergraduate dental students' gingival shade selection abilities were evaluated using an in-house developed computer application. A total of 15 intraoral gingival photographs and 21 pink gingival color porcelain samples were used. The environmental conditions were standardized, and no time limit was set for answering in the computer application. RESULTS: Fourteen gingival color samples (66.6%) were not useful for representing the studied gingival shades. Not all natural gingival colors studied were represented within the 50.50% acceptability limits of the pink samples. There were no statistically significant differences between men and women in terms of "hit" percentages. The highest correlation coefficient (in absolute value) was for the L* coordinate (the darker the gingiva in the picture, the higher the hit rate for choosing the "ideal" shade tab); however, none of the linear correlation coefficients were statistically significant. CONCLUSIONS: Not all colors provided in the pink ceramic system were useful for subjective gingival selection. There were no statistically significant differences between male and female dental students in gingival color perception. The L* coordinate was the only one that influenced the correct perception of gingival color by dental students, and it did so more in women than in men.

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.

Photography or videography? A paradigm shift in dental clinical documentation.

Clinical documentation is critical in dental practices. Its influence reaches beyond diagnosis and treatment planning to patient education and evidence-based research. Historically, this documentation has relied mainly on photographic recordings. However, in the present era of rapid technical breakthroughs, a paradigm shift has occurred from photography to videography, driven by the transition from digital single-lens reflex (DSLR) cameras to mirrorless systems, which provide improved video capabilities. This article explores the technological journey from dental photography to videography, highlighting the need for revised and standardized clinical documenting methods to accommodate this changing landscape. It also includes a complete guide to maximizing the capabilities of mirrorless cameras and green screen technologies for the creation of high-quality video content. The essential element of protecting data privacy and security in the midst of these developments is also explored, providing a comprehensive view of the paradigm shift in dental clinical documentation.