Comparison of two different caries risk assessment tools for infants and toddlers - A cross-sectional study.

CONTEXT: One of the essential components for successful caries management is caries risk assessment (CRA). Among CRA tools (CRATs) published in the literature: Caries management by risk assessment (CAMBRA) 123 and American Academy of Pediatric Dentistry (AAPD) CRATs are specifically designed for infants and toddlers. AIMS: The aim of this study is to compare readily available internationally accepted CRAT for infants and toddlers and check the usability of these tools in assigning caries risk among the Indian population. SETTINGS AND DESIGN: The study was conducted at Baby Oral Health Promotion Clinic, Department of Pediatrics, Tertiary Care Hospital using a cross-sectional study design. SUBJECTS AND METHODS: Data were collected using CAMBRA 123 and AAPD CRATs from 379 children aged 0-6 years. The caries risk of the children was recorded with each CRAT and a comparison was made between the two tools used. STATISTICAL ANALYSIS USED: The percentage of agreement and Cohen's kappa coefficient were used to know the agreement between the CAMBRA 123 and AAPD CRATs using the SPSS statistical tool. The significance level was set at 5% (α = 0.05). RESULTS: For children aged <2 years, the study showed slight agreement between the CAMBRA 123 and AAPD, whereas, for children more than 2 years, there was a fair agreement between the two methods which was statistically significant. This indicates that the agreement between the two methods is still not perfectly established, and AAPD CRA assigns a higher risk category than CAMBRA 123. CONCLUSIONS: CAMBRA 123 is a promising user-friendly quantitative method for CRA in clinical practice. Since there is ambiguity in assessing the caries risk in children <2 years, there is a need to establish a CRAT that can be used exclusively for children below 2 years.

Comparative evaluation and patient satisfaction with an electrical impedance-based device versus digital radiography in the estimation of remaining dentin thickness in carious posterior permanent teeth: (Diagnostic accuracy study).

BACKGROUND: Accurate assessment of remaining dentin thickness (RDT) is paramount for restorative decisions and treatment planning of vital teeth to avoid any pulpal injury. This diagnostic accuracy study compared the validity and patient satisfaction of an electrical impedance based device Prepometer™ (Hager & Werken, Duisburg, Germany) versus intraoral digital radiography for the estimation of remaining dentin thickness in carious posterior permanent teeth. METHODS: Seventy patients aged 12-25 years with carious occlusal or proximal permanent vital posterior teeth were recruited. Tooth preparation was performed to receive an adhesive restoration. Pre- and post-excavation RDT were measured radiographically by two calibrated raters using the paralleling periapical technique. Prepometer™ measurements were performed by the operator. Patients rated their satisfaction level with each tool on a 4-point Likert scale and 100 mm visual analog scale (VAS). Inter and intragroup comparisons were analyzed using signed rank test, while agreement between devices and observations was tested using weight kappa (WK) coefficient. RESULTS: the intergroup comparisons showed that, before and after excavation, there was a significant difference between measurements made by both techniques (p < 0.001). After excavation, there was a weak agreement between measurements (WK = 0.2, p < 0.001), whereas before excavation, the agreement was not statistically significant (p = 0.407). Patients were significantly more satisfied with Prepometer™ based on scales and VAS (p < 0.001). CONCLUSION: Prepometer™ could be a viable clinical tool for determining RDT with high patient satisfaction, while radiographs tended to overestimate RDT in relation to the Prepometer™.

Two-year clinical and radiographic evaluation of ACTIVA BioACTIVE versus Compomer (Dyract® eXtra) in the restoration of class-2 cavities of primary molars: a non-inferior split-mouth randomised clinical trial.

OBJECTIVES: The trial aimed to compare the clinical performance and radiographic success of ACTIVA BioACTIVE versus Compomer in restoring class-II cavities of primary molars. MATERIALS AND METHODS: A non-inferior split-mouth design was considered. A pre-calculated sample size of 96 molars (48 per group) with class-2 cavities of twenty-one children whose ages ranged from 5 to 10 years were randomly included in the trial. Pre-operative Plaque Index (PI), DMFT/dmft scores and the time required to fill the cavity were recorded. Over 24 months, the teeth were clinically evaluated every six months and radiographically every 12 months by two calibrated and blinded evaluators using the United States public health service (USPHS)-Ryge criteria. The two-sided 95% confidence interval (CI) for the difference in success rate was considered to assess non-inferiority, and the margin was set at -18%. The linear mixed model and Firth's logistic regression model were used for data analysis (P < 0.05). RESULTS: After 24 months, 86 teeth (43 per group) were evaluated. The mean PI score was 1.1(± 0.9), while DMFT/dmft was 0.35 (± 0.74) and 6.55 (± 2.25) respectively. The clinical and radiographic success rate of Dyract vs. ACTIVA was 95.3% and 88.3% vs. 93% and 86%, respectively. The two-sided 95% CI for the difference in success rate (-2.3%) was - 3.2 to 1.3% and didn't reach the predetermined margin of -18% which had been anticipated as the non-inferiority margin. Clinically, ACTIVA had a significantly better colour match (P = 0.002) but worse marginal discolouration (P = 0.0143). There were no significant differences regarding other clinical or radiographic criteria (P > 0.05). ACTIVA took significantly less placement time than Dyract, with a mean difference of 2.37 (± 0.63) minutes (P < 0.001). CONCLUSION: The performance of ACTIVA was not inferior to Dyract and both materials had a comparable high clinical and radiographic performance in children with high-caries experience. ACTIVA had a significantly better colour match but more marginal discolouration. It took significantly less time to be placed in the oral cavity. TRIAL REGISTRATION: The study was registered at ClinicalTrials.gov on 4 May 2018 (#NCT03516838).

Assessment of dental pulp response to carries via MR T2mapping and histological analysis.

OBJECTIVES: The aim of our study was to assess the correlation between T2 relaxation times and their variability with the histopathological results of the same teeth in relation to caries progression. MATERIALS AND METHODS: 52 extracted permanent premolars were included in the study. Prior to extractions, patients underwent magnetic resonance imaging (MRI) scanning and teeth were evaluated using ICDAS classification. Pulps of extracted teeth were histologically analysed. RESULTS: MRI T2 relaxation times (ms) were 111,9 ± 11.2 for ICDAS 0, 132.3 ± 18.5* for ICDAS 1, 124.6 ± 14.8 for ICDAS 2 and 112. 6 ± 18.2 for ICDAS 3 group (p = 0,013). A positive correlation was observed between MRI T2 relaxation times and macrophage and T lymphocyte density in healthy teeth. There was a positive correlation between vascular density and T2 relaxation times of dental pulp in teeth with ICDAS score 1. A negative correlation was found between T2 relaxation times and macrophage density. There was a positive correlation between T2 relaxation time variability and macrophage and T lymphocyte density in teeth with ICDAS score 2. In teeth with ICDAS score 3, a positive correlation between T2 relaxation times and T2 relaxation time variability and lymphocyte B density was found. CONCLUSION: The results of our study confirm the applicability of MRI in evaluation of the true condition of the pulp tissue. CLINICAL RELEVANCE: With the high correlation to histological validation, MRI method serves as a promising imaging implement in the field of general dentistry and endodontics.

Current Applications of Artificial Intelligence for Pediatric Dentistry: A Systematic Review and Meta-Analysis.

Purpose: To systematically evaluate artificial intelligence applications for diagnostic and treatment planning possibilities in pediatric dentistry. Methods: PubMed®, EMBASE®, Scopus, Web of Science™, IEEE, medRxiv, arXiv, and Google Scholar were searched using specific search queries. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) checklist was used to assess the risk of bias assessment of the included studies. Results: Based on the initial screening, 33 eligible studies were included (among 3,542). Eleven studies appeared to have low bias risk across all QUADAS-2 domains. Most applications focused on early childhood caries diagnosis and prediction, tooth identification, oral health evaluation, and supernumerary tooth identification. Six studies evaluated AI tools for mesiodens or supernumerary tooth identification on radigraphs, four for primary tooth identification and/or numbering, seven studies to detect caries on radiographs, and 12 to predict early childhood caries. For these four tasks, the reported accuracy of AI varied from 60 percent to 99 percent, sensitivity was from 20 percent to 100 percent, specificity was from 49 percent to 100 percent, F1-score was from 60 percent to 97 percent, and the area-under-the-curve varied from 87 percent to 100 percent. Conclusions: The overall body of evidence regarding artificial intelligence applications in pediatric dentistry does not allow for firm conclusions. For a wide range of applications, AI shows promising accuracy. Future studies should focus on a comparison of AI against the standard of care and employ a set of standardized outcomes and metrics to allow comparison across studies.

Effect of bottled fluoridated water to prevent dental caries in primary teeth: study protocol for a phase 2 parallel-group 3.5-year randomized controlled clinical trial (waterBEST).

BACKGROUND: Fluoridation of public water systems is known as a safe and effective strategy for preventing dental caries based on evidence from non-randomized studies. Yet 110 million Americans do not have access to a fluoridated public water system and many others do not drink tap water. This article describes the study protocol for the first randomized controlled trial (RCT) of fluoridated water that assesses its potential dental caries preventive efficacy when delivered in bottles. METHODS: waterBEST is a phase 2b proof-of-concept, randomized, quadruple-masked, placebo-controlled, parallel-group trial designed to estimate the potential efficacy of fluoridated versus non-fluoridated bottled water to prevent dental caries incidence in the first 4 years of life. Two hundred children living in eastern North Carolina, USA, and aged 2-6 months at screening are being allocated at random in a 1:1 ratio to receive fluoridated (0.7 mg/L F) or non-fluoridated bottled water sourced from two local public water systems. Throughout the 3.5-year intervention, study water is delivered monthly in 5-gallon bottles to each child's home with instructions to use it whenever the child consumes water as a beverage or in food preparation. Parents are interviewed quarterly to monitor children's water consumption and health. At annual visits, the presence of dental caries is evaluated with a dental screening examination. Clippings from fingernails and toenails are collected to quantify fluoride content as a biomarker of total fluoride intake. The primary endpoint is the number of primary tooth surfaces decayed, missing, or filled due to dental caries measured by the study dentist near the time of the child's fourth birthday. Tooth decay is assessed at the threshold of macroscopic enamel loss. For the primary aim, a least-squares, generalized linear model will estimate efficacy and its one-tailed, upper 80% confidence limit. DISCUSSION: waterBEST is the first evaluation of a randomized intervention of fluoridated drinking water in bottles to prevent dental caries in the primary dentition. This innovative method of delivering fluoridated water has the potential to prevent early childhood caries in a large segment of the US population that currently does not benefit from fluoridated public water. TRIAL REGISTRATION: ClinicalTrials.gov NCT04893681. Registered on March 2022. Last update posted on 10 October 2023. https://clinicaltrials.gov/study/NCT04893681?cond=Dental%20Caries%20in%20Children&term=fluoride&locStr=North%20Carolina,%20USA&country=United%20States&state=North%20Carolina&distance=50&rank=1.

ORCA-EFCD consensus report on clinical recommendation for caries diagnosis. Paper I: caries lesion detection and depth assessment.

OBJECTIVES: The aim of the present consensus paper was to provide recommendations for clinical practice considering the use of visual examination, dental radiography and adjunct methods for primary caries detection. MATERIALS AND METHODS: The executive councils of the European Organisation for Caries Research (ORCA) and the European Federation of Conservative Dentistry (EFCD) nominated ten experts each to join the expert panel. The steering committee formed three work groups that were asked to provide recommendations on (1) caries detection and diagnostic methods, (2) caries activity assessment and (3) forming individualised caries diagnoses. The experts responsible for "caries detection and diagnostic methods" searched and evaluated the relevant literature, drafted this manuscript and made provisional consensus recommendations. These recommendations were discussed and refined during the structured process in the whole work group. Finally, the agreement for each recommendation was determined using an anonymous Delphi survey. RESULTS: Recommendations (N = 8) were approved and agreed upon by the whole expert panel: visual examination (N = 3), dental radiography (N = 3) and additional diagnostic methods (N = 2). While the quality of evidence was found to be heterogeneous, all recommendations were agreed upon by the expert panel. CONCLUSION: Visual examination is recommended as the first-choice method for the detection and assessment of caries lesions on accessible surfaces. Intraoral radiography, preferably bitewing, is recommended as an additional method. Adjunct, non-ionising radiation methods might also be useful in certain clinical situations. CLINICAL RELEVANCE: The expert panel merged evidence from the scientific literature with practical considerations and provided recommendations for their use in daily dental practice.

AI-Dentify: deep learning for proximal caries detection on bitewing x-ray - HUNT4 Oral Health Study.

BACKGROUND: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informative analysis of the bitewing images. METHODS: A dataset of 13,887 bitewings from the HUNT4 Oral Health Study were annotated individually by six different experts, and used to train three different object detection deep-learning architectures: RetinaNet (ResNet50), YOLOv5 (M size), and EfficientDet (D0 and D1 sizes). A consensus dataset of 197 images, annotated jointly by the same six dental clinicians, was used for evaluation. A five-fold cross validation scheme was used to evaluate the performance of the AI models. RESULTS: The trained models show an increase in average precision and F1-score, and decrease of false negative rate, with respect to the dental clinicians. When compared against the dental clinicians, the YOLOv5 model shows the largest improvement, reporting 0.647 mean average precision, 0.548 mean F1-score, and 0.149 mean false negative rate. Whereas the best annotators on each of these metrics reported 0.299, 0.495, and 0.164 respectively. CONCLUSION: Deep-learning models have shown the potential to assist dental professionals in the diagnosis of caries. Yet, the task remains challenging due to the artifacts natural to the bitewing images.

Development of short forms for screening children's dental caries and urgent treatment needs using item response theory and machine learning methods.

OBJECTIVES: Surveys can assist in screening oral diseases in populations to enhance the early detection of disease and intervention strategies for children in need. This paper aims to develop short forms of child-report and proxy-report survey screening instruments for active dental caries and urgent treatment needs in school-age children. METHODS: This cross-sectional study recruited 497 distinct dyads of children aged 8-17 and their parents between 2015 to 2019 from 14 dental clinics and private practices in Los Angeles County. We evaluated responses to 88 child-reported and 64 proxy-reported oral health questions to select and calibrate short forms using Item Response Theory. Seven classical Machine Learning algorithms were employed to predict children's active caries and urgent treatment needs using the short forms together with family demographic variables. The candidate algorithms include CatBoost, Logistic Regression, K-Nearest Neighbors (KNN), Naïve Bayes, Neural Network, Random Forest, and Support Vector Machine. Predictive performance was assessed using repeated 5-fold nested cross-validations. RESULTS: We developed and calibrated four ten-item short forms. Naïve Bayes outperformed other algorithms with the highest median of cross-validated area under the ROC curve. The means of best testing sensitivities and specificities using both child-reported and proxy-reported responses were 0.84 and 0.30 for active caries, and 0.81 and 0.31 for urgent treatment needs respectively. Models incorporating both response types showed a slightly higher predictive accuracy than those relying on either child-reported or proxy-reported responses. CONCLUSIONS: The combination of Item Response Theory and Machine Learning algorithms yielded potentially useful screening instruments for both active caries and urgent treatment needs of children. The survey screening approach is relatively cost-effective and convenient when dealing with oral health assessment in large populations. Future studies are needed to further leverage the customize and refine the instruments based on the estimated item characteristics for specific subgroups of the populations to enhance predictive accuracy.

Machine learning to predict untreated dental caries in adolescents.

OBJECTIVE: This study aimed to predict adolescents with untreated dental caries through a machine-learning approach using three different algorithms METHODS: Data came from an epidemiological survey in the five largest cities in Mato Grosso do Sul, Brazil. Data on sociodemographic characteristics, consumption of unhealthy foods and behaviours (use of dental floss and toothbrushing) were collected using Sisson's theoretical model, in 615 adolescents. For the machine learning, three different algorithms were used: (1) XGboost; (2) decision tree and (3) logistic regression. The epidemiological baseline was used to train and test predictions to detect individuals with untreated dental caries, through eight main predictor variables. Analyzes were performed using the R software (R Foundation for Statistical Computing, Vienna, Austria). The Ethics Committee approved the study.. RESULTS: For the 615 adolescents, xgboost performed better with an area under the curve (AUC) of 84% versus 81% for the decision tree algorithm. The most important variables were the use of dental floss, unhealthy food consumption, self-declared race and exposure to fluoridated water. CONCLUSIONS: Family health teams can improve the work process and use artificial intelligence mechanisms to predict adolescents with untreated dental caries, and, in this way, schedule dental appointments for the treatment of adolescents earlier.

Effect of composite radiopacity and margin location of the restoration on the diagnosis of secondary caries.

This research aimed to evaluate the effect of the radiopacity of a Bulk-Fill composite (X-TraFil, VOCO, Germany) and a Conventional composite (P60, 3M ESPE, USA) and assessment of the margin location in the enamel and dentin on the diagnosis of secondary caries. 76 intact premolars with MOD preparation were divided into two equal groups and filled with the conventional and bulk-fill composite. Four regions were considered to simulate carious lesions (two regions in enamel and two regions in dentin). In each group, half of the regions in the dentin and half in the enamel were randomly selected for secondary caries simulation and filled with a wax-plaster combination while the remaining regions stayed intact. Bitewing imaging was done using the PSP digital sensor. Five examiners reviewed the images, and lesions were recorded. Caries diagnosis indicators and paired-sample t-test were used for statistical analysis. The reproducibility and accuracy of the examiners' responses were evaluated using the kappa and agreement coefficient (α=0.05). The sensitivity, specificity, and accuracy of diagnosing secondary carious lesions in enamel were significantly better under conventional than bulk-fill composite. Similarly, the sensitivity and accuracy of diagnosing secondary caries in dentin were significantly higher under conventional composite than bulk-fill composite (p<0.05). No significant differences were found in the agreement and kappa coefficient between conventional and bulk-fill composites in the enamel and dentin (p>0.05). The diagnostic accuracy of carious lesions was higher under conventional composite than bulk-fill composite. However, the location of the secondary was ineffective in caries diagnosis.

Occlusal caries detection and monitoring using a 3D intraoral scanner system. An in vivo assessment.

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.

Diagnosing and recording root caries: A survey among Swiss dentists.

OBJECTIVES: Despite the increase in the root caries prevalence, little is still known about how dentists manage this condition. The present study aimed to evaluate the knowledge of dentists on diagnosing and recording root caries lesions (RCL). METHODS: The survey consisted of three domains: (1) dentists' knowledge on diagnosing, recording and managing RCL; (2) information about their current general clinical routines; and (3) their demographics. The four Swiss Universities distributed the survey via e-mail lists for alumni or professionals participating in continuing education. The data was quality checked. Construct validity, internal reliability and intraclass correlation (ICC) were assessed. RESULTS: The survey was answered by 383 dentists from 25(out of 26) cantons [mean(SD) working experience: 22.5(12) years]. The majority replied that they see less than 5 patients with RCL per week, whereas 41 have at least 5 per week, and 40 % (157 dentists) do not distinguish RCL from coronal caries in their patients' medical records. When diagnosing active RCL, tactile sensation was the most predominant criterion (n = 380), whereas color (n = 224) and visual appearance (n = 129) of the lesion were less often selected. The most often chosen risk factors for RCL were poor oral hygiene and presence of biofilm.The responses were significantly influenced by the participants' place of education, their age and working area. CONCLUSION: The present survey highlights the huge diversity in diagnosing, recording and assessing risk factors of RCL. The benefits of an appropriate diagnosis, recording and management of risk factors of RCL should be highlighted in under- and postgraduate dental education. CLINICAL SIGNIFICANCE: A great diversity in diagnosing, recording and assessing risk factors of RCL was observed, which migh strongly impact how dentists manage RCL. The study emphasizes the necessity for intensive efforts to bridge the gap between guideline recommendations and their implementation in private dental practices.

Artificial neural network and convolutional neural network for prediction of dental caries.

Dental caries has high prevalence among kids and adults thus it has become one of the global health concerns. The current modern dentistry focused on the preventives measures to reduce the number of dental caries cases. The employment of machine learning coupled with UV spectroscopy plays a crucial role to detect the early stage of caries. Artificial neural network with hyperparameter tuning was employed to train spectral data for the classification based on the International Caries Detection and Assesment System (ICDAS). Spectra preprocessing namely mean center (MC), autoscale (AS) and Savitzky Golay smoothing (SG) were applied on the data for spectra correction. The best performance of ANN model obtained has accuracy of 0.85 with precision of 1.00. Convolutional neural network (CNN) combined with Savitzky Golay smoothing performed on the spectral data has accuracy, precision, sensitivity and specificity for validation data of 1.00 respectively. The result obtained shows that the application of ANN and CNN capable to produce robust model to be used as an early screening of dental caries.

Caries risk assessment by Caries Management by Risk Assessment (CAMBRA) Protocol among the general population of Pakistan-a multicenter analytical study.

Background: Caries risk (CR) assessment tools are used to properly identify individuals with caries risk and to improve preventive procedures and programs. A tool such as CAMBRA determines the precise protective factors of caries and identifies an individual's specific therapeutic intervention. The purpose of this study was to assess the caries risk using the CAMBRA protocol among the general population of Pakistan. Methods: This multicentre analytical study was conducted in ten dental hospitals in different provinces of Pakistan and the caries risk assessment was carried out using a questionnaire that was designed using the Caries Management by Risk Assessment (CAMBRA) protocol. All 521 participants were intra-orally examined to assess oral hygiene status and the presence of disease. Multiple logistic regression test was performed for analysis. Results: A higher number of participants (61.2%) were found to be in the moderate risk category of caries risk assessment. The males are 51% less likely to have caries compared to the females (AOR = 0.49, P = 0.081). The majority of participants (71.3%) had one or more disease indicators, with white spots and visible cavities. Those with visible, heavy plaque were 13.9 times more likely to have caries compared to those without (AOR = 13.92, P < 0.001). Those using calcium and phosphate during the last 6 months were 90% less likely to have caries compared to those not using them (AOR = 0.10, P < 0.001). There was no significant interaction between all eight risk factors retained in the final model (P > 0.05), the Hosmer and Lemeshow Test P < 0.001, classification accuracy = 87.1%, and AUC = 91.2%. Conclusion: The caries risk among the general population of Pakistan is moderate, with significant variation among age groups, education levels, and socioeconomic status.

Volumetric analysis after caries excavation with caries detecting dyes and chemomechanical caries removal agents using 3D scanner-a randomised clinical trial.

AIM: This research aimed to use an extra-oral 3D scanner for conducting volumetric analysis after caries excavation using caries-detecting dyes and chemomechanical caries removal agents in individuals with occlusal and proximal carious lesions. METHODS: Patients with occlusal (A1, A2, A3) and proximal carious lesions (B1, B2, B3) were treated with the conventional rotary technique, caries detecting dyes (CDD) and chemomechanical caries removal (CMCR) method on 90 teeth (n = 45 for each). Group A1, B1: Excavation was performed using diamond points. Group A2, B2: CDD (Sable Seek™ caries indicator, Ultradent) was applied and left for 10 s, and then the cavity was rinsed and dried. For caries removal, diamond points or excavators were used. Group A3 and B3: BRIX3000 papain gel was applied with a micro-brush for 20 s and was activated for 2 min, and then the carious tissue was removed with a sharp spoon excavator. Post-excavation cavity volume analysis was performed using a 3D scanner. The time required and the verbal pain score (VPS) for pain were scored during excavation. Post-restoration evaluation was performed at 1, 3, and 6 months FDI (Federation Dentaire Internationale) criteria. RESULTS: Comparison of age, time and volume with study groups were made using Independent Sample' t' test and one-way analysis of variance (ANOVA) for two and more than two groups, respectively. Using Cohen's Kappa Statistics, evaluators 1 and 2 agreed on caries removal status aesthetic, functional and biological properties at different follow-ups. The chi-square test revealed that the rotary groups [A1(2.5 ± 0.4 min) B1(4.0 ± 0.4 min)] had significantly less (p = 0.000) mean procedural time than CDD [A2(4.5 ± 0.4 min) B2(5.7 ± 0.4 min)] and CMCR [A3(5.4 ± 0.7 min) B3(6.2 ± 0.6 min)] groups. The CMCR group showed better patient acceptance and less pain during caries excavation than the rotary and CDD groups. CMCR group showed significantly less mean caries excavated volume(p = 0.000). Evaluation of restoration after 1-, 3-, and 6-month intervals was acceptable for all the groups. CONCLUSION: Brix3000 helps effectively remove denatured teeth with less pain or sensitivity. The time required for caries removal was lowest in the rotary method and highest in the brix3000 group, while the volume of caries removed was the lowest for brix3000 and highest for the rotary group.

Developmental and Acquired Abnormalities of the Teeth.

This review aims to present a detailed analysis of the most common developmental and acquired dental abnormalities, including caries, resorptive lesions, and congenital anomalies of teeth number, size, form, and structure. This review highlights how diagnostic imaging can aid in the accurate identification and management of these conditions.

Artificial intelligence for radiographic imaging detection of caries lesions: a systematic review.

BACKGROUND: The aim of this systematic review is to evaluate the diagnostic performance of Artificial Intelligence (AI) models designed for the detection of caries lesion (CL). MATERIALS AND METHODS: An electronic literature search was conducted on PubMed, Web of Science, SCOPUS, LILACS and Embase databases for retrospective, prospective and cross-sectional studies published until January 2023, using the following keywords: artificial intelligence (AI), machine learning (ML), deep learning (DL), artificial neural networks (ANN), convolutional neural networks (CNN), deep convolutional neural networks (DCNN), radiology, detection, diagnosis and dental caries (DC). The quality assessment was performed using the guidelines of QUADAS-2. RESULTS: Twenty articles that met the selection criteria were evaluated. Five studies were performed on periapical radiographs, nine on bitewings, and six on orthopantomography. The number of imaging examinations included ranged from 15 to 2900. Four studies investigated ANN models, fifteen CNN models, and two DCNN models. Twelve were retrospective studies, six cross-sectional and two prospective. The following diagnostic performance was achieved in detecting CL: sensitivity from 0.44 to 0.86, specificity from 0.85 to 0.98, precision from 0.50 to 0.94, PPV (Positive Predictive Value) 0.86, NPV (Negative Predictive Value) 0.95, accuracy from 0.73 to 0.98, area under the curve (AUC) from 0.84 to 0.98, intersection over union of 0.3-0.4 and 0.78, Dice coefficient 0.66 and 0.88, F1-score from 0.64 to 0.92. According to the QUADAS-2 evaluation, most studies exhibited a low risk of bias. CONCLUSION: AI-based models have demonstrated good diagnostic performance, potentially being an important aid in CL detection. Some limitations of these studies are related to the size and heterogeneity of the datasets. Future studies need to rely on comparable, large, and clinically meaningful datasets. PROTOCOL: PROSPERO identifier: CRD42023470708.

Dental Caries diagnosis from bitewing images using convolutional neural networks.

BACKGROUND: Dental caries, also known as tooth decay, is a widespread and long-standing condition that affects people of all ages. This ailment is caused by bacteria that attach themselves to teeth and break down sugars, creating acid that gradually wears away at the tooth structure. Tooth discoloration, pain, and sensitivity to hot or cold foods and drinks are common symptoms of tooth decay. Although this condition is prevalent among all age groups, it is especially prevalent in children with baby teeth. Early diagnosis of dental caries is critical to preventing further decay and avoiding costly tooth repairs. Currently, dentists employ a time-consuming and repetitive process of manually marking tooth lesions after conducting radiographic exams. However, with the rapid development of artificial intelligence in medical imaging research, there is a chance to improve the accuracy and efficiency of dental diagnosis. METHODS: This study introduces a data-driven model for accurately diagnosing dental decay through the use of Bitewing radiology images using convolutional neural networks. The dataset utilized in this research includes 713 patient images obtained from the Samin Maxillofacial Radiology Center located in Tehran, Iran. The images were captured between June 2020 and January 2022 and underwent processing via four distinct Convolutional Neural Networks. The images were resized to 100 × 100 and then divided into two groups: 70% (4219) for training and 30% (1813) for testing. The four networks employed in this study were AlexNet, ResNet50, VGG16, and VGG19. RESULTS: Among different well-known CNN architectures compared in this study, the VGG19 model was found to be the most accurate, with a 93.93% accuracy. CONCLUSION: This promising result indicates the potential for developing an automatic AI-based dental caries diagnostic model from Bitewing images. It has the potential to serve patients or dentists as a mobile app or cloud-based diagnosis service (clinical decision support system).