Teeth and jawbone are mineralized tissues with a strong capacity to incorporate and retain small exogenous molecules, e.g., pharmaceuticals. Their limited metabolic turnover provides a stable record of exposure for a long time after administration. These features make them well-suited for spatially resolved chemical analysis. This study presents the first proof-of-concept application of matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) to visualize both endogenous metabolites and clinically relevant pharmaceuticals in situ within human dental hard tissues and jawbone. Human teeth, jawbone, and teeth with adjacent jawbone from four patients were sectioned and analyzed using high-resolution Orbitrap MSI, followed by metabolite annotation in METASPACE and confirmatory analysis. MSI successfully mapped endogenous metabolites such as arginine, phosphatidylcholine, and lysophosphatidylcholine, and revealed distinct distribution patterns of the pharmaceuticals lidocaine, bupivacaine, and chlorhexidine. Injected analgesics were predominantly localized within the tissues, whereas topically applied chlorhexidine was confined to the surface. The study demonstrates that MSI can detect retained pharmaceuticals in mineralized tissues and differentiate their spatial patterns according to route of administration. The findings highlight the value of MSI as a complement to conventional LC-MS by providing spatial context that cannot be obtained from homogenized samples. MSI can reveal the spatial distribution and accumulation of both endogenous metabolites and exogenous drugs within dental hard tissues and jawbone. Such spatial insight is valuable for understanding how pharmaceuticals interact with mineralized tissues, where they may accumulate or undergo biotransformation, and may ultimately support the development and optimization of therapeutics for dental infections such as osteomyelitis and periodontitis.

Introduction: Sealing of occlusal dentin caries has shown promise in studies, but long-term outcomes remain inconclusive. This RCT aimed to investigate the potential of postponing restorative interventions of manifest occlusal caries by sealing. Methods: After randomization (ratio 2:1), 341 resin sealings and 152 composite restorations in 493 patients (6–17 years) were performed by 66 dentists in nine Danish municipalities. All lesions were pre-designated to require restorative treatment by the treating dentists. The treatments were controlled annually both clinically and radiographically. The primary objectives, assessed at different observation intervals, were to analyze the survival of sealing until replacement by restoration, the survival of sealing and restoration until retreatment, caries progression beneath sealing and restoration, and the longevity of repaired versus unrepaired sealing until replacement by restoration. The secondary objectives identified factors influencing survival of sealings and restorations. Chi-square/Kaplan-Meier/Cox-regression were used for statistical analyses. Results: After 11 years, dropout rate was 10%, and 22% of sealings were repaired/renewed. 58% of sealings and 81% of restorations remained sealed/restored until completion due to age 18 or primary caries. Additionally, 33% of sealings were replaced by restorations, and 12% of restorations were repaired/replaced (p-values<0.001). No endodontics were performed. The mean annual failure rates were 4%–7% for sealings and 1% for restorations. The survival of sealings were affected by various predictor variables across the outcomes and observation intervals. The survival rate of repaired/renewed sealings was comparable to that of new sealings (p=0.96). Conclusions: This long-term study provided a comprehensive overview regarding reasons and time for retreatments of occlusal sealings and restorations. The results indicate that sealing is an effective treatment for occlusal enamel and dentin caries.

BACKGROUND:Regular follow-up after dental treatment is essential to prevent the development and progression of new caries lesions in children. However, there is no consensus on the optimal recall interval. AIM:The aim of the present clinical trial was to evaluate the effectiveness of two different recall intervals for high caries risk children. DESIGN:A total of 224 preschool children (aged 3-5 years) with at least one active caries lesion were selected. After treatment, children were randomized into two different groups according to the recall interval: every 4 months or every 8 months. A single trained and calibrated examiner re-evaluated participants according to their group. The primary outcome was caries lesion progression into dentine (ICDAS 5-6). Logistic regression analysis was used to evaluate the association between the independent variables and primary outcome (α = 5%). RESULTS:Children in the 8-month recall group were 2.5 times more likely to develop new cavitated caries lesions when compared to those in the 4-month recall group, over 30 months. CONCLUSION:High caries risk children on an 8-month recall schedule had a significantly higher risk of developing new cavitated caries lesions compared to those with a 4-month recall interval.

Aim This study evaluated the effect of a short, personalised training session on student performance in using an artificial intelligence (AI)-based platform for pulp exposure prediction before caries excavation and determined the required sample size for a further randomised controlled trial (RCT).Methodology Undergraduate dental students were randomly assigned to the experimental (training) group and the control (no training) group. The training group received a 1-h training session before undertaking the experiment, focusing on the uses, applications, and drawbacks of AI and carious lesion penetration depth. The theoretical presentation was followed by practical exercises and a quiz to check learning progress. Later, participants in both groups completed an experimental task involving 292 cases. They were asked to predict pulp exposure using an AI-based website. Sample size calculations determined the required sample size, with 80% power and an alpha of 5%.Results 18 participants were enrolled (9 in each group). The agreement between participants' decisions and AI predictions regarding the presence or absence of pulp exposure (agreeableness with AI) was higher in the training group compared to the control group (0.83 vs. 0.76). The training group had a slightly higher mean F1-score (0.63 vs. 0.62), accuracy (0.69 vs. 0.68), and sensitivity (0.63 vs. 0.59) than the control group. Based on the sample size calculation, at least 31 participants per group are needed for the future RCT.Conclusions The results support further investigation of customised training sessions prior to using an AI-based platform to assess their impact on dental students' agreement with AI predictions.Trial Registration ClinicalTrial.gov identifier: NCT05912361

This study focuses on the challenging problem of labeling a collection of objects with inherent morphological and positional dependencies, where instances may be missing or duplicated. We integrate principles of assignment theory in the design of a convolutional neural network to find the optimal label set given pairwise geometrical features extracted from the candidate objects. The objective function aims to minimize the distance between the one-hot encoded labels of the objects and the scores produced by the model, with added emphasis on the scores corresponding to the optimal assignment plan. We tested our solution in the dental domain on the task of finding the teeth labels given a set of candidate instances. The study database included 1200 dental casts of upper and lower jaws from 600 patients. The model reached identification accuracies of 0.952 and 0.968 for the lower and upper jaws, respectively. Moreover, we presented a solution for generating teeth candidates using a multi-step pipeline consisting of coarse and fine segmentations. The algorithm was tested on a database consisting of 600 dental casts, reaching an F1 score of 0.968.

Integrating artificial intelligence (AI) into medical and dental applications can be challenging due to clinicians’ distrust of computer predictions and the potential risks associated with erroneous outputs. We introduce the idea of using AI to trigger second opinions in cases where there is a disagreement between the clinician and the algorithm. By keeping the AI prediction hidden throughout the diagnostic process, we minimize the risks associated with distrust and erroneous predictions, relying solely on human predictions. The experiment involved 3 experienced dentists, 25 dental students, and 290 patients treated for advanced caries across 6 centers. We developed an AI model to predict pulp status following advanced caries treatment. Clinicians were asked to perform the same prediction without the assistance of the AI model. The second opinion framework was tested in a 1000-trial simulation. The average F1-score of the clinicians increased significantly from 0.586 to 0.645.

OBJECTIVE:To assess the agreement in detecting and monitoring occlusal caries over thirty months using conventional visual and radiographic assessment and an intraoral scanner system which supports automated caries scoring.METHODS:Ninety-one young participants aged 12-19 years were included in the study. All occlusal surfaces were examined visually, radiographically (when indicated), and scanned with the TRIOS 4 intraoral scanner. TRIOS Patient Monitoring software (vers. 2.3, 3Shape TRIOS A/S, Denmark) was used for automated caries detection on the 3D digital models.RESULTS:Fifty-five of the study participants were re-examined after 30-months. Significant differences regarding caries detection were found between the conventional methods and the automated caries scoring system (p < 0.01), with moderate positive percent agreement (49-61%) and high negative percent agreement (87-98%). All methods reported significant caries progression over the follow-up period (p < 0.01). However, the automated system showed significantly more caries progression than the other methods (p < 0.01).CONCLUSIONS:The software for automated caries detection and classification showed moderate positive agreement and strong negative agreement with the conventional methods considering both the baseline and the follow-up assessments. The automated caries scoring system detected significantly fewer caries lesions and tended to underestimate the caries severity. All methods indicated significant caries progression over the follow-up period, while the automated system detected more caries progression.CLINICAL SIGNIFICANCE:The TRIOS system supporting automated occlusal caries detection and classification can assist in detecting and monitoring occlusal caries on permanent teeth as a complementary tool to the conventional methods. However, the operator should be aware that the automated system shows a tendency to underestimate the caries presence and lesion severity.

Objectives: To evaluate the diagnostic performance of visual caries assessment on 3D dental models obtained using an intraoral scanner and to compare it with the performance of the clinical visual inspection.Methods: Fifty-three permanent posterior teeth scheduled for extraction were randomly selected and included in this study. One to three independent examination sites on the occlusal surface of each tooth were clinically inspected using International Caries Detection and Assessment System (ICDAS) criteria. Afterwards, the exam-ined teeth were scanned intraorally with a 3D intraoral scanner (TRIOS 4, 3Shape TRIOS A/S, Copenhagen, Denmark) using white and blue-violet light (415 nm wavelength) to capture the colour and fluorescence signal from the tissues. Six months after the clinical examination, the same examiner conducted the on-screen assessment of the obtained 3D digital dental models at the selected examination sites using modified ICDAS criteria. Both tooth colour and fluorescence texture with high resolution were assessed. Lastly, an independent examiner conducted the histological examination of all teeth after extraction. Using histology as the reference test, Sensitivity (SE), Specificity (SP), Accuracy (ACC), area under the Receiver Operating Characteristic (ROC) curve, and Spearman's correlation coefficient were calculated for the clinical and on-screen ICDAS assessments.Results: The ACC values of the evaluated methods varied between 0.59-0.79 for initial caries lesions and 0.77-0.99 for moderate-extensive caries lesions. Apart from SE values corresponding to caries in the inner half of enamel, no significant difference was observed between clinical visual inspection and on-screen assessment. In addition, no difference was found in the assessment of 3D models with tooth colour alone or supplemented with fluorescence for all the evaluated diagnostic measures.Conclusions: On-screen visual assessment of 3D digital dental models with tooth colour or fluorescence showed a similar diagnostic performance to the clinical visual inspection when detecting and classifying occlusal caries lesions on permanent teeth.Clinical significance: 3D intraoral scanning can aid the detection and classification of occlusal caries as part of patient screening and can potentially be used in remote caries assessment for clinical and research purposes.

ObjectiveThe principal aim of this randomized clinical trial (RCT) was to test the effectiveness in the prevention of Early Childhood Caries (ECC) through an educational intervention program with the use of a printed guide for pediatricians and parents both designed by pediatric dentists.Materials and methodsAfter ethical approval, the first step was to design the educational guides, which were based on the information obtained from a focus group with pediatricians (n = 3), phone interviews with mothers to toddlers' (n = 7), and the best evidence available about children's oral health. For the RCT, 309 parents with their 10-12 months old children were randomly allocated to either the intervention or the control group. Parents in the intervention group received oral health education from the pediatricians supported by the printed guides. Parents in both groups received an oral health kit with a toothbrush and toothpaste at the first visit as well as at each 6-month follow-up visit. After 18 months the children were evaluated using ICDAS criteria.ResultsAt baseline, data were available from 309 children (49.8% girls). The mean age of the children was of 10.8 months (SD = 0.8) and 69.3% had not had their teeth brushed with toothpaste. After 18 months, a total of 28 (22%) children in the intervention group and 44 (24%) in the control group were clinically examined. Regarding the number of tooth surfaces with caries lesions, the children in the intervention group had a mean of 6.50 (SD = 6.58) surfaces, while the children in the control group had a mean of 5.43 (SD = 4.74) surfaces with caries lesions. This difference was not significant (p = 0.460).ConclusionThe RCT showed no effectiveness in caries-progression control. Despite this result, this study managed to identify barriers that do not allow pediatricians from offering parents adequate oral health recommendations. With this learning, it is possible to work on collaborative programs with pediatricians that over time likely will increase dental health by controlling for ECC.

Objectives: To determine how daily consumption of a lozenge combining arginine and two probiotic strains affects the Relative Risk Reduction (RRR) in children regarding dental caries transitions and lesion activity at tooth surface level during 10-12 months. Methods: A total of 21,888 tooth surfaces in 288 children were examined. The intervention group (n = 141) received a lozenge containing 2% arginine, Lacticaseibacillus rhoosus, LGG & REG; (DSM33156), and Lactobacillus paracasei subsp. paracasei, L. CASEI 431 & REG; (DSM33451). The placebo group (n = 147) received a placebo lozenge. Both groups received 1,450 ppm F- toothpaste. Primary canines, molars, and first permanent molars were examined clinically (ICDAS0-6) and radiographically (R0-6) at baseline and follow-up. Sealed, filled, and missing surfaces were also included. Caries activity was computed as a sum of each caries lesion's location, color, texture, cavitation, and gingival bleeding. RRRs were computed with cluster effect on surface level. ICH-GCP was fol-lowed, including external monitoring. Results: A total of 19,950 surfaces were analyzed after excluding 1,938 tooth surfaces. No statistically significant differences were found between the groups. The RRRs showed less caries progression (13.6%, p = 0.20), more regression (0.3%, p = 0.44), and fewer active caries lesions (15.3%, p = 0.15) in the intervention group. Conclusion: Daily consumption of a lozenge combining arginine and probiotics for 10-12 months given to 5-9-years-old children characterized being with low caries risk demonstrated a marked, though not statistically significant RRR for caries progression, regression, and number of active lesions in the intervention group compared to the placebo-group. ClinicalTrials.gov (NCT03928587). Clinical significance: Since all the RRRs were in favor of the intervention group and the PF of combined arginine and probiotics is high (81.6%) compared to fluoride toothpaste (24.9%) and arginine-fluoride toothpaste alone (19.6%) the combined pre-and probiotics approach may be a future additional tool regarding caries prevention and control.

Panoramic X-rays are frequently used in dentistry for treatment planning, but their interpretation can be both time-consuming and prone to error. Artificial intelligence (AI) has the potential to aid in the analysis of these X-rays, thereby improving the accuracy of dental diagnoses and treatment plans. Nevertheless, designing automated algorithms for this purpose poses significant challenges, mainly due to the scarcity of annotated data and variations in anatomical structure. To address these issues, the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge (DENTEX) has been organized in association with the International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) in 2023. This challenge aims to promote the development of algorithms for multi-label detection of abnormal teeth, using three types of hierarchically annotated data: partially annotated quadrant data, partially annotated quadrant-enumeration data, and fully annotated quadrant-enumeration-diagnosis data, inclusive of four different diagnoses. In this paper, we present the results of evaluating participant algorithms on the fully annotated data, additionally investigating performance variation for quadrant, enumeration, and diagnosis labels in the detection of abnormal teeth. The provision of this annotated dataset, alongside the results of this challenge, may lay the groundwork for the creation of AI-powered tools that can offer more precise and efficient diagnosis and treatment planning in the field of dentistry. The evaluation code and datasets can be accessed at https://github.com/ibrahimethemhamamci/DENTEX

OBJECTIVES:To investigate the effect of daily use of a lozenge containing arginine and probiotics for 10-12 months on caries increment, gingivitis- and plaque occurrence in children aged 5-9 years. METHODS:In this placebo-controlled, double-blinded, parallel-grouped randomized clinical trial, 343 children were randomly assigned to one of the study arms (1:1). The intervention group (n = 172) received a lozenge containing Lacticaseibacillus rhamnosus, LGG® (DSM33156), Lactobacillus paracasei subsp. paracasei, L. CASEI 431® (DSM33451) and prebiotic (arginine 2%). The placebo group (n = 171) received an identical lozenge without arginine or probiotics. Primary canines and molars, and permanent first molars were examined clinically (d/D= ICDAS1-6) and radiographically (d/D = R1-6) at baseline and follow-up. Missing (m/M), sealed (s/S), and filled (f/F) surfaces (-s/-S) in both dentitions were also included. Utilizing clinical and radiographic scorings, caries experience was classified as dICDAS1-6msf-s (primary teeth), DICDAS1-6MSF-S (permanent teeth), d/DICDAS1-6 m/M-s/S-f/F-s/S (mixed dentition). A weighted and an unweighted score system was applied. RESULTS:The study was completed by 288 children. The dropout rate was 15%. The increase in ∆mean dICDAS3-6msf-s and ∆mean d/DICDAS3-6m/M-s/S-f/F-s/S was lower in the intervention group (p = 0.007). No differences were found for gingivitis- and plaque occurrence. No product-related side effects were reported. This study followed ICH-GCP including external monitoring. CONCLUSION:Daily consumption of a lozenge containing prebiotic arginine and two strains of probiotics showed safe use and statistically significantly reduction in caries incrementbut no effect on the mean plaque or gingivitis occurrence in children. The use of a lozenge with arginine and probiotics combined has a promising potential as a supplementary tool for future management of caries. www. CLINICALTRIALS:gov (NCT03928587). CLINICAL SIGNIFICANCE:The combination of prebiotic arginine and probiotics shows clinical potential as a supplementary approach to toothbrushing with fluoride toothpaste in managing caries increment in children. A new era in the management of caries may be emerging.

Objectives: The objective was to examine the effect of giving Artificial Intelligence (AI)-based radiographic information versus standard radiographic and clinical information to dental students on their pulp exposure prediction ability.Methods: 292 preoperative bitewing radiographs from patients previously treated were used. A multi-path neural network was implemented. The first path was a convolutional neural network (CNN) based on ResNet-50 architecture. The second path was a neural network trained on the distance between the pulp and lesion extracted from X-ray segmentations. Both paths merged and were followed by fully connected layers that predicted the probability of pulp exposure. A trial concerning the prediction of pulp exposure based on radiographic input and information on age and pain was conducted, involving 25 dental students. The data displayed was divided into 4 groups (G): G(X-ray), G(X-ray+clinical data), G(X-ray+AI), G(X-ray+clinical data+AI).Results: The results showed that AI surpassed the performance of students in all groups with an F1-score of 0.71 (P < 0.001). The students' F1-score in G(X-ray+AI) and G(X-ray+clinical data+AI) with model prediction (0.61 and 0.61 respectively) was slightly higher than the F1-score in G(X-ray) and G(X-ray+clinical) data (0.58 and 0.59 respectively) with a borderline statistical significance of P = 0.054. Conclusions: Although the AI model had much better performance than all groups, the participants when given AI prediction, benefited only 'slightly'. AI technology seems promising, but more explainable AI predictions along with a 'learning curve' are warranted.

Abstract Objectives To examine the dimensional changes of endodontic sealers during 18 months using three‐dimensional (3D) surface scanning and subtraction radiography in a novel in vitro sealer‐extrusion model. Material and Methods Fifty endodontically instrumented acrylic teeth were randomly allocated to five groups (n = 10) filled with Apexit Plus, AH Plus, BioRoot RCS, TubliSeal EWT, and gutta‐percha (control). Freshly mixed sealers were intentionally extruded during obturation. All teeth were immersed in a physiologic solution for up to 18 months. Blinded 3D surface scans (resolution: ~10 μm) and digital radiographs of the teeth were obtained at baseline (immediately after obturation); and then after 1 week, and at 1, 3, and 18 months. For blinded assessment of sealer dimensional change, 3D models and radiographs were superimposed using specific software. Volumetric differences, root mean square (RMS), and area change from subtraction radiography measured at each period within each sealer group were thereafter calculated. Repeated measures analyses were done with Bonferroni adjustment for multiple comparisons; standard errors, p values, and 95% confidence intervals (CI) were reported. Results Analyses of the volumetric data confirmed significant, progressive material loss for Apexit Plus when compared to the other investigated sealers or the control group (p ≤ 0.02). Immersion period significantly influenced the volumetric dimensional changes of Apexit Plus already after 1 month (p < 0.01). For TubliSeal EW, the effect of the immersion period on the dimensional changes was noted after immersion for 3 months (p ≤ 0.02), while for BioRoot RCS this was evident only at 18 months (p < 0.01). Same trends were noted for the RMS data, whereas progressive dimensional changes using subtraction radiography only revealed significant changes for Apexit Plus (p = 0.01). Conclusions The largest dimensional changes were shown by Apexit Plus, followed by Tubliseal EWT and BioRoot RCS. AH Plus remained stable throughout 18 months.

This paper describes our solution for the Dental Enumeration and Diagnosis on Panoramic X-rays Challenge at MICCAI 2023. Our approach consists of a multi-step framework tailored to the task of detecting and classifying abnormal teeth. The solution includes three sequential stages: dental instance detection, healthy instance filtering, and abnormal instance classification. In the first stage, we employed a Faster-RCNN model for detecting and identifying teeth. In subsequent stages, we designed a model that merged the encoding pathway of a pretrained U-net, optimized for dental lesion detection, with the Vgg16 architecture. The resulting model was first used for filtering out healthy teeth. Then, any identified abnormal teeth were categorized, potentially falling into one or more of the following conditions: embedded, periapical lesion, caries, deep caries. The model performing dental instance detection achieved an AP score of 0.49. The model responsible for identifying healthy teeth attained an F1 score of 0.71. Meanwhile, the model trained for multi-label dental disease classification achieved an F1 score of 0.76. The code is available at https://github.com/tudordascalu/2d-teeth-detection-challenge.

As an interesting cancer immunotherapy approach, cancer vaccines have been developed to deliver tumor antigens and adjuvants to antigen-presenting cells (APCs). Although the safety and easy production shifted the vaccine designing platforms toward the subunit vaccines, their efficacy is limited due to inefficient vaccine delivery. Nanotechnology-based vaccines, called nanovaccines, address the delivery limitations through co-delivery of antigens and adjuvants into lymphoid organs and APCs and their intracellular release, leading to cross-presentation of antigens and induction of potent anti-tumor immune responses. Although the nanovaccines, either as encapsulating agents or biomimetic nanoparticles, exert the desired anti-tumor activities, there is evidence that the mixing formulation to form nanocomplexes between antigens and adjuvants based on the electrostatic interactions provokes high levels of immune responses owing to Ags' availability and faster release. Here, we summarized the various platforms for developing cancer vaccines and the advantages of using delivery systems. The cancer nanovaccines, including nanoparticle-based and biomimetic-based nanovaccines, are discussed in detail. Finally, we focused on the nanocomplexes formation between antigens and adjuvants as promising cancer nanovaccine platforms.