Global Research Trends in the Detection and Diagnosis of Dental Caries: A Bibliometric Analysis.

This study aims to provide an overview of the global research trends in the detection and diagnosis of dental caries in the past 20 years. A literature search was conducted in the Scopus Database to retrieve studies on the diagnostic approaches for dental caries published from January 2003 to December 2023. The diagnostic approaches in the retrieved studies were examined and the studies were categorized according to the diagnostic approaches investigated. Bibliometric data including journals, countries, affiliations, authors, and numbers of citations of the publications were summarised. The publications' keyword co-occurrence was analysed using VOSviewer. This bibliometric analysis included 1879 publications investigating seven categories of caries diagnostic approaches, including visual and/or tactile (n = 459; 19%), radiation-based (n = 662; 27%), light-based (n = 771; 32%), ultrasound-based (n = 28; 1%), electric-based (n = 51; 2%), molecular-based (n = 196; 8%) diagnostic approaches, as well as AI-based diagnostic interpretation aids (n = 265; 11%). An increase in the annual number of publications on caries diagnostic approaches was observed in the past 20 years. Caries Research (n = 103) presented the highest number of publications on caries diagnostic approaches. The country with the highest number of publications was the United States (n = 1092). The University of São Paulo was the institution that published the highest number of articles (n = 195). The publication with the highest citation has been cited 932 times. VOS viewer revealed that the most frequently occurring keywords were 'Deep Learning', 'Artificial Intelligence', 'Laser Fluorescence' and 'Radiography'. This bibliometric analysis highlighted an emerging global research trend in the detection and diagnosis approaches for dental caries in the past 20 years. An evident increase in publications on molecular-based caries diagnostic approaches and AI-based diagnostic interpretation aids was perceived over the last 5 years.

Methods used for caries detection and diagnosis in Ontario dental practices: a cross-sectional survey.

BACKGROUND: Early detection of caries is essential for applying non-surgical treatment procedures and preventing the formation of cavitated lesions leading to unnecessary removal of tooth structure. Understanding dentists' preferences for caries detection tools can inform stakeholders about their strategies and knowledge of contemporary, evidence-based caries management approaches. However, there is a lack of research exploring the detection methods of caries commonly used by dentists in Ontario, Canada. The objective of this study was to investigate the methods of caries detection and diagnosis preferred by dentists in Ontario. METHODS: A 21-item self-reported survey was mailed to one thousand Ontario dental practices in the Winter of 2022. Descriptive and bivariate data analysis were performed to determine the associations between: demographics and professional practice characteristics (explanatory variables), and methods for detecting and diagnosing dental caries (outcome variables) using SPSS Statistics 29.0. RESULTS: A total of 325 dentists (33%) responded to the survey, with 274 answering all of the questions completely. The highest proportion of respondents were 35-44 years of age (32.8%) and male (53.4%). More than half of the respondents reported using a dental explorer to assess primary occlusal caries (57.6%), secondary caries (57.1%), and cervical caries (57.5%). Likewise, 57.9% of the participants reported using dental radiographs to diagnose proximal caries. Among additional caries detection tools, digital radiography (89.8%) and traditional radiography (84.7%) were the most used methods/modalities, while cone beam computed tomography was the least (12.8%). Most study participants did not use any caries classification system (77.7%) or caries risk assessment tool (85.3%). CONCLUSIONS: Participants preferred conventional methods for caries detection, instead of contemporary visual-tactile caries lesions classification and/or caries risk assessment systems. These findings indicate a need for continuing dental education programs tailored to evidence-based caries management approaches.

Near-infrared light transillumination (NIR-LT) vs clinical-visual inspection/FOTI to detect interproximal caries lesions in vivo.

OBJECTIVE: To evaluate the ability of near-infrared light transillumination (NIR-LT) to detect interproximal enamel and dentinal caries lesions compared to clinical-visual inspection (VI) aided by fibre-optic transillumination (FOTI). METHOD AND MATERIALS: From 170 Finnish adolescents aged 15-17 years, 5294 interproximal surfaces of premolars and molars were examined first clinical-visually aided by FOTI (VI+FOTI) using the International Caries Detection and Assessment System (ICDAS) classification. Subsequently, the surfaces were examined using NIR-LT. The extent of lesions was determined using the modified NIR-LT classification based on the Söchtig criteria. For the analyses, data on upper and lower premolars and molars were combined. Distributions of lesions were presented as frequencies. Differences between VI+FOTI and NIR-LT at the tooth and tooth surface levels were analysed by Chi-square and Fisher's exact tests. Sensitivity and specificity of the NIR-LT method to detect any lesion was performed using VI+FOTI as the gold standard. RESULTS: By VI+FOTI, 92.4% surfaces were classified as sound and by NIR-LT, 88.2%. Enamel caries lesions were found on 7.0% of the surfaces by VI+FOTI and on 11.6% by NIR-LT. Enamel lesions identified by NIR-LT were nearly double for all examined teeth groups, except for lower molars it was 1.3-fold. In 66% of the surfaces, the differences between NIR-LT and VI+FOTI findings were statistically significant (p<0.001). The sensitivity for all teeth of NIR-LT was 48.4% and the specificity was 91.1%. CONCLUSION: Radiation-free NIR-LT method shows considerable potential as a supplementary method for early detection of caries lesions among low caries prevalence adolescents.

A Tele-detection and referral pAthways model for early childhood cariEs control- a protocol for a randomized factorial study: the TRACE study.

BACKGROUND: Early childhood caries (ECC) is one of the most common childhood diseases affecting the primary teeth of children younger than 6 years of age. ECC progression can be reversed in the early stages although these lesions often go undetected. New approaches are needed to detect oral diseases at an early stage when they can be better controlled. The aim of the study is to assess the effectiveness of ECC tele-detection methods combined with referral pathways with and without user fee removal in controlling ECC. METHODS: A randomized factorial trial will be used to compare two tele-dentistry detection methods for ECC (intraoral camera and smartphone camera) and two referral pathways (user fee removal versus conventional care). The study will recruit children younger than 6 years of age in marginalized communities in Alexandria, Egypt. The primary outcome is the percentage of teeth receiving indicated care, while the secondary outcomes are the oral health-related quality of life, acceptance of teledentistry by dentists, procedure time, and child cooperation. Two-way analysis of variance will be used to assess the effect of the two factors as between group variables on the outcomes after 6 and 12 months. The interaction between detection methods and referral pathways will also be assessed, and the effect of confounders will be controlled in a multivariable linear regression model. DISCUSSION: The findings of this study have the potential to inform clinical practice and oral healthcare policies for ECC management. Successful tele-detection and referral pathways could be integrated into oral healthcare systems, leading to improved oral health outcomes for children. TRIAL REGISTRATION: The trial has been registered on ClinicalTrials.gov in August 2023 (initial release) ID: NCT06019884.

Clinical SWIR and CP-OCT imaging of interproximal lesions.

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.

Diagnostic Strategies for Restorations Management: A 70-Month RCT.

We aimed to evaluate the impact of 2 visual diagnostic strategies for assessing secondary caries and managing permanent posterior restorations on long-term survival. We conducted a diagnostic cluster-randomized clinical trial with 2 parallel groups using different diagnostic strategies: (C+AS) based on caries assessment, marginal adaptation, and marginal staining aspects of the FDI (World Dental Federation) criteria and (C) based on caries assessment using the Caries Associated with Restorations or Sealants (CARS) criteria described by the International Caries Detection and Assessment System (ICDAS). The treatment for the restoration was conducted based on the decision made following the allocated diagnostic strategy. The restorations were then clinically reevaluated for up to 71 mo. The primary outcome was restoration failure (including tooth-level failure: pain, endodontic treatment, and extraction). Cox regression analyses with shared frailty were conducted in the intention-to-treat population, and hazard ratios (HRs) and 95% confidence intervals (95% CIs) were derived. We included 727 restorations from 185 participants and reassessed 502 (69.1%) restorations during follow-up. The evaluations occurred between 6 and 71 mo. At baseline, C led to almost 4 times fewer interventions compared with the C+AS strategy. A total of 371 restorations were assessed in the C group, from which 31 (8.4%) were repaired or replaced. In contrast, the C+AS group had 356 restorations assessed, from which 113 (31.7%) were repaired or replaced. During follow-up, 34 (9.2%) failures were detected in the restorations allocated to the C group and 30 (8.4%) allocated to the C+AS group in the intention-to-treat population, with no significant difference between the groups (HR = 0.83; 95% CI = 0.51 to 1.38; P = 0.435, C+AS as reference). In conclusion, a diagnostic strategy focusing on marginal defects results in more initial interventions but does not improve longevity over the caries-focused strategy, suggesting the need for more conservative approaches.

Correlation between Matrix Metalloproteinase Presence and Caries Surface Appearance.

INTRODUCTION: Cariogenic bacterial acids dissolve the inorganic elements in dentine, leaving the dentine matrix exposed. Host-derived matrix metalloproteinases (MMPs) play an essential role in caries progression as they are significant regulators of extracellular matrix turnover and can degrade exposed collagen. This paper investigates the expression of MMP2 and MMP9 across various stages of caries in primary human teeth and relate this with a diagnosis recorded by the International Caries Detection and Assessment System (ICDAS). METHODS: Twenty-four sections (150 µm in thickness) from extracted teeth, clinically diagnosed using ICDAS, were immunohistochemically treated with monoclonal anti-MMP2 and anti-MMP9 antibodies. Positive staining was visualised by immunofluorescence using a VectorFluor Duet Double Labeling Kit. Images from triplicate samples for each ICDAS score were analysed using ImageJ software. Collagen degradation in caries lesions was detected using a hydroxyproline assay. RESULTS: MMPs were weakly detected in caries with ICDAS 1-2 scores, and an insignificant increase was detected in ICDAS 3. However, a significant increase in MMP expression was seen in caries with an ICDAS score of 4-6. There was a strong positive correlation between the ICDAS score and MMP2 (r [6] = 0.86, p = 0.002) and between ICDAS and MMP9 (r [6] = 0.82, p = 0.004). Data were analysed using two-way ANOVA followed by Tukey multiple comparison test (*p < 0.05). CONCLUSION: The use of ICDAS to assess the severity of caries lesions and how this correlates with the presence of MMP in these lesions validates the modern approach to caries management with a minimally invasive concept.

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

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

Artificial intelligence enabled smart phone app for real-time caries detection on bitewing radiographs.

Diagnosis of proximal caries is a difficult task. Artificial intelligence (AI) enabled diagnosis is gaining momentum. Therefore, it is of interest to evaluate the effectiveness of an artificial intelligence (AI) smart phone application for bitewing radiography towards real-time caries lesion detection. The Efficient Det-Lite1 artificial neural network was used after training 100 radiographic images obtained from the department of Oral Medicine. Trained model was then installed in a Google Pixel 6 (GP6) smartphone as artificial intelligence app. The back-facing mobile phone video camera of GP6 was utilised to detect caries lesions on 100 bitewing radiographs (BWR) with 80 carious lesion in real-time. Two different techniques such as scanning the static BWR on laptop with a moving mobile and scanning the moving radiograph on the laptop with stationery mobile were used. The average value of sensitivity/precision/F1 scores for both the techniques was 0.75/0.846 and 0.795 respectively. AI programme using the rear-facing mobile phone video camera was found to detect 75% of caries lesions in real time on 100 BWR with a precision of 84.6%. Thus, the use of AI with smart phone app is useful for caries diagnosis which is readily accessible, easy to use and fast.

Salivary Diagnosis of Dental Caries: A Systematic Review.

The activity of dental caries, combined with its multifactorial etiology, alters salivary molecule composition. The present systematic review was developed to answer the following question: "Are salivary biomarkers reliable for diagnosis of dental caries?". Following the "Preferred Reporting Item for Systematic Reviews and Meta-analysis" (PRISMA) guidelines, the review was conducted using multiple database research (Medline, Web of Science, and Scopus). Studies performed on healthy subjects with and without dental caries and providing detailed information concerning the clinical diagnosis of caries (Decayed, Missing, Filled Teeth-DMFT and International Caries Detection and Assessment System-ICDAS criteria) were included. The quality assessment was performed following a modified version of the Joanna Briggs Institute Prevalence Critical Appraisal Checklist. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO, ID: CRD42022304505). Sixteen papers were included in the review. All studies reported statistically significant differences in the concentration of salivary molecules between subjects with and without caries (p < 0.05). Proteins were the most investigated molecules, in particular alpha-amylase and mucins. Some studies present a risk of bias, such as identifying confounding factors and clearly defining the source population. Nevertheless, the 16 papers were judged to be of moderate to high quality. There is evidence that some salivary compounds studied in this review could play an important diagnostic role for dental caries, such as salivary mucins, glycoproteins (sCD14), interleukins (IL-2RA, 4,-13), urease, carbonic anhydrase VI, and urea.

Clinical and ex-vivo effect of LASERs on prevention of early-enamel caries: systematic review & meta-analyses.

To investigate the in vivo and in situ effect of different types of lasers in prevention of enamel demineralization in high caries risk cases (around orthodontic brackets, around restoration and in caries susceptible pits and fissures). PubMed was searched using the following keyword sequence; (Laser therapy OR laser irradiation OR laser application) AND (enamel caries prevention OR enamel demineralization OR enamel remineralization OR early enamel caries OR early-enamel caries OR enamel resistance OR enamel decalcification OR white spot lesions WSLs OR incipient lesion OR enamel decay OR enamel Dissolution OR enamel microhardness) AND (clinical trial OR Randomized clinical trial OR In situ study). The latest literature search was ended by "30 January 2023". PubMed was used as a primary data base for study selection. Scopus, EBSCO, and Google scholar are checked in our study after results of systematic search on PubMed. Only duplicates were found. Two meta-analyses were carried out. The first, clinical meta-analysis on incidence of white spot lesions (WSLs) following CO2 laser irradiation of enamel. The second meta-analysis on ex-vivo/in situ effect of CO2 laser on microhardness of enamel. In each meta-analysis three studies were included. Risk of bias was assessed. The search identified eight studies (four ex-vivo and four clinical trials). Regarding the clinical meta-analysis, the overall standardized mean difference was 0.21 [ 95% confidence interval (CI): 0.15-0.30, p < 0.00001]. This indicates that the incidence of new WSLs in patients who received low power CO2 laser treatment was highly significantly lower than placebo groups. The heterogeneity was considerable (I2 = 71%). In the second meta-analysis, the overall standardized mean difference was 49.55 [ 95% confidence interval (CI): 37.74, 61.37, p < 0.00001]. This indicates that microhardness of enamel receiving low power (0.4-5 W) CO2 laser irradiation is highly significantly lower than control untreated enamel. The heterogeneity was substantial (I2 = 48%). Within the limitations of this study, Low level laser therapy concept with CO2 laser seems to be effective in preventing enamel caries.Prospero registration number: CRD42023437379.

Short-Wavelength Infrared Imaging of Infected and Affected Dentin.

Stains produced by bacteria or those found in blood and food byproducts accumulate in highly porous caries lesions. They can interfere with accurate diagnosis and the selective removal of carious tissue during cavity preparations. Short-wavelength infrared (SWIR) imaging studies have shown that stain molecules do not absorb light beyond 1200 nm. The objective of this study was to image affected and infected dentin atSWIR wavelengths. Sections of 3 mm thickness were cut from the extracted teeth with deep dentinal lesions. The sound (normal), affected (stained), and infected (demineralized) dentin on each section were examined with reflected light at wavelengths from 400 to 1700 nm, red and green fluorescence, and with optical coherence tomography (OCT). Microcomputed tomography (microCT) was used to measure the mineral density at each location investigated. Significant (p < 0.05) differences were observed in the reflected light intensity at 400-850 nm and for fluorescence between the sound, affected, and infected dentin. SWIR imaging did not show significant reductions in reflectivity for the affected and infected dentin. SWIR images may be valuable for monitoring the lateral spread of dentinal lesions on the occlusal surfaces of teeth.

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.

Performance comparison of multifarious deep networks on caries detection with tooth X-ray images.

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.

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.

Occlusal Caries Detection with Intraoral Scanners in Pediatric Dentistry: A Comparative Clinical Study.

Background: The aim of this clinical study was to compare the occlusal caries detection (OCD) performance of the intraoral scanners (IOSs) Trios 4 (TIO, 3Shape) and Emerald S (EME, Planmeca) and the Diagnocam (DIA, KaVo) with the established visual (WHO) examination (VIS, reference method). Methods: Between 08/2022 and 02/2023, 60 children (mean age 9.6 ± 2.5 years) were examined as part of their regular dental checkups. OCD was performed at the tooth level, separately for primary and permanent unrestored teeth. Furthermore, two thresholds were analyzed: sound versus overall caries (pooled data of enamel and dentin caries, TH1) and pooled data of sound and enamel caries versus dentin caries (TH2). Results: The best agreement with the reference method (reliability) in both dentitions was obtained for DIA (ĸ = 0.829/ĸ = 0.846; primary/permanent teeth), followed by EME (ĸ = 0.827/ĸ = 0.837) and TIO (ĸ = 0.714/ĸ = 0.680). Similar results were shown for the diagnostic quality (sensitivity, specificity and area under the curve of the receiver operating characteristic curve), with higher values for TH1 than for TH2. Both IOSs and the DIA showed worse results than the reference method VIS. Conclusions: Currently, IOS should be used as an additional caries detection tool, especially for visualization, and cannot be recommended as a basic tool for diagnosis or invasive/noninvasive therapy decisions in OCD.

Automatic caries detection in bitewing radiographs-Part II: experimental comparison.

OBJECTIVE: The objective of this study was to compare the detection of caries in bitewing radiographs by multiple dentists with an automatic method and to evaluate the detection performance in the absence of a reliable ground truth. MATERIALS AND METHODS: Four experts and three novices marked caries using bounding boxes in 100 bitewing radiographs. The same dataset was processed by an automatic object detection deep learning method. All annotators were compared in terms of the number of errors and intersection over union (IoU) using pairwise comparisons, with respect to the consensus standard, and with respect to the annotator of the training dataset of the automatic method. RESULTS: The number of lesions marked by experts in 100 images varied between 241 and 425. Pairwise comparisons showed that the automatic method outperformed all dentists except the original annotator in the mean number of errors, while being among the best in terms of IoU. With respect to a consensus standard, the performance of the automatic method was best in terms of the number of errors and slightly below average in terms of IoU. Compared with the original annotator, the automatic method had the highest IoU and only one expert made fewer errors. CONCLUSIONS: The automatic method consistently outperformed novices and performed as well as highly experienced dentists. CLINICAL SIGNIFICANCE: The consensus in caries detection between experts is low. An automatic method based on deep learning can improve both the accuracy and repeatability of caries detection, providing a useful second opinion even for very experienced dentists.

Detection of caries around restorations on bitewings using deep learning.

OBJECTIVE: Secondary caries lesions adjacent to restorations, a leading cause of restoration failure, require accurate diagnostic methods to ensure an optimal treatment outcome. Traditional diagnostic strategies rely on visual inspection complemented by radiographs. Recent advancements in artificial intelligence (AI), particularly deep learning, provide potential improvements in caries detection. This study aimed to develop a convolutional neural network (CNN)-based algorithm for detecting primary caries and secondary caries around restorations using bitewings. METHODS: Clinical data from 7 general dental practices in the Netherlands, comprising 425 bitewings of 383 patients, were utilized. The study used the Mask-RCNN architecture, for instance, segmentation, supported by the Swin Transformer backbone. After data augmentation, model training was performed through a ten-fold cross-validation. The diagnostic accuracy of the algorithm was evaluated by calculating the area under the Free-Response Receiver Operating Characteristics curve, sensitivity, precision, and F1 scores. RESULTS: The model achieved areas under FROC curves of 0.806 and 0.804, and F1-scores of 0.689 and 0.719 for primary and secondary caries detection, respectively. CONCLUSION: An accurate CNN-based automated system was developed to detect primary and secondary caries lesions on bitewings, highlighting a significant advancement in automated caries diagnostics. CLINICAL SIGNIFICANCE: An accurate algorithm that integrates the detection of both primary and secondary caries will permit the development of automated systems to aid clinicians in their daily clinical practice.

Accuracy of an intraoral scanner with near-infrared imaging feature in detection of interproximal caries of permanent teeth: An in vivo validation.

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.

AI-based dental caries and tooth number detection in intraoral photos: Model development and performance evaluation.

OBJECTIVES: In this study, we aimed to integrate tooth number recognition and caries detection in full intraoral photographic images using a cascade region-based deep convolutional neural network (R-CNN) model to facilitate the practical application of artificial intelligence (AI)-driven automatic caries detection in clinical practice. METHODS: Our dataset comprised 24,578 images, encompassing 4787 upper occlusal, 4347 lower occlusal, 5230 right lateral, 5010 left lateral, and 5204 frontal views. In each intraoral image, tooth numbers and, when present, dental caries, including their location and stage, were annotated using bounding boxes. A cascade R-CNN model was used for dental caries detection and tooth number recognition within intraoral images. RESULTS: For tooth number recognition, the model achieved an average mean average precision (mAP) score of 0.880. In the task of dental caries detection, the model's average mAP score was 0.769, with individual scores spanning from 0.695 to 0.893. CONCLUSIONS: The primary objective of integrating tooth number recognition and caries detection within full intraoral photographic images has been achieved by our deep learning model. The model's training on comprehensive intraoral datasets has demonstrated its potential for seamless clinical application. CLINICAL SIGNIFICANCE: This research holds clinical significance by achieving AI-driven automatic integration of tooth number recognition and caries detection in full intraoral images where multiple teeth are visible. It has the potential to promote the practical application of AI in real-life and clinical settings.