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Objective: Aimed to compare the volumetric calculations (VC) of intra-and-extraoral scanners on carious teeth before/after caries removal., Materials and Methods: 120 extracted human molars with ICDAS scores of 3, 4, and 5 were included. The teeth were scanned using an extraoral scanner (Ineos-X5, Dentsply Sirona) and three intraoral scanners (IOS) (iTero Element-5D, Align Technology; Primescan, Dentsply Sirona; Trios 4, 3Shape) before-and-after caries removal (CR). Eight Standard Tessellation Language (STL) data of each tooth sample were overlapped in Meshmixer (Autodesk) software for VC. Shapiro-Wilk, Paired Two-Sample T-test, repeated analysis of variance test statistics, and intraclass correlation coefficient (ICC) were used (p < 0.05)., Results: In initial VC, a significant difference observed between Ineos-X5 and iTero Element-5D (p < 0.001) and Ineos-X5 and Primescan (p < 0.001), regardless of the ICDAS score. No significant difference observed after CR between Ineos-X5 and iTero Element-5D (p = 0.917), Ineos-X5 and Primescan (p = 0.936), and Ineos-X5 and Trios 4 (p = 0.727) respectively. There was also no difference between the three IOS after CR (p ≥ 0.05), whereas the iTero Element-5D scanner significantly calculated less volume than Primescan and Trios-4 initially (p = 0.003). Maximum ICC was observed between the Ineos-X5 and Primescan in the ICDAS 5 score, before CR (ICC = 1, p < 0.001) and triple comparisons of iTero Element-5D, Primescan, and Trios-4 (ICC = 1, p < 0.001)., Conclusion: Carious and irregular surfaces might affect the data collection of IOS., Clinical Relevance: Intra-and-extraoral data may present negligible differences in the volumetric calculation, depending on the caries status and anatomical properties. These differences and factors may be important for future artificial intelligence networks that simulate the aftermath of caries removal. The IOSs tested in the current study can successfully collect data from irregular, deep and shallow cavities after caries removal., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Objectives: This study aimed to evaluate the validity of the dye-enhanced quantitative light-induced fluorescence (DEQLF) method for assessing early enamel caries activity., Methods: Seventy extracted human teeth with early enamel caries on smooth surfaces were included. Two dentists evaluated the caries activity using the Nyvad system followed by the DEQLF method. The teeth were hydrated with distilled water for 60 s, dehydrated with compressed air for 10 s, and stained with 100 μM fluorescein sodium solution for 10 s. White and fluorescent images were captured using a QLF-D 2 + Billuminator. The change in fluorescence (ΔΔG) was calculated using image analysis software. Independent-sample t-tests were performed to evaluate the difference in ΔΔG between active and inactive lesions for both DEQLF and conventional quantitative light-induced fluorescence (QLF) methods. Receiver operating characteristic (ROC) curve analysis was used to assess the validity of ΔΔG for distinguishing lesion activity using the area under the ROC curve (AUROC)., Results: Among the 70 caries lesions, 33 were active and 37 were inactive. Using the DEQLF method, the ΔΔG for active lesions (3.8 ± 5.6) was significantly higher than that for inactive lesions (1.0 ± 2.5) (P < 0.05). With the conventional QLF method, there was no significant difference in ΔΔG between active (-1.1 ± 1.7) and inactive (-1.3 ± 1.7) lesions. DEQLF-derived ΔΔG demonstrated an AUROC of 0.72, a sensitivity of 0.67, and a specificity of 0.76., Conclusions: Applying the DEQLF method to human teeth enabled the quantitative assessment of lesion activity based on dye penetration. DEQLF-derived ΔΔG values showed significant differences based on lesion activity status and demonstrated high validity in distinguishing lesion activity., Clinical Significance: Clinicians can use the DEQLF method, which involves applying a fluorescent dye for 10 s prior to conventional QLF, to objectively quantify and distinguish the activity status of early enamel caries, potentially replacing the traditional reliance on subjective visual assessments., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. We declare that there is no conflict of interests regarding the publication of this article., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Introduction: Radiographic diagnostic competences are a primary focus of dental education. This study assessed two feedback methods to enhance learning outcomes and explored the feasibility of artificial intelligence (AI) to support education., Materials and Methods: Fourth-year dental students had access to 16 virtual radiological example cases for 8 weeks. They were randomly assigned to either elaborated feedback (eF) or knowledge of results feedback (KOR) based on expert consensus. Students´ diagnostic competences were tested on bitewing/periapical radiographs for detection of caries, apical periodontitis, accuracy for all radiological findings and image quality. We additionally assessed the accuracy of an AI system (dentalXrai Pro 3.0), where applicable. Data were analysed descriptively and using ROC analysis (accuracy, sensitivity, specificity, AUC). Groups were compared with Welch's t-test., Results: Among 55 students, the eF group by large performed significantly better than the KOR group in detecting enamel caries (accuracy 0.840 ± 0.041, p = .196; sensitivity 0.638 ± 0.204, p = .037; specificity 0.859 ± 0.050, p = .410; ROC AUC 0.748 ± 0.094, p = .020), apical periodontitis (accuracy 0.813 ± 0.095, p = .011; sensitivity 0.476 ± 0.230, p = .003; specificity 0.914 ± 0.108, p = .292; ROC AUC 0.695 ± 0.123, p = .001) and in assessing the image quality of periapical images (p = .031). No significant differences were observed for the other outcomes. The AI showed almost perfect diagnostic performance (enamel caries: accuracy 0.964, sensitivity 0.857, specificity 0.074; dentin caries: accuracy 0.988, sensitivity 0.941, specificity 1.0; overall: accuracy 0.976, sensitivity 0.958, specificity 0.983)., Conclusion: Elaborated feedback can improve student's radiographic diagnostic competences, particularly in detecting enamel caries and apical periodontitis. Using an AI may constitute an alternative to expert labelling of radiographs., (© 2024 The Author(s). European Journal of Dental Education published by John Wiley & Sons Ltd.)

Background: Proximal caries datasets for training artificial intelligence (AI) algorithms commonly include clinician-annotated radiographs. These conventional annotations are susceptible to observer variability, and early caries may be missed. Micro-computed tomography (CT), while not feasible in clinical applications, offers a more accurate imaging modality to support the creation of a reference-standard dataset for caries annotations. Herein, we present the Academic Center for Dentistry Amsterdam-Diagnostic Insights for Radiographic Early-caries with micro-CT (ACTA-DIRECT) dataset, which is the first dataset pairing dental radiographs and micro-CT scans to enable higher-quality annotations., Methods: The ACTA-DIRECT dataset encompasses 179 paired micro-CT scans and radiographs of early proximal carious teeth, along with three types of annotations: conventional annotations on radiographs, micro-CT-assisted annotations on radiographs, and micro-CT annotations (reference standard). Three dentists independently annotated proximal caries on radiographs, both with and without micro-CT assistance, enabling determinations of interobserver agreement and diagnostic accuracy. To establish a reference standard, one dental radiologist annotated all caries on the related micro-CT scans., Results: Micro-CT support improved interobserver agreement (Cohen's Kappa), averaging 0.64 (95% confidence interval [CI]: 0.59-0.68) versus 0.46 (95% CI: 0.44-0.48) in its absence. Likewise, average sensitivity and specificity increased from 42% (95% CI: 34-51%) to 63% (95% CI: 54-71%) and from 92% (95% CI: 88-95%) to 95% (95% CI: 92-97%), respectively., Conclusion: The ACTA-DIRECT dataset offers high-quality images and annotations to support AI-based early caries diagnostics for training and validation. This study underscores the benefits of incorporating micro-CT scans in lesion assessments, providing enhanced precision and reliability., (© 2024. The Author(s).)

Objective: Intraoral photographic images are instrumental in the early screening and clinical diagnosis of oral diseases. In addition, people have been trying to apply artificial intelligence to these images. The purpose of this study is to investigate and evaluate a deep learning system designed to segment intraoral photographic images for the detection of dental caries, dental calculus, and gingivitis, and to assess the degree of dental calculus based on the overall features of the tooth surface and gingival margin., Material and Methods: This cross-sectional study collected 3,365 oral endoscopic images, randomly distributed in training datasets (2,019 images), validation dataset (673 images), and test dataset (673 images). The training set and verification set images are manually labeled. An oral endoscopic image segmentation method based on Mamba (Oral-Mamba) and an intelligent evaluation model of dental calculus degree were proposed, achieving the segmentation of two types of oral diseases, namely gingivitis and dental caries, as well as the segmentation of dental calculus regions, and the intelligent evaluation of the degree of dental calculus., Results: Oral-Mamba demonstrated high accuracy in segmentation, with accuracy rates for gingivitis, dental caries, and dental calculus at 0.83, 0.83, and 0.81, respectively. In particular, these rates surpassed those of the U-Net model in IoU, accuracy, and recall metrics. Furthermore, Oral-Mamba runs 25% faster than U-Net.The accuracy of degree classification in the intelligent evaluation model of dental calculus degree is 85%., Conclusion: The proposed deep learning system is expected to be used for the detection of two types of oral diseases and dental calculus, and the degree judgment of photographic images from an intraoral camera. This system offers a practical method to assist in the oral screening of dental caries, dental calculus, and gingivitis, providing benefits such as intuitive use, time efficiency, cost-effectiveness, and ease of deployment., (© 2024. The Author(s).)

Objective: The purpose of the present study was to verify the diagnostic performance of an AI system for the automatic detection of teeth, caries, implants, restorations, and fixed prosthesis on panoramic radiography., Methods: This is a cross-sectional study. A dataset comprising 1000 panoramic radiographs collected from 500 adult patients was analyzed by an AI system and compared with annotations provided by two oral and maxillofacial radiologists., Results: A strong correlation (R > 0.5) was observed between AI perception and observers 1 and 2 in carious teeth (0.691-0.878), implants (0.770-0.952), restored teeth (0.773-0.834), teeth with fixed prostheses (0.972-0.980), and missing teeth (0.956-0.988)., Discussion: Panoramic radiographs are commonly used for diagnosis and treatment planning. However, they often suffer from artifacts, distortions, and superimpositions, leading to potential misinterpretations. Thus, an automated detection system is required to tackle these challenges. Artificial intelligence (AI) has revolutionized various fields, including dentistry, by enabling the development of intelligent systems that can assist in complex tasks such as diagnosis and treatment planning., Conclusion: The automatic detection by the AI system was comparable to oral radiologists and may be useful for automatic identifications in panoramic radiographs. These findings signify the potential for AI systems to enhance diagnostic accuracy and efficiency in dental practices, potentially reducing the likelihood of diagnostic errors caused by unexperienced professionals., Competing Interests: Declaration of conflicting interestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Objective: To assess the effectiveness of the REFIX technology in the remineralization process of initial caries simulated on bovine enamel. The assessment involved the analysis of backscatter intensity, which was determined from laser speckle images., Method: Twenty-one bovine teeth were divided into three groups: G1 and G7 were submitted to treatment with the REFIX technology for 1 and 7 days, respectively. The control group was treated with deionized water., Results: A significant difference in backscatter was found between the carious and sound areas in all groups (p = 0.0038, p < 0.0001, and p = 0.0002 for the control group, G1, and G7, respectively). The intergroup comparison revealed no significant difference among the groups studied., Conclusion: REFIX technology did not alter the optical properties of the samples of bovine teeth with simulated initial caries lesions after 1 and 7 days of treatment., (© 2024 Wiley‐VCH GmbH.)

Objectives: This study aimed to assess the accuracy of ultra-low dose (ULD) cone-beam CT (CBCT) for detection of proximal caries., Methods: This in vitro study evaluated 104 molar and premolar teeth. The teeth were mounted in dry skulls and underwent CBCT with 4 protocols of high-resolution (HR), normal (NORM), ULD-HR, and ULD-NORM; 78 CBCT images were scored by 3 observers for the presence and penetration depth of caries twice with a 2-week interval using a 5-point Likert scale. The teeth were then sectioned and observed under a stereomicroscope (gold standard). The 4 protocols were compared with each other and with the gold standard. The receiver operating characteristic curve was drawn, and the area under the curve (AUC) was calculated and compared by the Chi-square test (alpha = .05)., Results: The interobserver agreement ranged from 0.5233 to 0.6034 for ULD-NORM, 0.5380 to 0.6279 for NORM, 0.5856 to 0.6300 for ULD-HR, and 0.6614 to 0.7707 for HR images. The intra-observer agreement ranged from 0.6027 to 0.8812 for ULD-HR, 0.7083 to 0.7556 for HR, 0.6076 to 0.9452 for ULD-NORM, and 0.7012 to 0.9221 for NORM images. Comparison of AUC revealed no significant difference between NORM and ULD-NORM (P > .05), or HR and ULD-HR (P > .05). The highest AUC belonged to HR (0.8529) and the lowest to NORM (0.7774)., Conclusions: Considering the significant reduction in radiation dose in ULD CBCT and its acceptable diagnostic accuracy for detection of proximal caries, this protocol may be used for detection of proximal carious lesions and assessment of their depth., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology and the International Association of Dentomaxillofacial Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: To assess the diagnostic validity of ICDAS clinical criteria on 3D dental models from intraoral scanning., Methods: This is a retrospective analysis on part of the baseline sample collected in a cohort study and included 73 participants (12-19 years) from Denmark and Greece. The assessment was made by visual inspection, and then by visual inspection associated with radiographs. All participants were scanned with TRIOS 4 which uses white light to obtain the 3D models with tooth color, as well as blue light source (415 nm) for fluorescence. The 3D models' evaluation was conducted using tooth-color texture and subsequently fluorescence. Two scores were obtained for the 3D model examination: i) ICDAS based on tooth-color information; ii) ICDAS based on tooth-color information supplemented with fluorescence. For the analysis, weighted kappa, sensitivity (SE), specificity (SP) and accuracy (ACC) were calculated., Results: Regarding all lesions the values for SE, SP, and ACC were respectively 0.804, 0.801, and 0.802 for tooth-color, and 0.819, 0.808, and 0.810 for tooth-color supplemented with fluorescence. In terms of accuracy parameters for moderate-extensive lesions, the values for SE, SP, and ACC for tooth color were 0.709, 0.948, and 0.944, while for fluorescence they were 0.815, 0.937, and 0.934., Conclusion: Caries assessment with ICDAS criteria on 3D dental models produces reliable scores. Visual caries analysis using 3D models demonstrates commendable diagnostic accuracy and reasonable consistency with traditional methods. The use of intraoral scanners may be beneficial in evaluating occlusal caries., Clinical Significance: The importance of this study is to prove the diagnostic accuracy of caries lesions diagnosis made using and intraoral scanner and to offer greater confidence to professionals who use this diagnosis tool in their daily clinical practice. Intraoral scanners demonstrate to be an accurate tool for diagnosing occlusal caries., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: When this study was conducted, the co-author Stavroula Michou was employed at 3Shape TRIOS A/S. The other authors declare no conflict of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

This study examined the association between a dental implant and changes in adjacent teeth over time. Electronic health records of 1818 patients who received a dental implant were retrospectively evaluated over 14 years (2005-2019) in a university setting. The status of the adjacent tooth and vertical and horizontal distance from the implant platform to adjacent teeth were determined using digital intraoral radiographs taken at baseline and the last follow-up visit (1-14 years, median 4 years). In total, 1085 dental implants were evaluated. There were 234 instances of a change in the adjacent tooth. Decay was observed in 83 (7.6%) of adjacent teeth; the mean time to development was 4 years (range 1-14 years). Approximately 9% of adjacent teeth received direct restorations, 4.8% received indirect restorations, 1% received endodontic root canal treatment, and 5.6% were extracted. The mean horizontal distance between the implant platform and the adjacent teeth was 3.56 mm; the mean vertical distance from the contact point to the alveolar crest on the tooth side was 6.2 mm at the first time of the reported decay on X ray. These distances did not significantly influence the occurrence of caries. The prevalence of interproximal contact loss was higher on the mesial of the implant crown at 63% compared with 20% on the distal side. This large retrospective analysis identified that teeth adjacent to a dental implant were at risk of decay and changes in their condition. In addition, the implant-to-tooth distance and inadequate emergence profile may contribute as caries risk factors in addition to hygiene and a high sugar level diet. These findings appear essential for clinicians when making treatment decisions and discussing outcomes with patients.

Given today's higher demand for online transmission of radiographic images, clinicians and regulatory agencies should be given the evidence they need to guide them in choosing the best image file format to be adopted. To this end, the present scoping review aims to explore, map, and evaluate the literature, with the object of reporting the influence of image file formats on dental diagnostic tasks by assessing intraoral radiographic images. This scoping review complies with PRISMA-ScR. It was customized to assess the risk of bias of the included studies, and was registered on the Open Science Framework platform. The data extraction protocol was developed based on the PCC acronym. An electronic search was conducted in six databases (Pubmed, Web of Science, Scopus, Embase, Lilacs, Cochrane) in December 2023. Original articles were screened, having observational, diagnostic accuracy, and consisting of in vivo or ex vivo laboratory studies investigating the influence of file formats on different diagnostic tasks in dentistry. Eighteen studies, published between the years 1996 and 2022, were included. The following data were extracted from the selected articles: article title, authors' citation, publication date, country, diagnostic task, image file formats tested, compression level, and main conclusion. The most widely investigated diagnostic task was caries lesions (n = 10), led by root resorptions (n = 3), root fractures (n = 2), periapical lesions (n = 2), and periodontal disease (n = 1). The most commonly used radiographic techniques were periapical (n = 12) and bitewing (n = 6). The most frequently investigated image file formats were JPEG (all studies) and TIFF (n = 10 studies). BMP, PNG, and JPEG2000 were also included in 7, 3 and 3 studies, respectively. No studies included the DICOM file format. In regard to the subjective assessment of the several dental diagnostic tasks, the studies mostly showed that the influence of the file formats was not significant (n = 10/55.5%). As for the quality assessment of the included papers, more than 70% of the studies featured a low risk of bias. Current evidence on image file formats and dental radiographic diagnosis is reliable. Any image file format can be used without impairing diagnostic accuracy.

Background: Bulk-fill resin composites may suffer from recurrent caries around compound proximal restorations in posterior teeth, especially at the proximo-gingival interface.Over 12 months, will the bulk fill technique affect the caries recurrence rate at gingival margins when compared to the conventional incremental packing technique? How early will the first clinical, radiographical, and biochemical evidence of caries recurrence occur?, Methods: After randomization, in 30 patients with two compound (OM or OD) supragingival lesions, one tooth was restored using the bulk fill technique on one side (group 1) (n = 15). In contrast, the other tooth on the other side was restored utilizing the incremental layering technique (group 2) (n = 15). Both teeth received restorative material (X-tra fil, Voco, Cuxhaven, Germany). The FDI criteria were used to evaluate restorations. As for the periodontal assessment, the gingival index, plaque index, papillary bleeding scoring index and periodontal pocket depth were evaluated. The gingival crevicular fluid (GCF) specimens were gathered, and MMP-9 was extracted and quantitated by ELISA. A customized radiographic template was designed, and 3D printed digital bitewing radiographs were taken. Assessments were done clinically, radiographically and biochemically at baseline (1 week) and after 3, 6 and 12 months. Data was statistically analyzed., Results: The null hypothesis was accepted clinically; no statistically significant differences appeared between bulk and incrementally filled posterior restorations. As for the radiographic assessment, the null hypothesis was accepted except for increased periodontal ligament width at 3 months. The null hypothesis for the biochemical evaluation was rejected as there were significant changes in levels of MMP-9 at different testing times., Conclusions: 1. With similar results but less sensitivity and significant time saving, the bulk fill technique can be considered an efficient alternative to the incremental fill technique in restoring proximal cavities. 2. Early evidence of caries recurrence can be correlated to an increase in the MMP-9 level in gingival crevicular fluid, followed by an increase in radiographic periodontal ligament width measurement., Trial Registration: An ethical approval from the Research Ethics Committee at the Faculty of Dentistry, October 6 University, (Approval No. RECO6U/5-2022). The study was registered at the Pan African Clinical Trials Registry on 24/07/2023 with an identification number (PACTR202307573531455)., (© 2024. The Author(s).)

Objective: To investigate the outcomes and factors influencing the success of vital pulp treatment (VPT) in permanent teeth with carious pulp exposure and signs and symptoms of irreversible pulpitis (IP)., Materials and Methods: Dental records from 2016 to 2023 of patients who received VPT for carious pulp-exposed permanent teeth with IP symptoms were reviewed. Outcomes were assessed using clinical and radiographic criteria. Univariate and multivariate analyses were performed to identify factors affecting treatment success. The Kaplan-Meier estimator was used to compare survival probabilities., Results: The overall success and survival rates were 88.24% and 97.06%, respectively. Success rates for direct pulp capping, partial pulpotomy, and full pulpotomy were 87.7%, 92.0%, and 87.7%, respectively. Key factors influencing outcomes included the presence of periapical radiographic lesions, type of pulp dressing material, and quality of the final restoration. Full pulpotomy demonstrated the highest survival time, followed by partial pulpotomy and direct pulp capping., Conclusion: VPT in permanent teeth with carious pulp exposure and IP symptoms shows high success rates. Crucial factors include periapical radiographic findings, type of pulp dressing material, and quality of the final restoration. Full pulpotomy exhibited the longest survival time., Clinical Relevance: VPT in teeth with IP symptoms shows promising outcomes, highlighting the importance of certain clinical factors in treatment success., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To develop a no-code artificial intelligence (AI) model capable of identifying primary proximal surface caries using bitewings among pediatric patients. Methods: One hundred bitewing radiographs acquired at pediatric dental clinics were anonymized and reviewed. The inclusion criteria encompassed bitewing radiographs of adequate diagnostic quality of primary and mixed-dentition stages. The exclusion criteria included artifacts related to sensors' quality, positioning errors, and motion. Sixty-six bitewing radiographs were selected. Images were uploaded to LandingLens™, a no-code AI platform. A calibrated consensus panel determined the presence or absence of proximal caries lesions on all surfaces (using ground truth labeling). The radiographs were divided into training (70 percent), development (20 percent), and testing (10 percent) subsets. Data augmentation techniques were applied to artificially increase the sample size. Sensitivity, specificity, accuracy, precision, F1-score, and receiver operating characteristic area under the curve (ROC-AUC) were calculated. Results: Among the 755 proximal surfaces identified from 66 bitewings, 178 were annotated as caries lesions by experts. The model achieved the following metrics: 88.8 percent sensitivity, 98.8 percent specificity, 95.8 percent precision, 96.4 percent accuracy, and an F1-score of 92 percent by surface. The ROC-AUC was 0.965. Conclusions: The developed model demonstrated strong performance despite the limited dataset size. This may be attributed to the exclusion of unsuitable radiographs and the use of expert-labeled ground truth annotations. The utilization of no-code artificial intelligence may improve outcomes for computer vision tasks.

Camera image-based deep learning (DL) techniques have achieved promising results in dental caries screening. To apply the intraoral camera image-based DL technique for dental caries detection and assess its diagnostic performance, we employed the ensemble technique in the image classification task. 2,682 intraoral camera images were used as the dataset for image classification according to dental caries presence and caries-lesion localization using DL models such as ResNet-50, Inception-v3, Inception-ResNet-v2, and Faster R-convolutional neural network according to diagnostic study design. 534 participants whose mean age [SD] was 47.67 [±13.94] years were enrolled. The dataset was divided into training (56.0%), validation (14.0%), and test subset (30.0%) annotated by one experienced dentist as a reference standard about dental caries detection and lesion location. The confusion matrix, area under the receiver operating characteristic curve (AUROC), and average precision (AP) were evaluated for performance analysis. In the end-to-end dental caries image classification, the ensemble DL models had consistently improved performance, in which as the best results, the ensemble model of Inception-ResNet-v2 achieved 0.94 of AUROC and 0.97 of AP. On the other hand, the explainable model achieved 0.91 of AUROC and 0.96 of AP after the ensemble application. For dental caries classification using intraoral camera images, the application of ensemble techniques exhibited consistently improved performance regardless of the DL models. Furthermore, the trial to create an explainable DL model based on carious lesion detection yielded favorable results., Competing Interests: NO authors have competing interests, (Copyright: © 2024 Kang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objective: The objective was to measure the thickness of Streptococcus mutans (S. mutans) biofilms forming in an oral biofilm reactor (OBR) by using a noninvasive swept-source optical coherence tomography (SS-OCT) system at every 4 h time interval until 20 h and analyze the correlations with the amounts of biofilms., Methods: S. mutans biofilms were formed on square-shaped bovine enamel blocks inside an OBR. Biofilms were analyzed at every 4 h stage (4 h, 8 h, 12 h, 16 h and 20 h) using a SS-OCT system and a laser scanning confocal microscope (LSCM). The amounts of biofilms were measured at each stage by separating the water insoluble glucan (WIG) and bacterial cells. Co-relationships between the SS-OCT measured biofilm thickness and the amounts of adhered biofilms were analyzed., Results: The thickness of biofilms detected on SS-OCT images at 4 h stage was 0.059 ± 0.029 (Av ± SD) mm which increased time-dependently in a linear fashion after 8 h stage and reached to 0.435 ± 0.159 mm at 20 h stage and the correlation coefficient was about 0.89. The amounts of biofilms; bacterial optical density (OD) and WIG concentration increased time-dependently were 0.035 ± 0.008 / mm 2 and 10.328 ± 2.492 µg/ mm 2 respectively at 20 h stage. Correlation coefficients of 0.66 between 'the amounts of bacteria' and 'biofilm thickness on OCT' and 0.67 between 'the amounts of WIG' and 'biofilm thickness on OCT' were obtained, suggesting that there was a relatively positive correlation between them., Conclusion: The SS-OCT can be a useful tool to measure time-dependent growth of biofilms. Further studies are needed in order to assess biofilms using SS-OCT more accurately., Competing Interests: Declaration of Competing Interest The authors declare no financial interests., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Diagnosing dental caries poses a significant challenge in dentistry, necessitating precise and early detection for effective management. This study utilizes Self-Supervised Learning (SSL) tasks to improve the classification of dental caries in Cone Beam Computed Tomography (CBCT) images, employing the International Caries Detection and Assessment System (ICDAS). Faced with the challenge of scarce annotated medical images, our research employs SSL to utilize unlabeled data, thereby improving model performance. We have developed a pipeline incorporating unlabeled data extraction from CBCT exams and subsequent model training using SSL tasks. A distinctive aspect of our approach is the integration of image processing techniques with SSL tasks, along with exploring the necessity for unlabeled data. Our research aims to identify the most effective image processing techniques for data extraction, the most efficient deep learning architectures for caries classification, the impact of unlabeled dataset sizes on model performance, and the comparative effectiveness of different SSL approaches in this domain. Among the tested architectures, ResNet-18, combined with the SimCLR task, demonstrated an average F1-score macro of 88.42%, Precision macro of 90.44%, and Sensitivity macro of 86.67%, reaching a 5.5% increase in F1-score compared to models using only deep learning architecture. These results suggest that SSL can significantly enhance the accuracy and efficiency of caries classification in CBCT images., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Luiz Guilherme Kasputis Zanini reports financial support was provided by Centro de ciéncia de dados. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The aim of this study was to explore the feasibility of a time-resolved reflectance imaging system employing a single photodetector to assess the activity of caries lesions that exploits the differential absorption of water at 1300 and 1950 nm. The time-resolved reflectivity of 10 active and 10 arrested lesions on the proximal surfaces and 5 active and 5 arrested lesions on the occlusal surfaces of extracted teeth were monitored simultaneously at 1300 and 1950 nm during forced air drying for 60 s. The presence of a highly mineralized surface zone measured with microcomputed tomography (microCT) was used to indicate lesion activity. Multiple kinetic parameters were extracted from the acquired short wavelength infrared (SWIR) intensity versus time dehydration curves and used to assess lesion activity. Differences in the reflectivity between curves acquired at 1300 and 1950 nm due to differential absorption of water provided improved discrimination between active and arrested lesions over the use of 1950 nm alone. This study demonstrates that it is feasible to use a device with a single photodetector operating at 1950 nm to collect dehydration curves for the assessment of lesion activity and that a system employing two SWIR wavelengths with differential water absorption can improve the performance of lesion activity assessment., (© 2024. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.)

Background: Enamel is highly transparent at short wavelength infrared imaging (SWIR) wavelengths allowing the detection of dental decay without the need for ionizing radiation. The purpose of this study was to use SWIR imaging methods including cross polarization optical coherence tomography (CP-OCT), occlusal transillumination (SWIR-OT), proximal transillumination (SWIR-PT), and occlusal reflectance (SWIR-R) to image interproximal lesions in vivo and compare the sensitivity with radiography., Methods: Participants (n = 30) aged 18-80 each with a radiopositive interproximal lesion scheduled for restoration were enrolled in the study. Studies have shown that the opposing proximal surfaces across the contact will likely also have lesions. SWIR images were acquired of the adjoining teeth at each contact with an interproximal lesion scheduled for restoration. Lesion presence and depth were assessed on each side of the contact for radiography and each SWIR imaging method. Lesions on radiographs and in CP-OCT images were identified by a single examiner while lesions in SWIR images were identified by a contrast threshold via semi-automatic image segmentation., Results: All SWIR imaging methods had significantly higher sensitivity (P < 0.05) than radiographs for the detection of interproximal lesions on the teeth opposite those restored. CP-OCT and SWIR-R imaging methods had significantly higher sensitivity than the other methods. SWIR imaging methods showed significantly higher lesion contrast than radiography., Conclusions: SWIR imaging methods can be used to detect interproximal lesions on posterior teeth with higher diagnostic performance than radiographs. CP-OCT appears well suited as a potential gold standard for the detection of interproximal lesions and assessment of their severity in vivo., (© 2024. The Author(s).)

Background and Aim: Dental caries is largely preventable, yet an important global health issue. Numerous systematic reviews have summarized the efficacy of artificial intelligence (AI) models for the diagnosis and detection of dental caries. Therefore, this umbrella review aimed to synthesize the results of systematic reviews on the application and effectiveness of AI models in diagnosing and detecting dental caries., Methods: MEDLINE/PubMed, IEEE Explore, Embase, and Cochrane Database of Systematic Reviews were searched to retrieve studies. Two authors independently screened the articles based on eligibility criteria and then, appraised the included articles. The findings are summarized in tabulation form and discussed using the narrative method., Result: A total of 1249 entries were identified out of which 7 were finally included. The most often employed AI algorithms were the multilayer perceptron, support vector machine (SVM), and neural networks. The algorithms were built to perform the segmentation, classification, caries detection, diagnosis, and caries prediction from several sources, including periapical radiographs, panoramic radiographs, smartphone images, bitewing radiographs, near-infrared light transillumination images, and so forth. Convoluted neural networks (CNN) demonstrated high sensitivity, specificity, and area under the curve in the caries detection, segmentation, and classification tests. Notably, AI in conjunction with periapical and panoramic radiography images yielded better accuracy in detecting and diagnosing dental caries., Conclusion: AI models, especially convolutional neural network (CNN)-based models, have an enormous amount of potential for accurate, objective dental caries diagnosis and detection. However, ethical considerations and cautious adoption remain critical to its successful integration into routine practice., (© 2024 The Author(s). Clinical and Experimental Dental Research published by John Wiley & Sons Ltd.)

Dental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, detecting, and segmenting caries in X-ray images. Following the PRISMA methodology, we selected relevant studies and analyzed their methods, strengths, limitations, imaging modalities, evaluation metrics, datasets, and classification techniques. The review encompassed 42 studies retrieved from the Science Direct, IEEExplore, ACM Digital, and PubMed databases from the Computer Science and Health areas. The results indicate that 12% of the included articles utilized public datasets, with deep learning being the predominant approach, accounting for 69% of the studies. The majority of these studies (76%) focused on classifying dental caries, either in binary or multiclass classification. Panoramic imaging was the most commonly used radiographic modality, representing 29% of the cases studied. Overall, our systematic review provides a comprehensive overview of the computational methods employed in identifying caries in radiographic images and highlights trends, patterns, and challenges in this research field., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Introduction: Intraoral scanners commonly used in orthodontic offices now offer near-infrared imaging (NIRI) technology, advertised as a screening tool to identify interproximal caries. This study aimed to evaluate the reliability and validity of NIRI detection of interproximal carious lesions in a common intraoral scanner (iTero Element 5D; Align Technology, San Jose, Calif) with and without bitewing radiograph complement, compared with a microcomputed tomography (micro-CT) reference standard., Methods: Extracted human posterior teeth (premolars and molars) were selected for early (noncavitated) interproximal carious lesions (n = 39) and sound control surfaces (n = 47). The teeth were scanned via micro-CT for evaluation by 2 blinded evaluators using consensus scoring. The teeth were mounted to simulate anatomic interproximal contacts and underwent a NIRI scan using iTero Element 5D and bitewing radiographs. Two trained, calibrated examiners independently evaluated (1) near-infrared images alone with clinical photograph, (2) bitewing radiograph alone with clinical photograph, and (3) near-infrared images with bitewing radiograph and clinical photograph in combination, after at least a 10-day washout period between each evaluation., Results: Interrater reliability was highest for NIRI alone (k = 0.533) compared with bitewing radiograph alone (k = 0.176) or in combination (k = 0.256). NIRI alone showed high specificity (0.83-0.96) and moderate sensitivity (0.42-0.63) compared with a micro-CT reference standard. Dentin lesions were significantly more reliably detected than enamel lesions., Conclusions: After rigorous training and calibration, NIRI can be used with moderate reliability, high specificity, and moderate sensitivity to detect noncavitated interproximal carious lesions., (Copyright © 2024 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.)

Objectives: This study aimed to assess the reliability of AI-based system that assists the healthcare processes in the diagnosis of caries on intraoral radiographs., Methods: The proximal surfaces of the 323 selected teeth on the intraoral radiographs were evaluated by two independent observers using an AI-based (Diagnocat) system. The presence or absence of carious lesions was recorded during Phase 1. After 4 months, the AI-aided human observers evaluated the same radiographs (Phase 2), and the advanced convolutional neural network (CNN) reassessed the radiographic data (Phase 3). Subsequently, data reflecting human disagreements were excluded (Phase 4). For each phase, the Cohen and Fleiss kappa values, as well as the sensitivity, specificity, positive and negative predictive values, and diagnostic accuracy of Diagnocat, were calculated., Results: During the four phases, the range of Cohen kappa values between the human observers and Diagnocat were κ=0.66-1, κ=0.58-0.7, and κ=0.49-0.7. The Fleiss kappa values were κ=0.57-0.8. The sensitivity, specificity and diagnostic accuracy values ranged between 0.51-0.76, 0.88-0.97 and 0.76-0.86, respectively., Conclusions: The Diagnocat CNN supports the evaluation of intraoral radiographs for caries diagnosis, as determined by consensus between human and AI system observers., Clinical Significance: Our study may aid in the understanding of deep learning-based systems developed for dental imaging modalities for dentists and contribute to expanding the body of results in the field of AI-supported dental radiology.., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: David Manulis, Matyey Ezhov and Alex Sanders are employees of Diagnocat Co. Ltd.. Kaan Orhan is a scientific research advisor for the Diagnocat Co. Ltd., San Francisco CA., (Copyright © 2024. Published by Elsevier Ltd.)

The traditional methods in early caries detection had many limitations. So, this study aimed to evaluate the clinical performance of alternating current impedance spectroscopy ACIST in comparison with digital radiograph and ICDAS-II in detection of occlusal carious lesions. Occlusal surfaces of molar and premolar teeth from 40 adult participants were examined by two observers using three diagnostic methods: (1) international caries detection and assessment system (ICDAS-II) (2) digital radiograph (DR) and (3) Cariescan Pro device (ACIST). Agreement analysis and the difference in sensitivities and specificities were evaluated. The results showed an excellent agreement in the different groups. The difference from the visual tactile against ACIST scoring for enamel caries detection, was statistically significant (p = 0.012) and the agreement was moderate (k = 0.509). For dentinal caries the difference was not statistically significant (p > 0.05) and the agreement was similarly moderate (k < 0.6). The difference from the digital radiograph against ACIST scoring, for enamel caries, digital radiography had significantly higher sensitivity and specificity than ACIST (p < 0.001) while for dentinal caries detection and overall, ACIST had higher sensitivity and digital radiography had higher specificity and the difference was statistically significant (p < 0.001). Visual-tactile examination is a considered as feasible and valid technique for occlusal caries detection, digital radiography is superior to ACIST in diagnosing enamel caries, but it could underestimate the caries depth, ACIST is a reliable tool for detecting occlusal caries in dentin., (© 2024. The Author(s).)

Intraoral scanners are widely used in a clinical setting for orthodontic treatments and tooth restorations, and are also useful for assessing dental wear and pathology progression. In this study, we assess the utility of using an intraoral scanner and associated software for quantifying dental tissue loss in non-human primates. An upper and lower second molar for 31 captive hamadryas baboons ( Papio hamadryas ) were assessed for dental tissue loss progression, giving a total sample of 62 teeth. The animals are part of the Southwest National Primate Research Center and were all fed the same monkey-chow diet over their lifetimes. Two molds of each dentition were taken at either two- or three-year intervals, and the associated casts scanned using an intraoral scanner (Medit i700). Tissue loss was calculated in WearCompare by superimposition of the two scans followed by subtraction analysis. Four individuals had dental caries, and were assessed separately. The results demonstrate the reliability of these techniques in capturing tissue loss data, evidenced by the alignment consistency between scans, lack of erroneous tissue gain between scans, and uniformity of tissue loss patterns among individuals ( e.g. , functional cusps showing the highest degree of wear). The average loss per mm 2 per year for all samples combined was 0.05 mm 3 (0.04 mm 3 for females and 0.08 mm 3 for males). There was no significant difference in wear progression between upper and lower molars. Substantial variation in the amount of tissue loss among individuals was found, despite their uniform diet. These findings foster multiple avenues for future research, including the exploration of wear progression across dental crowns and arcades, correlation between different types of tissue loss ( e.g. , attrition, erosion, fractures, caries), interplay between tissue loss and microwear/topographic analysis, and the genetic underpinnings of tissue loss variation., Competing Interests: The authors declare there are no competing interests., (©2024 Towle et al.)

Objectives: Bitewing radiography is considered to be of high diagnostic value in caries detection, but owing to projections, lesions may remain undetected. The novel bitewing plus (BW +) technology enables scrolling through radiographs in different directions and angles. The present study aimed at comparing BW + with other 2D and 3D imaging methods in terms of sensitivity, specificity, and user reliability., Materials and Methods: Five human cadavers were used in this study. In three cadavers, natural teeth were transplanted post-mortem. BW + , two-dimensional (digital sensors, imaging plates, 2D and 3D bitewing radiographs) and 3D methods (high and low dose CBCT) were taken. Carious lesions were evaluated on 96 teeth at three positions (mesial, distal, and occlusal) and scored according to their level of demineralization by ten observers, resulting in 35,799 possible lesions across all observers and settings. For reference, µCT scans of all teeth were performed., Results: Overall, radiographic evaluations showed a high rate of false-negative diagnoses, with around 70% of lesions remaining undetected, especially enamel lesions. BW + showed the highest sensitivity for dentinal caries and had comparatively high specificity overall., Conclusions: Within the limits of the study, BW + showed great potential for added diagnostic value, especially for dentinal caries. However, the tradeoff of diagnostic benefit and radiation exposure must be considered according to each patient's age and risk., (© 2024. The Author(s).)

Quantitative analysis of optical attenuation based on optical coherence tomography images will offer an effective method to enhance diagnostic capabilities. In this paper, the optical attenuation in demineralized caries specimens was calculated to distinguish between normal teeth and carious teeth and further to differentiate the severity of caries, and thus come to the half-automated diagnosis of dental caries. Results show that the attenuation coefficient in carious regions is approximately 4.97 mm - 1 ± 0.206 , while that of normal teeth is about 3.69 mm - 1 ± 0.231 . Attenuation coefficient of carious regions is 35% higher than that of normal teeth. Moreover, five classes of caries were qualified and classified based on the optical attenuation coefficient. Compared with the healthy teeth, there is a noticeable disparity in the attenuation coefficients of carious teeth, both on the surface and at the dentinoenamel junction. This study provides a method for accurate caries diagnosis, particularly in detection of early lesions and subtle structural changes., (© 2024 Wiley‐VCH GmbH.)

Objectives: The aim of this study was to develop an assessment tool for automatic detection of dental caries in periapical radiographs using convolutional neural network (CNN) architecture., Methods: A novel diagnostic model named ResNet + SAM was established using numerous periapical radiographs (4278 images) annotated by medical experts to automatically detect dental caries. The performance of the model was compared to the traditional CNNs (VGG19, ResNet-50), and the dentists. The Gradient-weighted Class Activation Mapping (Grad-CAM) technique shows the region of interest in the image for the CNNs., Results: ResNet + SAM demonstrated significantly improved performance compared to the modified ResNet-50 model, with an average F1 score of 0.886 (95% CI 0.855-0.918), accuracy of 0.885 (95% CI 0.862-0.901) and AUC of 0.954 (95% CI 0.924-0.980). The comparison between the performance of the model and the dentists revealed that the model achieved higher accuracy than that of the junior dentists. With the assist of the tool, the dentists achieved superior metrics with a mean F1 score of 0.827 and the interobserver agreement for dental caries is enhanced from 0.592/0.610 to 0.706/0.723., Conclusions: According to the results obtained from the experiments, the automatic assessment tool using the ResNet + SAM model shows remarkable performance and has excellent possibilities in identifying dental caries. The use of the assessment tool in clinical practice can be of great benefit as a clinical decision-making support in dentistry and reduce the workload of dentists., (© 2024. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Objectives: This study investigated the effectiveness of a deep convolutional neural network (CNN) in diagnosing and staging caries lesions in quantitative light-induced fluorescence (QLF) images taken by a self-manufactured handheld device., Methods: A small toothbrush-like device consisting of a 400 nm UV light-emitting lamp with a 470 nm filter was manufactured for intraoral imaging. A total of 133 cases with 9,478 QLF images of teeth were included for caries lesion evaluation using a CNN model. The database was divided into development, validation, and testing cohorts at a 7:2:1 ratio. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated for model performance., Results: The overall caries prevalence was 19.59%. The CNN model achieved an AUC of 0.88, an accuracy of 0.88, a specificity of 0.94, and a sensitivity of 0.64 in the validation cohort. They achieved an overall accuracy of 0.92, a sensitivity of 0.95 and a specificity of 0.55 in the testing cohort. The model can distinguish different stages of caries well, with the best performance in detecting deep caries followed by intermediate and superficial lesions., Conclusions: Caries lesions have typical characteristics in QLF images and can be detected by CNNs. A QLF-based device with CNNs can assist in caries screening in the clinic or at home., Trial Registration: The clinical trial was registered in the Chinese Clinical Trial Registry (No. ChiCTR2300073487, Date: 12/07/2023)., (© 2024. The Author(s).)

Purpose: To compare the applicability of modified US Public Health Service (USPHS) and FDI criteria for evaluating glass ionomer cement (GIC) restorations in primary posterior teeth through digital image analysis., Methods: This comparative analytic study was conducted at the Children's Dental Clinic RSKGM FKG UI, involving 40 GIC restorations on lower first primary molars in children aged 4-9 years. After cleaning, the restorations were assessed clinically using modified USPHS and FDI criteria before taking digital images, then the collected images were re-evaluated using both sets of criteria, and the clinical assessment results were compared to the digital image assessment results., Results: Statistical analysis revealed significant differences between the clinical evaluation of GIC restorations in primary teeth and their corresponding digital photographs when using the modified USPHS criteria, and although the use of FDI criteria yielded different results, these differences were not statistically significant., Conclusion: The assessment of GIC restorations through digital images aligns more closely with clinical assessments using the FDI criteria compared to the modified USPHS criteria., (© 2024. The Author(s), under exclusive licence to European Academy of Paediatric Dentistry.)

Purpose: To evaluate the proximal caries progression in primary molars using the radiographic International Caries Detection and Assessment System (ICDAS)., Methods: A study was conducted on 196 children aged 3-9 years old who underwent the clinical examination and bitewing radiography during baseline and 6-month (and over) follow-up visits. The primary molars bitewing radiographs with initial enamel caries (RA1 and RA2) or outer dentine caries (RA3) of proximal surfaces were included. Caries advancement was scored using ICDAS criteria and statistical analyses with the chi-square test. Median survival time was evaluated using Kaplan-Meier survival curves and log-rank tests., Results: A total of 439 surfaces of primary molars were included in this study and an averaged follow-up period of enamel and dentine caries group were 18.3 ± 9.6 months and 16.5 ± 9.5 months respectively. The progression of proximal enamel lesions significantly differed between primary maxillary and mandibular molars (p = 0.002) and among each patient's primary mandibular second molar and the others (p = 0.002). On the contrary, the outer dentine caries of each group of primary molars was not different. The median survival time of the initial enamel proximal caries (23.30 months) was non-significantly longer than that of the dentine (20.80 months)., Conclusions: Progressions of the initial enamel proximal caries were significantly different among primary molars at the average 18.3-month follow-up. The median survival period of the enamel proximal caries was more extended than that of dentine but without statistical difference. These results provide essential information for dentists regarding an appropriate appointment for bitewing examinations., (© 2024. The Author(s), under exclusive licence to European Academy of Paediatric Dentistry.)

Objective: Radiographs are an integral part of detecting proximal caries. The aim of this study was to evaluate the effect of contrast, brightness, noise, sharpness, and γ adjustment of digital intraoral radiographs on the diagnosis of proximal caries., Materials and Methods: In this in vitro study, 40 extracted teeth including 20 premolars and 20 molars with enamel lesions (white spot or dentin discoloration seen through the enamel) were mounted together in groups of eight inside the skull. Bitewing radiographic images of each dental group were obtained by a photostimulable phosphor plate sensor with exposure conditions of 8 mA, 70 kV, and 0.2 s. The images were reconstructed by the built-in software and examined by two oral and maxillofacial radiologists in various settings of contrast, brightness, sharpness, noise, and γ. The teeth were then cut mesiodistally and the presence or absence of caries was confirmed by an oral and maxillofacial pathologist using a stereomicroscope. The data were then analyzed using the κ agreement coefficient, sensitivity, specificity, and accuracy (α = .05)., Results: Adjustment of brightness and contrast led to higher diagnostic performance with an accuracy of 82.5% and 83.8 (for observers 1 and 2, respectively) and 82.5% (for both observers), respectively. Noise adjustment was the least helpful approach for diagnosis of proximal dental caries among other adjustments, with an accuracy of 78.8% and 77.5% for observers 1 and 2, respectively., Conclusion: Brightness and contrast setting was more efficient in improving the diagnostic potential of bitewing radiographs compared to other adjustments., (© 2024 The Authors. Clinical and Experimental Dental Research published by John Wiley & Sons Ltd.)

Objective: This study aimed to evaluate the accuracy of an intraoral scanner with near-infrared imaging (NIRI) feature in the diagnosis of interproximal caries and to compare it with the visual-tactile method (VTM), bitewing radiography (BWR), and panoramic radiography (PR)., Materials and Methods: Six hundred thirty-nine interproximal surfaces (mesial-distal) of posterior teeth from 22 volunteers were examined. Results were scored by VTM, BWR, PR, and NIRI. Lesions were scored as 0 for no-caries, 1 for early-enamel lesion (EEL), and 2 for lesions involving dentino-enamel junction (DEJ). McNemar, Kappa, and Fleis Kappa tests were used to evaluate the agreement levels. Pearson's Chi-square test was used to determine the matching rates after validation., Results: A good level of agreement was observed between examination methods (Ƙ = 0.613; p < 0.001). In pairwise comparisons, a moderate agreement was seen between all the methods for lesions with DEJ involvement, while a statistically good agreement was observed between BWR and NIRI (Ƙ = 0.675; p < 0.001). As a result of validation, the accuracy of NIRI for molars was considered 85.2% and 75.7% for premolars in EELs, 85.2% for molars, and 70% for premolars regarding the lesions involving DEJ., Conclusions: Intraoral scanners with the NIRI feature may be used for diagnosing interproximal caries, especially for permanent molars., Clinical Significance: Early detection of proximal caries is one of the most essential topics forming the basis of preventive dentistry. This study investigates a caries diagnostic tool integrated into intraoral scanners to diagnose interproximal caries. A caries diagnostic tool integrated into an intraoral scanner may prevent the harmful effects of ionizing radiation in early caries diagnosis and may improve the patient's oral health status., (© 2024 The Authors. Journal of Esthetic and Restorative Dentistry published by Wiley Periodicals LLC.)

Objectives: This study aimed to evaluate the diagnostic performance of near-infrared imaging (NIRI) and unaided visual examination (UVE) in detecting proximal caries in permanent dentition in comparison with cone-beam computed tomography (CBCT)., Methods: Patients who underwent NIRI, UVE, and CBCT imaging within 1 week were enrolled. Using CBCT as the reference test, the positive percent agreement (PPA), negative percent agreement (NPA), and overall percent agreement (OPA) of NIRI, UVE, and a combination of the two for detecting proximal caries at different depths and in different tooth locations were assessed. Additionally, the consistency of these diagnostic methods with CBCT was evaluated., Results: We evaluated 6,084 proximal surfaces and identified 177 CBCT-positive sites. NIRI had a PPA, NPA, and OPA of 68.93 %, 99.09 %, and 98.21 %, respectively, with a substantial agreement with CBCT. When combined with UVE, the PPA increased by approximately 50 % compared with that of UVE alone. Regarding caries at different depths, NIRI outperformed UVE in detecting initial caries (ICDAS 1-2) over moderate-to-advanced caries (ICDAS 3-6). However, the combined use of NIRI and UVE improved the detection of moderate-to-advanced caries. In the anterior teeth region, NIRI exhibited excellent agreement with CBCT, surpassing its performance in the posterior region., Conclusions: Although NIRI cannot fully replace radiographic methods, the substantial agreement of NIRI with CBCT in detecting proximal caries highlights its potential as a complementary tool in routine caries screening, especially when combined with UVE., Clinical Significance: This study highlights the potential of NIRI as a radiation-free method for detecting proximal caries in permanent teeth. Early detection through regular NIRI scanning can lead to timely intervention, improved patient outcomes, and reduced overall disease burden., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

This paper introduces a novel application of the laser speckle technique in dentistry, focusing on assessing the efficiency of dental excavation methods used to remove decayed tooth structure. The aim is to evaluate the efficiency of two chemo-mechanical agents and the high-speed drill using the laser speckle technique, which offers objective, non-invasive, and real-time evaluation capabilities. Extracted human primary molars with active occlusal carious lesions were sectioned into three parts, with each part allocated to one of three groups: Group 1 (Brix3000 ® ), Group 2 (Papacarie DUO ® ), and Group 3 (High-speed drill mechanical caries removal). Caries removal was performed using the designated agent or method for each group. After caries excavation, speckle imaging using a 632.8 nm laser was conducted. Additionally, SEM was used to acquire micro-photographs of the surface morphology of the treated samples. The findings reveal insights into the comparative efficiency of the three dental excavation agents and methods using the laser speckle technique. The speckle parameters extracted from speckle patterns generated by treated teeth provide valuable information for evaluating the performance of the excavation methods. The scanning electron microscopy images also offer detailed visual evidence to support the analysis. This paper demonstrates the potential of the laser speckle technique for assessing the efficiency of dental excavation methods. The objective, non-invasive, and real-time evaluation provided offers advantages over subjective visual assessment and manual measurements., (© 2024. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.)

Background: With the development and utilization of three-dimensional (3D) intraoral scanning (IOS) technology, the morphological characteristics of teeth were quantitatively assessed. In this research, we aimed to explore the prevalence of dental caries in relation to each measurable morphological indicator of the tooth body via 3D intraoral scanning techniques., Methods: A hospital-based single-centre study was conducted at our hospital from Dec. 2021 to Apr. 2023. A total of 53 patients were involved in the study, providing complete morphological data for 79 teeth. Each patient completed an oral hygiene routine questionnaire and underwent examination by an experienced dentist to evaluate caries conditions before undergoing 3D intraoral scanning to obtain a digital dental model. Geomagic Studio 2014 was used to extract oral morphological data from the models. The acquired data were entered, cleaned and edited using Excel 2016 and subsequently exported to SPSS version 25.0 for analysis. Chi-square analysis and logistic regression analyses were employed to test the associations., Results: Among the participants, 33 (61.1%) were female, with a mean age of 26.52 ± 10.83 years. Significant associations were found between dental caries and the vertical distance between the distal tip and the gum (OR 14.02; 95% CI 1.80-109.07; P = 0.012), the distal lateral horizontal distance of occlusion (OR 0.40; 95% CI 0.18-0.90; P = 0.026), and the mesial horizontal distance of occlusion (OR 2.20; 95% CI 1.12-4.31; P = 0.021). The Hosmer-Lemeshow test indicated a P value of 0.33., Conclusions: The vertical distance between the distal tip and the gum, the distal lateral horizontal distance of the occlusion and the mesial horizontal distance of the occlusion were the influencing factors for dental caries (identified as independent risk factors). We hypothesize that these factors may be associated with the physiological curvature of teeth and the role of chewing grooves in plaque formation over time. However, further studies involving larger population samples and more detailed age stratification are still needed., (© 2024. The Author(s).)

Objectives: Deep networks have been preliminarily studied in caries diagnosis based on clinical X-ray images. However, the performance of different deep networks on caries detection is still unclear. This study aims to comprehensively compare the caries detection performances of recent multifarious deep networks with clinical dentist level as a bridge., Methods: Based on the self-collected periapical radiograph dataset in clinic, four most popular deep networks in two types, namely YOLOv5 and DETR object detection networks, and UNet and Trans-UNet segmentation networks, were included in the comparison study. Five dentists carried out the caries detection on the same testing dataset for reference. Key tooth-level metrics, including precision, sensitivity, specificity, F1-score and Youden index, were obtained, based on which statistical analysis was conducted., Results: The F1-score order of deep networks is YOLOv5 (0.87), Trans-UNet (0.86), DETR (0.82) and UNet (0.80) in caries detection. A same ranking order is found using the Youden index combining sensitivity and specificity, which are 0.76, 0.73, 0.69 and 0.64 respectively. A moderate level of concordance was observed between all networks and the gold standard. No significant difference (p > 0.05) was found between deep networks and between the well-trained network and dentists in caries detection., Conclusions: Among investigated deep networks, YOLOv5 is recommended to be priority for caries detection in terms of its high metrics. The well-trained deep network could be used as a good assistance for dentists to detect and diagnose caries., Clinical Significance: The well-trained deep network shows a promising potential clinical application prospect. It can provide valuable support to healthcare professionals in facilitating detection and diagnosis of dental caries., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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)., (© 2024. The Author(s).)

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™., (© 2024. The Author(s).)

Objectives: This study aimed to investigate the caries diagnosis performances of dental students after training with an artificial intelligence (AI) application utilizing deep learning techniques, a type of artificial neural network., Methods: A total of 1200 bitewing radiographs were obtained from the institution's database and two specialist dentists labeled the caries lesions in the images. Randomly selected 1000 images were used for training purposes and the remaining 200 radiographs were used to evaluate the caries diagnostic performance of the AI. Then, a convolutional neural network, a deep learning algorithm commonly employed to analyze visual imagery problems, called "You Only Look Once," was modified and trained to detect enamel and dentin caries lesions in the radiographs. Forty dental students were selected voluntarily and randomly divided into two groups. The pre-test results of dental caries diagnosis performances of both groups were recorded. After 1 week, group 2 students were trained using an AI application. Then, the post-test results of both groups were recorded. The labeling duration of the students was also measured and analyzed., Results: When both groups' pre-test and post-test results were evaluated, a statistically significant improvement was found for all parameters examined except precision score (p < 0.05). However, the trained group's accuracy, sensitivity, specificity, and F1 scores were significantly higher than the non-trained group in terms of post-test scores (p < 0.05). In group 2 (trained group), the post-test labeling time was considerably increased (p < 0.05)., Conclusions: The students trained by AI showed promising results in detecting caries lesions. The use of AI can also contribute to the clinical education of dental students., (© 2024 American Dental Education Association.)

Purpose: The potential of combining teledentistry and engaging parents as underutilised resources to monitor paediatric dental health was emphasised during the COVID-19 pandemic and remains underexplored. This study aims to assess parental acceptance and use of a commercially available intraoral camera (IOC) for effective remote monitoring., Methods: 47 child-parent dyads, where the parent was the main caregiver and the child was treated under general anaesthesia for early childhood caries, were recruited. Caregivers were trained to image their child's teeth on a commercially available IOC. Subsequently, submitted images were reviewed asynchronously by dentists for image quality, presence of dislodged fillings, abscesses, cavitation, and oral hygiene. Post-surgery monitoring was performed using teledentistry at 1 and 2 months and in-person at 4 months. A modified Telehealth Usability Questionnaire (TUQ) was used to record caregiver acceptance for study procedures., Results: A mean TUQ of 6.09 out of 7 was scored by caregivers. Caregiver-reported issues were limited to problems with technique and child uncooperativeness. The number of clear images during the second teledentistry review was improved compared to the first (p = 0.007). 68% of children liked having images of their teeth taken., Conclusion: This study supports the feasibility of using an IOC as a clinically appropriate avenue for teledentistry with a high level of caregiver-child acceptance., (© 2024. The Author(s), under exclusive licence to European Academy of Paediatric Dentistry.)

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., Competing Interests: Disclosure None of the authors have any commercial or financial conflicts of interest and any funding sources., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: Dental health has been getting increased attention. Timely detection of non-normal teeth (caries, residual root, retainer, teeth filling, etc.) is of great importance for people's health, well-being, and quality of life. This work proposes a rapid detection of non-normal teeth based on improved Mask R-CNN, aiming to achieve comprehensive screening of non-normal teeth on dental X-ray images., Methods: An improved Mask R-CNN based on attention mechanism was used to develop a non-normal teeth detection method trained on a high-quality annotated dataset, which can segment the whole mask of each non-normal tooth on the dental X-ray image immediately., Results: The average precision (AP) of the proposed non-normal teeth detection was 0.795 with an intersection-over-union of 0.5 and max detections (maxDets) of 32, which was higher than that of the typical Mask R-CNN method (AP = 0.750). In addition, validation experiments showed that the evaluation metrics (AP, recall, precision-recall (P-R) curve) of the proposed method were superior to those of the Mask R-CNN method. Furthermore, the experimental results indicated that proposed method exhibited a high sensitivity (95.65%) in detecting secondary caries. The proposed method took about 0.12 s to segment non-normal teeth on one dental X-ray image using the laptop (8G memory, NVIDIA RTX 3060 graphics processing unit), which was much faster than conventional manual methods., Conclusion: The proposed method enhances the accuracy and efficiency of abnormal tooth diagnosis for practitioners, while also facilitating early detection and treatment of dental caries to substantially lower patient costs. Additionally, it can enable rapid and objective evaluation of student performance in dental examinations., (© 2024. CARS.)

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., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interest: SM has been employed at 3Shape TRIOS A/S. When this study was conducted, SM was a PhD student working under an industrial PhD agreement between 3Shape TRIOS A/S and the University of Copenhagen, i.e. SM was employed at 3Shape TRIOS A/S and her salary was partially covered by 3Shape TRIOS A/S and partially by Innovation Fund Denmark. Currently, SM is no longer employed at 3Shape TRIOS A/S. The other co-authors declare no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Objectives: This study aimed to investigate the accuracy of deep learning algorithms to diagnose tooth caries and classify the extension and location of dental caries in cone beam computed tomography (CBCT) images. To the best of our knowledge, this is the first study to evaluate the application of deep learning for dental caries in CBCT images., Methods: The CBCT image dataset comprised 382 molar teeth with caries and 403 noncarious molar cases. The dataset was divided into a development set for training and validation and test set. Three images were obtained for each case, including axial, sagittal, and coronal. The test dataset was provided to a multiple-input convolutional neural network (CNN). The network made predictions regarding the presence or absence of dental decay and classified the lesions according to their depths and types for the provided samples. Accuracy, sensitivity, specificity, and F1 score values were measured for dental caries detection and classification., Results: The diagnostic accuracy, sensitivity, specificity, and F1 score for caries detection in carious molar teeth were 95.3%, 92.1%, 96.3%, and 93.2%, respectively, and for noncarious molar teeth were 94.8%, 94.3%, 95.8%, and 94.6%. The CNN network showed high sensitivity, specificity, and accuracy in classifying caries extensions and locations., Conclusions: This research demonstrates that deep learning models can accurately identify dental caries and classify their depths and types with high accuracy, sensitivity, and specificity. The successful application of deep learning in this field will undoubtedly assist dental practitioners and patients in improving diagnostic and treatment planning in dentistry., Clinical Significance: This study showed that deep learning can accurately detect and classify dental caries. Deep learning can provide dental caries detection accurately. Considering the shortage of dentists in certain areas, using CNNs can lead to broader geographic coverage in detecting dental caries., Competing Interests: Conflict of interest None disclosed., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

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.

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., (© 2024. The Author(s).)