Intraoral Photographs Research Articles

Automatic 3D Teeth Reconstruction From Five Intra-Oral Photos Using Parametric Teeth Model.

Orthodontic treatment is a lengthy process that requires regular in-person dental monitoring, making remote dental monitoring a viable alternative when face-to-face consultation is not possible. In this study, we propose an improved 3D teeth reconstruction framework that automatically restores the shape, arrangement, and dental occlusion of upper and lower teeth from five intra-oral photographs to aid orthodontists in visualizing the condition of patients in virtual consultations. The framework comprises a parametric model that leverages statistical shape modeling to describe the shape and arrangement of teeth, a modified U-net that extracts teeth contours from intra-oral images, and an iterative process that alternates between finding point correspondences and optimizing a compound loss function to fit the parametric teeth model to predicted teeth contours. We perform a five-fold cross-validation on a dataset of 95 orthodontic cases and report an average Chamfer distance of 1.0121 mm2 and an average Dice similarity coefficient of 0.7672 on all the test samples in the cross-validation, demonstrating a significant improvement compared with the previous work. Our teeth reconstruction framework provides a feasible solution for visualizing 3D teeth models in remote orthodontic consultations.

Comparison of the perceived image quality of intraoral orthodontic photographs taken with DSLR camera and mobile phone camera: A double-blinded prospective study

Clinical orthodontic photography is a vital skill that every orthodontist should master to record the patients’ details and to permit the orthodontist to carefully plan, monitor and execute the treatment. With the advancement of technology, some clinicians opt to take intraoral photographs with their mobile phone rather than DSLR camera. Hence, this study aimed to answer one main question: whether there was any significant difference in the perceived quality image between intraoral photos taken with a mobile phone and a standard DSLR camera. The cameras used were a DSLR (Nikon D300s with AF-S Micro NIKKOR 105mm lens and NIKON R1C1 Twin Flash) and Mobile Phone (Apple I-Phone 11 with Selfie Ring Light). Assessment of 20 sets of intraoral photographs (100 individual images) by five IIUM orthodontists using a perceived quality Likert scale of Zero (0) to Ten (10). The assessors and the lead investigator were blinded to the source of the photographs. Reliability was evaluated using a test-retest method on 4 sets of intraoral photographs (20 individual images), a few weeks after their initial assessment. There was no significant difference (p=0.35) in perceived quality of intraoral photographs taken between DSLR and mobile phone, with the mean value of 7.34 and 7.12 respectively. Reliability was good (ICC=0.549). This prospective study showed that there was no statistical difference between the perceived quality of intraoral orthodontic photographs taken with a DSLR camera and a Mobile Phone camera.

Tonsillar healing membrane characteristic for tonsillectomy using combined cold dissection and bipolar electrocautery

PurposeTo evaluate post-tonsillectomy healing process using combined cold dissection and bipolar cautery for hemostasis.MethodsThis case series included patients for whom tonsillectomy was done by cold dissection and limited the bipolar cautery for hemostasis and dealing with the lower pole only. Saline irrigation was used for the tonsillar bed after tonsillectomy and a wet saline-moistened pack was placed into the tonsil fossa. Intra-oral digital photographs were reviewed postoperative, at 3rd, 5th, 7th, 9th, 11th, 13th, 15th, and 21st day after surgery. Patients were asked to register their postoperative pain using a standardized visual analog scale. The time of separation of the healing membrane (HM) and any blood clot and/or bleeding surface were recorded.ResultsAmong included 114 patients, the HM color was white without odor in all cases. Extension of the HM was limited to the tonsillar bed in 112 patients (98%) and exceeded in 2 patients (2%). The mean time for HM separation was 8.8 ± 1.1 days (range = 7–14) with a significantly longer period of separation in females (p = 0.0008). There was no significant correlation between the time of the HM separation and the age of the patients (P = 0.9).ConclusionCold dissection tonsillectomy with limited hot tools usage for hemostasis by bipolar cautery with frequent saline wash leads to odorless HM and a good healing process with an average separation of the HM and so less pain and post-tonsillectomy bleeding.

Efficiency of oral keratinized gingiva detection and measurement based on convolutional neural network.

With recent advances in artificial intelligence, the use of this technology has begun to facilitate comprehensive tissue evaluation and planning of interventions. This study aimed to assess different convolutional neural networks (CNN) in deep learning algorithms to detect keratinized gingiva based on intraoral photos and evaluate the ability of networks to measure keratinized gingiva width. Six hundred of 1200 photographs taken before and after applying a disclosing agent were used to compare the neural networks in segmenting the keratinized gingiva. Segmentation performances of networks were evaluated using accuracy, intersection over union, and F1 score. Keratinized gingiva width from a reference point was measured from ground truth images and compared with the measurements of clinicians and the DeepLab image that was generated from the ResNet50 model. The effect of measurement operators, phenotype, and jaw on differences in measurements was evaluated by three-factor mixed-design analysis of variance (ANOVA). Among the compared networks, ResNet50 distinguished keratinized gingiva at the highest accuracy rate of 91.4%. The measurements between deep learning and clinicians were in excellent agreement according to jaw and phenotype. When analyzing the influence of the measurement operators, phenotype, and jaw on the measurements performed according to the ground truth, there were statistically significant differences in measurement operators and jaw (p<0.05). Automated keratinized gingiva segmentation with the ResNet50 model might be a feasible method for assisting professionals. The measurement results promise a potentially high performance of the model as it requires less time and experience. With recent advances in artificial intelligence (AI), it is now possible to use this technology to evaluate tissues and plan medical procedures thoroughly. This study focused on testing different AI models, specifically CNN, to identify and measure a specific type of gum tissue called keratinized gingiva using photos taken inside the mouth. Out of 1200 photos, 600 were used in the study to compare the performance of different CNN in identifying gingival tissue. The accuracy and effectiveness of these models were measured and compared to human clinician ratings. The study found that the ResNet50 model was the most accurate, correctly identifying gingival tissue 91.4% of the time. When the AI model and clinicians' measurements of gum tissue width were compared, the results were very similar, especially when accounting for different jaws and gum structures. The study also analyzed the effect of various factors on the measurements and found significant differences based on who took the measurements and jaw type. In conclusion, using the ResNet50 model to identify and measure gum tissue automatically could be a practical tool for dental professionals, saving time and requiring less expertise.

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

ObjectivesThe 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 methodsFifty-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.ResultsA 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.ConclusionsClinical 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 relevanceNumerous 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 registrationRegistration number is “NCT06316505” and date of registration is 18/03/2024.

SPATIAL DISTRIBUTION OF MOLAR INCISOR HYPOMINERALIZATION (MIH) OPACITIES IN FIRST PERMANENT MOLARS

Aim: To evaluate the spatial distribution of MIH opacities in first permanent molars (FPM). Materials and methods: an analysis of intraoral photographs of FPM with demarcated MIH opacities was conducted. The presence of opacity was computed in a digital matrix, discriminating the anatomical regions of the FPM surfaces. The frequencies of distribution of the opacities were descriptively analyzed through 227 FPM digital images of 89 children built in GIMP and Python and by Spearman correlation (= 0,05). Results: the occlusal surface was the most affected one (94% to 100%). In the upper FPM, the palatine surface was the second most affected one (84%-91%). In the lower FPM, the vestibular surface was the second most affected one (85%-80%). A similar pattern of opacity distribution was observed in the contralateral teeth. On smooth surfaces, opacities were more frequent in the regions closer to the occlusal surface than to the cervical one. Conclusion: MIH opacities were mostly present on occlusal, vestibular, and lingual/palatine surfaces, respectively. There is a possibility that the occurrences are in accordance with the chronology of dental formation. Keywords: Molar Incisor Hypomineralization, First Permanent Molars, Dental photography, intraoral photography

Long-term Outcomes of Traditional Braces versus Invisalign in Orthodontic Treatment

ABSTRACT Objective: This research targets to compare the longstanding outcomes of traditional braces versus Invisalign in orthodontic treatment, focusing on dental alignment, occlusal stability, patient satisfaction, and treatment duration. Methods: A retrospective cohort research with 200 subjects was piloted. One hundred subjects received traditional braces, while the other 100 received Invisalign. Dental models, cephalometric radiographs, and intraoral photographs were obtained at baseline, post-treatment, and at 1, 2, and 5 years follow-up. Patient satisfaction surveys were administered at each follow-up visit. Results: Both traditional braces and Invisalign effectively improved dental alignment and occlusal stability over the 5-year follow-up period. Patient satisfaction scores were consistently higher in the Invisalign group compared to the traditional braces group. However, there were no significant differences in treatment duration between the two groups. Conclusion: Invisalign may offer comparable or superior longstanding outcomes compared to traditional braces in orthodontic treatment, with higher patient satisfaction levels. These findings support the use of Invisalign as a viable alternative to traditional braces, particularly for subjects seeking esthetic and convenient orthodontic solutions.

Accessories in clear aligner therapy: Laypeople's expectations for comfort and satisfaction.

This study explored the layperson's perception of comfort, satisfaction, and willingness to use various accessories in clear aligner therapy. A total of 267 people analyzed standardized intraoral photographs of a female model in orthodontic treatment using: 1) only clear aligner (CA), the control group; 2) clear aligner+attachments (AT); 3) clear aligners+Cl II elastics (EL); 4) clear aligner+hybrid treatment with esthetic braces (HEB); 5) Clear aligner+hybrid treatment with metallic braces (HMB); 6) clear aligner+mini-implants (MI); 7) clear aligner+mini-implants and elastics for intrusion (MIE). In addition, a social media questionnaire was distributed to assess the willingness to undergo orthodontic treatment with various accessories. There was a significant difference between CA and all the other groups (P<0.001), with CA being considered more comfortable and providing greater satisfaction compared to other accessories. Moreover, AT showed a significant difference in reducing treatment time compared to other groups. The CA was the most comfortable, exhibiting a higher satisfaction rate and a greater willingness to use it. The AT therapy was perceived as more comfortable and was associated with higher satisfaction and a greater likelihood of use, especially if it resulted in reduced treatment time. On the other hand, the participants reported that the HMB, MI, and MIE accessories were less comfortable.

Is there a difference in quality of photographs obtained using smartphones, compared to the photographs obtained via digital single-lens reflex camera?

PurposeThe purpose of this study was to compare the quality of photographs obtained with three different cameras: I-phone, Samsung, and DSLR, as assessed by Oral and Maxillofacial Surgeons (OMSs). MethodsThis was an anonymous online survey study. The study population consisted of OMSs in New Jersey, New York, Pennsylvania, and Massachusetts who were members of their state societies. Primary outcome variable was ratings on the quality of extraoral and intraoral photographs obtained with three different cameras. Predictor variable was type of camera used to obtain the photographs: I-Phone, Samsung, and DSLR camera. Descriptive statistics were computed. Spearman test was utilized to analyze correlation between the outcome variables. ResultsMajority of respondents were from full time private practice (n = 84, 77.06%), followed by full time academia (n = 12, 11.01%). Spearman test revealed negligible correlation between extraoral and intraoral photographs on the best quality rating (0.15, p=0.13). Negligible correlation between extraoral and intraoral photographs were noted on the second best (0.15, p=0.11), and least quality rating (0.09, p=0.32). ConclusionStudy results revealed that there is no difference between the quality of photographs obtained using smartphones, compared to the photographs obtained via DSLR camera. Future discussions on an ideal application on smartphones would allow for consent and transfer of high-quality photographs via a secure messaging system between healthcare providers to address ethical concerns of confidentiality.

Aesthetic assessment of maxillary lateral and canine implant-supported crowns by laypersons and orthodontists.

The objectives of this study were to evaluate how laypersons and orthodontists evaluate and rank aesthetic parameters of an implant-supported crown (ISC) on the canine position (ISC-C) and lateral position (ISC-L). A digital survey of 11 cases, 5 ISC-C, 5 ISC-L and 1 control case without ISC, was distributed to 207 laypersons and 296 orthodontists. All cases included one extraoral photograph and three intraoral photographs. The respondents were asked to identify the ISC and to evaluate the aesthetic parameters regarding colour of the implant (CI), shape of the implant (SI) and gingival colour around ISC (GCI). Differences within and between the groups were tested using Chi-2-test and Independent-Samples t-test. All invited laypersons and 184 orthodontists (62% response rate) answered the survey. Orthodontists (89%) more correctly identified the ISC, regardless of its position, than laypersons (50%) (p < 0.001). Both laypersons (54%) and orthodontists (23%) rated higher proportions of acceptance of CI, SI and GCI in favour for the ISC-L than ISC-C (laypersons: 40%, orthodontists: 10%) (p < 0.001). Assessing each parameter separately, orthodontists rated higher for ISC-L, compared to the ISC-C (p < 0.001). In general, laypersons and orthodontist ranked tooth colour (mean, standard deviation [SD]:8.0,1.5 and 9.0, 1.0) and tooth shape (mean, SD: 8.0, 1.7 and 8.8, 1.2) as aesthetically higher than the gingival colour (mean, SD: 7.2, 2.2 and 8.0, 1.7) (p > 0.001). Conclusion: Laypersons and orthodontists consider the ISC-L as aesthetically more preferable, compared to the ISC-C.

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.

Evaluation of Dental Plaque Area with Artificial Intelligence Model

Objectives: This study aims to assess the diagnostic accuracy of an artificial intelligence (AI) system employing deep learning for identifying dental plaque, utilizing a dataset comprising photographs of permanent teeth. Materials and Methods: In this study, photographs of 168 teeth belonging to 20 patients aged between 10 and 15 years, who met our criteria, were included. Intraoral photographs were taken of the patients in two stages, before and after the application of the plaque staining agent. To train the AI system to identify plaque on teeth with dental plaque that is not discolored, plaque and teeth were marked on photos with exposed dental plaque. One hundred forty teeth were used to construct the training group, while 28 teeth were used to create the test group. Another dentist reviewed images of teeth with dental plaque that was not discolored, and the effectiveness of AI in detecting plaque was evaluated using pertinent performance indicators. To compare the AI model and the dentist’s evaluation outcomes, the mean intersection over union (IoU) values were evaluated by the Wilcoxon test. Results: The AI system showed higher performance in our study with a precision of 82% accuracy, 84% sensitivity, 83% F1 score, 87% accuracy, and 89% specificity in plaque detection. The area under the curve (AUC) value was found to be 0.922, and the IoU value was 76%. Subsequently, the dentist’s plaque diagnosis performance was also evaluated. The IoU value was 0.71, and the AUC was 0.833. The AI model showed statistically significantly higher performance than the dentist (P &lt; 0.05). Conclusions: The AI algorithm that we developed has achieved promising results and demonstrated clinically acceptable performance in detecting dental plaque compared to a dentist.

Artificial Intelligence (AI)-Based Detection of Anaemia Using the Clinical Appearance of the Gingiva.

Background and aim Millions suffer from anaemiaworldwide, and systemic disorders like anaemia harm oral health. Anaemia is linked to periodontitis as certain inflammatory cytokines produced during periodontal inflammation candepress erythropoietin productionleading to the development of anemia. Thus, detecting and treating it is crucial to tooth health. Hence, this study aimed to evaluate three different machine-learning approaches for the automated detection of anaemia using clinical intraoral pictures of a patient's gingiva. Methodology Orange was employed with squeeze net embedding models for machine learning. Using 300 intraoral clinical photographs of patients' gingiva, logistic regression, neural network, and naive Bayes were trained and tested for prediction and detection. Accuracy was measured using a confusion matrix and receiver operating characteristic (ROC) curve. Results In the present study, three convolutional neural network (CNN)-embedded machine-learning algorithms detected and predicted anaemia. For anaemia identification, naive Bayes had an area under curve (AUC) of 0.77, random forest plot had an AUV of 0.78, and logistic regression had 0.85. Thus, the three machine learning methods detected anaemia with 77%, 78%, and 85% accuracy, respectively. Conclusion Using artificial intelligence (AI) with clinical intraoral gingiva images can accurately predict and detect anaemia. These findings need to be confirmed with larger samples and additional imaging modalities.

Evaluation of near-infrared digital imaging transillumination compared with bitewing radiography for proximal caries detection in children.

Limitations in traditional caries detection tools have driven the development of alternatives methods, focused on the early lesion detection such as near-infrared digital imaging transillumination (NIDIT). The aim of this study was to evaluate the performance of NIDIT compared with bitewing radiography (BWR) in the detection of interproximal carious lesions in children. A retrospective audit of data from children who had NIDIT, BWR and intraoral photographs was conducted. Carious lesions were scored on a tooth surface level with BWR acting as the primary reference for comparison. Accuracy was determined using multi-class area under the curve (AUC), and correlation was determined using Fleiss' Kappa. Data from 499 tooth surfaces involving 44 children were included in this study. The average age across the participants was 86 months (~7 years) with an average dmft (decayed, missing and filled teeth in primary dentition) of 5.29. Multi-class AUC comparing NIDIT to BWR was 0.70. The correlation between NIDIT and BWR was moderate (0.43), whereas the correlation between photographic examination and BWR was 0.30, which is fair. When compared to BWR, NIDIT showed a high specificity but a low sensitivity for proximal caries detection in primary teeth.

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

BackgroundDeep 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.MethodsA 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.ResultsAt 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.ConclusionsThe 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.

Establishment and evaluation of a deep learning-based tooth wear severity grading system using intraoral photographs

Background/purposeArtificial 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 methodsThe study included 388 intraoral photographs. A tooth segmentation model was first established using the Mask R-CNN architecture, which incorporated U-Net and SGE attention mechanisms. Subsequently, 2774 individual tooth images output from the segmentation model were included into the classification task, labeled and randomized into training, validation, and test sets with 6.0:2.0:2.0 ratio. A vision transformer model optimized using a mask mechanism was constructed for TW degree classification. The models were evaluated using the accuracy, precision, recall, and F1-score metrics. The time required for AI analysis was calculated. ResultsThe accuracy of the tooth segmentation model was 0.95. The average accuracy, precision, recall, and F1-score in the classification task were 0.93, 0.91, 0.88, and 0.89, respectively. The F1-score differed in different grades (0.97 for grade 0, 0.90 for grade 1, 0.88 for grade 2, and 0.82 for grade 3). No significant difference was observed in the accuracy between different surfaces. The AI system reduced the time required to grade an individual tooth surface to 0.07 s, compared to the 2.67 s required by clinicians. ConclusionThe developed system provides superior accuracy and efficiency in determining TW degree using intraoral photographs. It might assist clinicians in the decision-making for TW treatment and help patients perform self-assessments and disease follow-ups.

Effects of extraoral storage time on autologous gingival graft early healing: A split-mouth randomized study.

Despite the established use of palatal tissue grafts for mucogingival procedures, there are no studies on the effect of extraoral storage time on graft outcomes. This prospective split-mouth randomized experimental clinical trial aimed to assess whether gingival graft extraoral storage time affects graft healing. Standardized grafts were harvested from the palate and stored extraorally for 2 (Control) or 40 (Test) minutes before being placed at recipient beds. Intraoral scans, clinical photographs, and tissue blood perfusion were obtained preoperatively, postoperatively, and at follow-up visits (Days 2 (PO2), 3 (PO3), 7 (PO7), and 14 (PO14)). Healing Score Index (HSI) and wound fluid (WF) biomarkers (angiogenin, IL-6, IL-8 (CXCL8), IL-33, VEGF-A, and ENA-78 (CXCL5)) were also assessed. Twenty-three participants completed all study visits. Extraoral storage time was 2.3 ± 1.1 min and 42.8 ± 3.4 min for C and T grafts, respectively (p < .0001). Recipient beds remained open for 21.4 ± 1.7 min. No graft underwent necrosis or failed to heal by PO14. Minimal volumetric changes were observed, without significant intergroup differences (p ≥ .11). Graft perfusion initially decreased post-harvesting before peaking on PO7 for both C and T grafts, with no significant intergroup differences (p ≥ .14). HSI values progressively increased, with no significant intergroup differences (p ≥ .22). WF analysis revealed detectable levels for all biomarkers tested, without significant intergroup differences (p ≥ .23). Extraoral storage time of 40 min has neither statistically significant nor clinically discernible effects on autologous graft revascularization, early healing, or survival, as determined by physiological, wound healing, and molecular parameters.

Incidence, severity, and risk factors for white spot lesions in adolescent patients treated with clear aligners.

This study was aimed to clarify the incidence, severity, and clinical risk factors for white spot lesions (WSLs) in adolescent patients treated with clear aligners. Pre-treatment and post-treatment intraoral photographs of 203 adolescent patients undergoing clear aligner therapy were retrospectively evaluated to assess the occurrence and severity of WSLs. Information on patients' general oral condition and orthodontic treatment was collected from clinical medical documents, retrospective questionnaires, and ClinCheck® software. Independent risk factors and model performance were determined by multivariate logistic regression and receiver operating characteristic curve analysis. Thirty-five percent of adolescent patients developed WSLs during clear aligner treatment. Logistic regression analysis revealed that the presence of WSLs before treatment (OR 2.484, 95% CI 1.245-4.957), frequency of drinking carbonated beverages (OR 1.508, 95% CI 1.045-2.177), and number of anterior attachments (OR 2.192, 95% CI 1.502-3.198) were risk factors for the occurrence of WSLs in adolescent patients treated with clear aligners (P < .05), whereas the number of times they brushed each day (OR 0.656, 95% CI 0.454-0.947) and frequency of aligner cleaning after eating while wearing them (OR 0.611, 95% CI 0.433-0.861) were protective factors against WSLs (P < .05). The incidence of WSLs was high in adolescent patients treated with clear aligners. Few brushings each day, pre-treatment WSLs, a high frequency of drinking carbonated beverages, a low frequency of aligner cleaning after eating while wearing them, and a high number of anterior attachments are strongly associated with the development of WSLs in adolescent patients treated with clear aligners.

Applying artificial intelligence to predict the outcome of orthodontic treatment

Objectives: The study aimed to train an algorithm to predict facial and dental outcomes following orthodontic treatment using artificial intelligence (AI). In addition, the accuracy of the algorithm was evaluated by four distinct groups of evaluators. Material and Methods: The algorithm was trained using pre-treatment and post-treatment frontal smiling and intraoral photographs of 50 bimaxillary patients who underwent all first bicuspid extraction and orthodontic treatment with fixed appliances. A questionnaire was created through Google form and it included 10 actual post-treatment and AI-predicted post-treatment images. The accuracy and acceptability of the AI-predicted outcomes were analyzed by four groups of 140 evaluators (35 orthodontists, 35 oral maxillofacial surgeons, 35 other specialty dentists, and 35 laypersons). Results: The Style-based Generative Adversarial Network-2 algorithm used in this study proved effective in predicting post-treatment outcomes using pre-treatment frontal facial photographs of bimaxillary patients who underwent extraction of all first bicuspids as part of their treatment regimen. The responses from the four different groups of evaluators varied. Laypersons exhibited greater acceptance of the AI-predicted images, whereas oral maxillofacial surgeons showed the least agreement. The base of the nose and the chin demonstrated the most accurate predictions, while gingival visibility and the upper lip-to-teeth relationship exhibited the least prediction accuracy. Conclusion: The outcomes underscore the potential of the method, with a majority of evaluators finding predictions made by the AI algorithm to be generally reliable. Nonetheless, further research is warranted to address constraints such as image tonicity and the proportional accuracy of the predicted images.

Exploring Midline Asymmetry: A Comprehensive Study Among Adolescents in the North Bihar Population

Objectives In the contemporary era, achieving a sense of equilibrium is a paramount concern, and a visually pleasing smile is intricately linked to both professional success and social acceptance. Esthetics, with a specific focus on Oro-facial aesthetics, emerges as a primary motivation for individuals seeking dental treatment. Nevertheless, there exists an optimal and aesthetically pleasing range of dental midline deviation. The current study aimed to ascertain the prevalence of dental midline shifting and the average extent of midline discrepancy among adolescents (aged 13-17) from North Bihar population. Material &amp; Methods Two hundred subjects participated in the study, with 100 males and 100 females selected based on predetermined inclusion criteria. Intraoral frontal occlusion photographs were captured for each participant, and subsequent analysis was done utilizing Digimizer software. Statistical analysis was applied to the linear measurement data obtained through photographic evaluation. Results The study’s findings revealed a noteworthy proportion of subjects displaying deviations between upper and lower midlines and anterior teeth midlines, falling within the range of 0.03-3.80 mm. Conclusion The prevalence of these deviations was observed to be higher in males compared to females, and this difference was statistically significant.