Evaluation of the effects of postprocessing settings in digital bitewing radiographs on proximal caries detection.

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.

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.

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.

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.

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.

Caries detection and characterization in pediatric patients using iTero 5D near-infrared technology.

INTRODUCTION: Near-infrared imaging (NIRI) has been proposed as an alternative to radiographs and uses nonionizing radiation in the near-infrared spectrum to differentially scatter light off tooth surfaces and generate images allowing interproximal caries detection. The new iTero 5D Element Scanner (Align Technology, Santa Clara, Calif) has integrated NIRI capture and viewing technology but has not been specifically studied in a pediatric population. Therefore, this study aimed to assess clinicians' abilities to detect and characterize caries in pediatric patients using this instrument. METHODS: Bitewing (BW) radiographs and an intraoral scan were captured on 17 pediatric patients (344 surfaces were analyzed). Data were randomized and graded by 5 calibrated clinicians individually with 2 different rounds of grading. RESULTS: The reliability of lesion characterization (ie, grade) among examiners was poor to fair in both systems, whereas the reliability of caries detection was moderate. Both systems had a high specificity and low sensitivity. The reliability of the characterization of the combined dataset was moderate to substantial, whereas, for detection, it was substantial. CONCLUSIONS: When using either BW or NIRI analysis, reliability is relatively poor, and clinicians are more likely to correctly identify a healthy tooth surface when compared with a carious surface. There is a small difference in error rate between BW and NIRI systems that is not likely to be clinically significant. When NIRI and BW data are combined, clinician agreement for both lesion characterization and detection increases significantly.

Radiation doses in extraoral bitewing radiography compared with intraoral bitewing and panoramic radiography.

OBJECTIVES: We compared the effective dose (E) and thyroid equivalent dose of 2 extraoral bitewing (EOBW) units and compared E with their respective panoramic (PAN) modes and with intraoral bitewing radiography (IOBW). STUDY DESIGN: Child and adult anthropomorphic phantoms with dosimeters were used to evaluate Orthophos SL, Rayscan α+, and 1 intraoral unit using rectangular and circular collimation. Extraoral bitewing thyroid equivalent dose was assessed without and with thyroid shielding. RESULTS: Child and adult E values of EOBW were lower with Orthophos (3.6 and 8.6 µSv) than with Rayscan (28.1 and 30.2 µSv). For IOBW, E was lower with rectangular vs circular collimation for child (7.0 vs 11.8 µSv) and adult (4.6 vs 14.2 µSv). E values of EOBW were lower than PAN for Orthophos. The IOBW E was lower than Rayscan EOBW for child (≤11.8 vs 28.1 µSv) and adult (≤14.2 vs 30.2 µSv). Adult E for rectangular IOBW (4.6 µSv) was lower than EOBW with Orthophos (8.6 µSv) and Rayscan (30.2 µSv). Thyroid shielding reduced EOBW thyroid equivalent dose with Rayscan in the adult from 190.7 to 89.0 µSv. CONCLUSION: Orthophos provides significantly lower EOBW E than Rayscan, thus EOBW recommendations must be unit specific. For children, Orthophos EOBW could be an alternative to IOBW, for which rectangular collimation is recommended. Thyroid shielding reduced adult Rayscan equivalent dose but added imaging artifacts.

Use of artificial intelligence software in dental education: A study on assisted proximal caries assessment in bitewing radiographs.

INTRODUCTION: Teaching of dental caries diagnostics is an essential part of dental education. Diagnosing proximal caries is a challenging task, and automated systems applying artificial intelligence (AI) have been introduced to assist in this respect. Thus, the implementation of AI for teaching purposes may be considered. The aim of this study was to assess the impact of an AI software on students' ability to detect enamel-only proximal caries in bitewing radiographs (BWs) and to assess whether proximal tooth overlap interferes with caries detection. MATERIALS AND METHODS: The study included 74 dental students randomly allocated to either a test or control group. At two sessions, both groups assessed proximal enamel caries in BWs. At the first session, the test group registered caries in 25 BWs using AI software (AssistDent®) and the control group without using AI. One month later, both groups detected caries in another 25 BWs in a clinical setup without using the software. The student's registrations were compared with a reference standard. Positive agreement (caries) and negative agreement (no caries) were calculated, and t-tests were applied to assess whether the test and control groups performed differently. Moreover, t-tests were applied to test whether proximal overlap interfered with caries registration. RESULTS: At the first and second sessions, 56 and 52 tooth surfaces, respectively, were detected with enamel-only caries according to the reference standard. At session 1, no significant difference between the control (48%) and the test (42%) group was found for positive agreement (p = .08), whereas the negative agreement was higher for the test group (86% vs. 80%; p = .02). At session 2, there was no significant difference between the groups. The test group improved for positive agreement from session 1 to session 2 (p < .001), while the control group improved for negative agreement (p < .001). Thirty-eight per cent of the tooth surfaces overlapped, and the mean positive agreement and negative agreement were significantly lower for overlapping surfaces than non-overlapping surfaces (p < .001) in both groups. CONCLUSION: Training with the AI software did not impact on dental students' ability to detect proximal enamel caries in bitewing radiographs although the positive agreement improved over time. It was revealed that proximal tooth overlap interfered with caries detection.

Performance of the caries diagnosis feature of intraoral scanners and near-infrared imaging technology-A narrative review.

PURPOSE: To describe and discuss the benefits and drawbacks of various dental caries diagnostic techniques, including the use of intraoral scanners for caries diagnosis based on near-infrared imaging (NIR) technology. MATERIAL AND METHODS: A MEDLINE search from 1980-2023 focused on dental caries diagnostic techniques, emphasizing intraoral scanners using NIR technology. Alternative caries detection methods were also evaluated for their advantages and limitations, enabling a comparison with NIR. The review included traditional caries tools, the latest detection methods, and NIR's role in intraoral scanners, drawing from case reports and both in vivo and in vitro studies. Keywords like "caries detection," "intraoral scanners," and "Near Infrared Imaging (NIRI)" guided the search. After screening titles and abstracts for relevance, full texts with valuable insights were thoroughly analyzed. The data was grouped into three: traditional diagnostics, advanced digital methods, and intraoral scanner-based detection. RESULTS: This comprehensive narrative review described and discussed the current state of dental caries diagnostic methods, given the insufficient number of clinical investigations suitable for a systematic review. Traditional caries diagnosis techniques have shown variable accuracy dependent on a dentist's experience and the potential over-removal of healthy tooth structures. Intraoral scanners have emerged as a novel caries detection method, because of their integration of NIR technology. Various studies have confirmed the efficacy of NIR in detecting interproximal caries and in the early diagnosis of non-cavitated caries. Specifically, intraoral scanners have demonstrated promising results, proving comparable to established diagnostic methods like bitewing radiography. Nevertheless, while the integration of NIR into intraoral scanners seems promising, the technology still faces challenges, notably its accuracy in detecting secondary and subgingival cavities. However, with anticipated integrations of AI, NIR in intraoral scanners could revolutionize early caries detection. CONCLUSIONS: Intraoral scanners with NIR technology offer non-destructive imaging, real-time lesion visuals, and enhanced patient communication. Although comparable to bitewing radiography in some studies, a universally accepted diagnostic tool is lacking. Future research should compare them with existing methods, focusing on clinical outcomes, cost-effectiveness, and patient acceptance.

Clinical validation of near-infrared imaging for early detection of proximal caries in primary molars.

OBJECTIVES: The aim of this study was to validate the near-infrared imaging (NIRI) in comparison with visual inspection (VI) for early detection of proximal caries in primary molars. METHODS: VI and intraoral scans were performed on 126 patients aged 3-12 years with at least one non-cavitied and non-restored proximal tooth surface, who were scheduled for bite wing radiography (BWR) as part of their standard care. Teeth with signs of proximal cavities, restorations or residual caries were excluded in this study. BWR, a gold standard to diagnose proximal caries in primary molars, was used to validate the findings of NIRI and VI. The accuracy, sensitivity, specificity and the area under the curve (AUC) of NIRI and VI were calculated. RESULTS: The accuracy, sensitivity and specificity of NIRI were 82.89%, 74.10% and 90.97%, while those of VI were 71.64%, 43.88% and 97.14%, respectively. NIRI showed higher accuracy and sensitivity, and lower specificity (P < 0.001). The AUC of NIRI was higher than that of VI (0.826 vs 0.706; P < 0.05). CONCLUSIONS: NIRI showed higher sensitivity and lower specificity compared with VI when detecting proximal caries in primary molars. Therefore, it is recommended to use NIRI in combination with BWR to improve the detection rate of proximal caries in primary molars. CLINICAL SIGNIFICANCE: In children, there is a high incidence of proximal caries in primary molars, which require high technical sensitivity for detection. NIRI shows high sensitivity in detecting proximal caries, which may improve their detection rate in primary molars. THE CLINICAL TRIAL REGISTRATION NUMBER: ChiCTR2300070916.

Underlying Dentin Shadows (ICDAS 4) in Occlusal Surface of Permanent Teeth Have Low Progression Rate after 1-2 Years.

The aim of this prospective cohort study was to assess the radiographic progression of underlying dentin shadows (UDS) on the occlusal surfaces of permanent posterior teeth of adolescents and young adults over 1-2 years and to identify possible risk factors. A total of 149 UDS lesions (from 101 individuals) were included at baseline. Each participant had to present at least one UDS to be considered eligible for the study. Data collection included the application of a questionnaire, clinical examination, and bilateral bitewing radiographs, performed at baseline and after 1-2 years. The association between possible predictors and UDS progression (defined radiographically as an increase in the radiographic score from baseline to follow-up) was assessed using Weibull regression models. Hazard ratios (HRs) and their 95% confidence intervals (CIs) were estimated. A total of 81 individuals (mean age: 24.0, standard deviation: 8.03) were reexamined after 1-2 years (742 occlusal surfaces, of which 118 were UDS). The overall progression rate was 8.6% after 1-2 years, being 12.6% for UDS without baseline radiolucency and 20% for UDS with baseline radiolucency. The risk analysis showed that UDS without radiolucency at baseline had a similar likelihood of progression (adjusted HR = 1.71, 95% CI = 0.68-4.32, p = 0.26) while UDS with radiolucency at baseline were more likely to progress (adjusted HR = 2.96, 95% CI = 1.06-8.26, p = 0.04) than the reference category (sound occlusal surfaces without radiolucency). These estimates were adjusted for caries prevalence, tooth type, and arch. This study showed low progression rates of UDS after 1-2 years. The presence of radiolucency at baseline was found to predict UDS progression.

Computed vs. film-based radiographs' contour artifacts influence diagnosis of secondary caries.

To test local grey-scale changes on dental bitewing radiographs near filling margins for image acquisition. Forty approximal preparations in caries-free amalgam filled teeth and bitewing radiographs were acquired under standardized conditions applying four techniques. Film-based analog radiographs were digitized using flat-bed scanner (FDR). Phosphor-plate computed radiographs (PCR) were directly acquired by scanning VistaScan imaging plates. Image quality was tested using Preset Filter (PF) or manually applied IntraOral Fine Filter (IF) to enhance digital images. Local changes from digital imaging processing were assessed by comparing the margin-near (MN) and margin-far (MF) zone by a multivariate repeated measurements analysis. All images were acquired with 8-bit depth (256 shades). Dentine was displayed in 79 shades for FDR and 54 shades for PCR. PF or IF locally modify bitewing radiographs by darkening marginal dentine by 8 or 29 shades, respectively. The sharpest display of the margin (shades per pixel) from dentine to filling was found for IF (26.2), followed by FDR (23.2), PF (15.3) and PCR (8.3). Computed radiography with phosphor plates generate more homogeneous images compared to flatbed-digitized film-based radiographs. The filling margin was sharpest represented with the IF filter at the detriment of an artificial darkening of the dentine near the margin of the filling. Contour artifacts by filters have the potential to confound diagnosis of secondary caries. Algorithms and filters for sensor data processing, causing local changes above 2% of the dynamic range by non-continuous mathematical functions, should only be applied with caution, manually when diagnosing and reversibly.

Enhancing Caries Detection in Bitewing Radiographs Using YOLOv7.

The study aimed to evaluate the impact of image size, area of detection (IoU) thresholds and confidence thresholds on the performance of the YOLO models in the detection of dental caries in bitewing radiographs. A total of 2575 bitewing radiographs were annotated with seven classes according to the ICCMS™ radiographic scoring system. YOLOv3 and YOLOv7 models were employed with different configurations, and their performances were evaluated based on precision, recall, F1-score and mean average precision (mAP). Results showed that YOLOv7 with 640 × 640 pixel images exhibited significantly superior performance compared to YOLOv3 in terms of precision (0.557 vs. 0.268), F1-score (0.555 vs. 0.375) and mAP (0.562 vs. 0.458), while the recall was significantly lower (0.552 vs. 0.697). The following experiment found that the overall mAPs did not significantly differ between 640 × 640 pixel and 1280 × 1280 pixel images, for YOLOv7 with an IoU of 50% and a confidence threshold of 0.001 (p = 0.866). The last experiment revealed that the precision significantly increased from 0.570 to 0.593 for YOLOv7 with an IoU of 75% and a confidence threshold of 0.5, but the mean-recall significantly decreased and led to lower mAPs in both IoUs. In conclusion, YOLOv7 outperformed YOLOv3 in caries detection and increasing the image size did not enhance the model's performance. Elevating the IoU from 50% to 75% and confidence threshold from 0.001 to 0.5 led to a reduction of the model's performance, while simultaneously improving precision and reducing recall (minimizing false positives and negatives) for carious lesion detection in bitewing radiographs.

Avaliação da profundidade da cárie proximal nos exames radiográfico convencional e digitalizado / Evaluation of the size of proximal caries in radiography and digitized

Introdução: A cárie dentária é uma doença multifatorial que compreende vários fatores biológicos e sociais. A superfície proximal dos dentes é uma região de difícil visualização que pode esconder pequenas lesões cariosas no esmalte dentário, impossibilitando o diagnóstico através de inspeções visuais e táteis. Objetivo: O objetivo deste trabalho foi avaliar a profundidade da cárie proximal nos exames radiográficos convencionais e digitais, comparando as profundidades das lesões consideradas nestes exames às do exame histológico. Método: Foram utilizados exames radiográficos interproximais de 40 dentes humanos, 20 pré-molares e 20 molares, com alterações clínicas em uma das superfícies proximais, como lesões de mancha branca ou acastanhada e pequenas cavitações. Três profissionais especializados em radiologia odontológica com mais de cinco anos de experiência clínica mediram a profundidade das lesões pelos exames radiográfico e digital das amostras. Para obter os resultados, utilizou-se a técnica de análise de variância (ANOVA). Resultados: Constatou-se um nível de significância de 5% nas mensurações dos exames radiográficos convencionais e digitalizados, mostrando a fidelidade das imagens radiográficas em relação a real profundidade da lesão. Conclusão: Conclui-se que os exames de imagem avaliados foram eficientes na determinação da profundidade das lesões de cárie proximal.

Introduction: Dental caries is a multifactorial disease that comprises several biological and social factors. The proximal surface of the teeth is a region of difficult visualization that can hide small carious lesions in the dental enamel, making diagnosis through visual and tactile inspection infeasible. Objective: The objective of this study was to evaluate the depth of proximal caries in the conventional and digitized radiographic examinations, comparing the depths of the lesions considered in these examinations to those of the histological examination. Method: Interproximal radiographic examinations of 40 human teeth, 20 premolars and 20 molars, with clinical alterations on one of the proximal surfaces, such as white or brown spot lesions and small cavitations, were used. Three professionals specialized in dental radiology with more than five years of clinical experience measured the depth of the lesions by radiographic examination of the samples. To obtain the results, we used the technique of analysis of variance (ANOVA). Results: A level of significance of 5% was found in conventional and digitized radiographic measurements, showing the fidelity of the radiographic images in relation to the actual depth of the lesion. Conclusion: It was concluded that the imaging tests evaluated were efficient in determining the depth of proximal caries lesions.

Gaze patterns of dentists while evaluating bitewing radiographs.

OBJECTIVES: Understanding dentists' gaze patterns on radiographs may allow to unravel sources of their limited accuracy and develop strategies to mitigate them. We conducted an eye tracking experiment to characterize dentists' scanpaths and thus their gaze patterns when assessing bitewing radiographs to detect primary proximal carious lesions. METHODS: 22 dentists assessed a median of nine bitewing images each, resulting in 170 datasets after excluding data with poor quality of gaze recording. Fixation was defined as an area of attentional focus related to visual stimuli. We calculated time to first fixation, fixation count, average fixation duration, and fixation frequency. Analyses were performed for the entire image and stratified by (1) presence of carious lesions and/or restorations and (2) lesion depth (E1/2: outer/inner enamel; D1-3: outer-inner third of dentin). We also examined the transitional nature of the dentists' gaze. RESULTS: Dentists had more fixations on teeth with lesions and/or restorations (median=138 [interquartile range=87, 204]) than teeth without them (32 [15, 66]), p<0.001. Notably, teeth with lesions had longer fixation durations (407 milliseconds [242, 591]) than those with restorations (289 milliseconds [216, 337]), p<0.001. Time to first fixation was longer for teeth with E1 lesions (17,128 milliseconds [8813, 21,540]) than lesions of other depths (p = 0.049). The highest number of fixations were on teeth with D2 lesions (43 [20, 51]) and lowest on teeth with E1 lesions (5 [1, 37]), p<0.001. Generally, a systematic tooth-by-tooth gaze pattern was observed. CONCLUSIONS: As hypothesized, while visually inspecting bitewing radiographic images, dentists employed a heightened focus on certain image features/areas, relevant to the assigned task. Also, they generally examined the entire image in a systematic tooth-by-tooth pattern.

Interactive computer-assisted learning as an educational method for learning pediatric interproximal dental caries identification.

OBJECTIVE: We developed a web-based tool to measure the amount and rate of skill acquisition in pediatric interproximal caries diagnosis among pre- and postdoctoral dental students and identified variables predictive for greater image interpretation difficulty. STUDY DESIGN: In this multicenter prospective cohort study, a convenience sample of pre- and postdoctoral dental students participated in computer-assisted learning in the interpretation of bitewing radiographs of 193 children. Participants were asked to identify the presence or absence of interproximal caries and, where applicable, locate the lesions. After every case, participants received specific visual and text feedback on their diagnostic performance. They were requested to complete the 193-case set but could complete enough cases to achieve a competency performance standard of 75% accuracy, sensitivity, and specificity. RESULTS: Of 130 participants, 62 (47.7%) completed all cases. The mean change from initial to maximal diagnostic accuracy was +15.3% (95% CI, 13.0-17.7), sensitivity was +10.8% (95% CI, 9.0-12.7), and specificity was +15.5% (95% CI, 12.9-18.1). The median number of cases completed to achieve competency was 173 (interquartile range, 82-363). Of these 62 participants, 45 (72.6%) showed overall improvement in diagnostic accuracy. Greater numbers of interproximal lesions (P < .001) and the presence of noninterproximal caries (P < .001) predicted greater interpretation difficulty. CONCLUSIONS: Computer-assisted learning led to improved diagnosis of interproximal caries on bitewing radiographs among pre- and postdoctoral dental students.

Comparison of accuracy cone-beam computed tomography and digital bitewing radiography for detection of recurrent caries under various restorative materials: in vitro study.

OBJECTIVE: The aim of this study was to compare and evaluate diagnostic accuracy of two different CBCT scan modes and digital bitewing radiography for detection of recurrent caries under five different restorative materials, and determine the relationship between the types of restorative materials. MATERIALS AND METHODS: In this in vitro study, 200 caries-free upper and lower premolars and molars were selected. A standard deep Class II cavities was created in the middle of the mesial surface of all teeth. In 100 teeth of the experimental and control groups, secondary caries was artificially demineralized. All teeth were filled with five types of restorative material including two types of conventional composite resins, flow composite resin, glass ionomer and amalgam. The teeth were imaged with high resolution (HIRes) and standard CBCT scan modes and digital bitewing. The AUC, sensitivity, specificity and areas under the ROC curve were calculated and verified through SPSS. RESULTS: CBCT technique was the best option in diagnosing recurrent caries. The diagnostic accuracy and specificity of HIRes CBCT scan mode was significantly higher than standard mode (P = 0.031) and bitewing (P = 0.029) for detection of recurrent caries, especially under composite group. There were no significant differences in accuracy value of bitewing and standard CBCT scan mode. CONCLUSION: CBCT showed higher accuracy and specificity on the detection of recurrent caries which was more accurate than bitewing radiography. The HIRes CBCT scan mode achieved the highest accuracy and performed the best in recurrent caries detection.

Chapter 6: Diagnostic Considerations regarding Coronal Caries.

This chapter considers the main principles guiding diagnosis of the disease dental caries in clinical practice by means of clinical examination and radiographs as adjunct method. Dental professionals have been trained to diagnose caries disease by assessing clinical symptoms and signs of caries lesions complemented by radiographic examination as an adjunct method. Clinical examination is the foundation of the diagnosis and should be performed after removal of dental biofilm of tooth surfaces, air-drying, and under good illumination. Clinical diagnostic methods categorize caries lesions according to their severity and in some methods according to their activity. Caries lesion activity has been determined by surface reflection and texture. The detection of thick or heavy biofilm on tooth surfaces is an additional diagnostic clinical tool to estimate caries lesion activity. Patients with no caries experience, that is, without clinical and/or radiographic signs of caries lesions in the dentition, are considered caries inactive. Other caries-inactive patients may present inactive caries lesions/restorations in their dentition. In contrast, patients are considered caries active when presenting any active caries lesion at clinical level and/or any progressing lesion as demonstrated by at least two bitewing radiographs taken at different points in time. The main concern about caries-active patients is that caries lesions are likely to progress unless effective measures are implemented to interfere with its progression. Prescribed according to individual needs, bitewing radiographs provide additional information for clinical examination in the detection of approximal enamel and outer third dentine lesions that can be inactivated by nonoperative treatment.

Chapter 7: Technological Aids and Coronal Caries.

In recent decades, dentistry has developed significantly in all areas. While in the past, caries was mainly treated operatively, the today's management has shifted toward noninvasive, minimal invasive, and, only if needed, invasive treatment options. Aiming at enabling the most noninvasive or conservative treatment option requires early caries detection, which, however, remains challenging. The progression of early or noncavitated caries lesions can nowadays be successfully controlled, as well as lesions arrested by oral hygiene procedures combined with the use of fluorides, sealants, or resin infiltration. Methods such as near-infrared light transillumination, fibre-optic transillumination, digital fibre-optic transillumination, laser fluorescence, or quantitative light fluorescence measurements were introduced in the dental market to provide X-ray-free caries detection, assessment, and monitoring. For approximal surfaces that are not directly visible, bitewing radiography is still the standard in detecting caries lesions. The use of artificial intelligence has become the most recent technological aid for the detection of caries lesions on bitewing radiographs and clinical images and has to be understood as an emerging technology, which requires extensive research in the future. The aim of this chapter is to give an overview of different possibilities to detect coronal caries lesions and suggestions of how to improve this process.