Classification of Periapical and Bitewing Radiographs as Periodontally Healthy or Diseased by Deep Learning Algorithms.

Objectives The aim of this artificial intelligence (AI) study was to develop a deep learning algorithm capable of automatically classifying periapical and bitewing radiography images as either periodontally healthy or unhealthy and to assess the algorithm's diagnostic success. Materials and methods The sample of the study consisted of 1120 periapical radiographs (560 periodontally healthy, 560 periodontally unhealthy) and 1498 bitewing radiographs (749 periodontally healthy, 749 periodontally ill). From the main datasets of both radiography types, three sub-datasets were randomly created: a training set (80%), a validation set (10%), and a test set (10%). Using these sub-datasets, a deep learning algorithm was developed with the YOLOv8-cls model (Ultralytics, Los Angeles, California, United States) and trained over 300 epochs. The success of the developed algorithm was evaluated using the confusion matrix method. Results The AI algorithm achieved classification accuracies of 75% or higher for both radiograph types. For bitewing radiographs, the sensitivity, specificity, precision, accuracy, and F1 score values were 0.8243, 0.7162, 0.7439, 0.7703, and 0.7821, respectively. For periapical radiographs, the sensitivity, specificity, precision, accuracy, and F1 score were 0.7500, 0.7500, 0.7500, 0.7500, and 0.7500, respectively. Conclusion The AI models developed in this study demonstrated considerable success in classifying periodontal disease. Future applications may involve employing AI algorithms for assessing periodontal status across various types of radiography images and for automated disease detection.

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.

Progression of proximal caries in primary molars using the radiographic ICDAS: a retrospective cohort study.

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.

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

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

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

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

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.

Dental Caries diagnosis from bitewing images using convolutional neural networks.

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

A novel deep learning-based perspective for tooth numbering and caries detection.

OBJECTIVES: The aim of this study was automatically detecting and numbering teeth in digital bitewing radiographs obtained from patients, and evaluating the diagnostic efficiency of decayed teeth in real time, using deep learning algorithms. METHODS: The dataset consisted of 1170 anonymized digital bitewing radiographs randomly obtained from faculty archives. After image evaluation and labeling process, the dataset was split into training and test datasets. This study proposed an end-to-end pipeline architecture consisting of three stages for matching tooth numbers and caries lesions to enhance treatment outcomes and prevent potential issues. Initially, a pre-trained convolutional neural network (CNN) utilized to determine the side of the bitewing images. Then, an improved CNN model YOLOv7 was proposed for tooth numbering and caries detection. In the final stage, our developed algorithm assessed which teeth have caries by comparing the numbered teeth with the detected caries, using the intersection over union value for the matching process. RESULTS: According to test results, the recall, precision, and F1-score values were 0.994, 0.987 and 0.99 for teeth detection, 0.974, 0.985 and 0.979 for teeth numbering, and 0.833, 0.866 and 0.822 for caries detection, respectively. For teeth numbering and caries detection matching performance; the accuracy, recall, specificity, precision and F1-Score values were 0.934, 0.834, 0.961, 0.851 and 0.842, respectively. CONCLUSIONS: The proposed model exhibited good achievement, highlighting the potential use of CNNs for tooth detection, numbering, and caries detection, concurrently. CLINICAL SIGNIFICANCE: CNNs can provide valuable support to clinicians by automating the detection and numbering of teeth, as well as the detection of caries on bitewing radiographs. By enhancing overall performance, these algorithms have the capacity to efficiently save time and play a significant role in the assessment process.

Perception of Pain With Bitewing, DIAGNOcam, and Teeth Separation Amongst Children.

BACKGROUND: This study evaluated the pain and discomfort associated with 3 diagnostic techniques for proximal carious lesions in children aged 5 to 8 years: bitewing (BW) radiographs, DIAGNOcam, and temporary teeth separation. METHODS: The study included 60 healthy children between the ages of 5 and 8 years who had no prior history of dry mouth or mouth breathing, were definitely positive or positive based on Frankl Behavioral Rating Scale, had at least one pair of matched bilateral primary molars and/or permanent first molars in close contact with the adjacent tooth, and were free of restorations and frank cavitation. Each patient evaluated all 3 techniques. The pain and discomfort ratings were obtained by the Wong-Baker FACES Pain Rating Scale immediately after taking 2 standardised BW radiographs or undergoing use of DIAGNOcam and 2 days after temporary teeth separation with elastic separators by a single trained and experienced paediatric dentist. RESULTS: The DIAGNOcam procedure resulted in much higher pain and discomfort (3.69 ± 3.10) than the other 2 diagnostic techniques. Within-participant pain and discomfort scored significantly higher with DIAGNOcam compared to BW radiographs (P < .001) and temporary teeth separation (P = .002). CONCLUSIONS: The DIAGNOcam diagnostic technique caused much more pain and discomfort than BW radiographs and temporary teeth separation using orthodontic elastic separators. The report is part of a randomised clinical trial that was registered at www. CLINICALTRIALS: gov under the identifier NCT03685058.

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.

Dental student application of artificial intelligence technology in detecting proximal caries lesions.

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.

Dental bitewing radiographs segmentation using deep learning-based convolutional neural network algorithms.

OBJECTIVES: Dental radiographs, particularly bitewing radiographs, are widely used in dental diagnosis and treatment Dental image segmentation is difficult for various reasons, such as intricate structures, low contrast, noise, roughness, and unclear borders, resulting in poor image quality. Recent developments in deep learning models have improved performance in analyzing dental images. In this research, our primary objective is to determine the most effective segmentation technique for bitewing radiographs based on different metrics: accuracy, training time, and the number of training parameters as a reflection of architectural cost. METHODS: In this research, we employ several deep learning models, namely Resnet-18, Resnet-50, Xception, Inception Resnet v2, and Mobilenetv2, to segment bitewing radiographs. The process begins by importing the radiographs into MATLAB®(MathWorks Inc), where the images are first improved, then segmented using the graph cut method based on regions to produce a binary mask that distinguishes the background from the original X-ray. RESULTS: The deep learning models were trained on 298 and 99 radiograph training and validation sets and were evaluated using 99 images from the testing set. We also compare the segmentation model using several criteria, including accuracy, speed, and size, to determine which network is superior. Furthermore, we compare our findings with prior research to provide a comprehensive understanding of the advancements made in dental image segmentation. The accurate segmentation achieved was 93.67% and 94.42% by the Resnet-18 and Resnet-50 models, respectively. CONCLUSION: This research advances dental image analysis and facilitates more accurate diagnoses and treatment planning by determining the best segmentation technique. The outcomes of this study can guide researchers and practitioners in selecting appropriate segmentation methods for practical dental image analysis.

Automatic caries detection in bitewing radiographs: part I-deep learning.

OBJECTIVE: The aim of this work was to assemble a large annotated dataset of bitewing radiographs and to use convolutional neural networks to automate the detection of dental caries in bitewing radiographs with human-level performance. MATERIALS AND METHODS: A dataset of 3989 bitewing radiographs was created, and 7257 carious lesions were annotated using minimal bounding boxes. The dataset was then divided into 3 parts for the training (70%), validation (15%), and testing (15%) of multiple object detection convolutional neural networks (CNN). The tested CNN architectures included YOLOv5, Faster R-CNN, RetinaNet, and EfficientDet. To further improve the detection performance, model ensembling was used, and nested predictions were removed during post-processing. The models were compared in terms of the [Formula: see text] score and average precision (AP) with various thresholds of the intersection over union (IoU). RESULTS: The twelve tested architectures had [Formula: see text] scores of 0.72-0.76. Their performance was improved by ensembling which increased the [Formula: see text] score to 0.79-0.80. The best-performing ensemble detected caries with the precision of 0.83, recall of 0.77, [Formula: see text], and AP of 0.86 at IoU=0.5. Small carious lesions were predicted with slightly lower accuracy (AP 0.82) than medium or large lesions (AP 0.88). CONCLUSIONS: The trained ensemble of object detection CNNs detected caries with satisfactory accuracy and performed at least as well as experienced dentists (see companion paper, Part II). The performance on small lesions was likely limited by inconsistencies in the training dataset. CLINICAL SIGNIFICANCE: Caries can be automatically detected using convolutional neural networks. However, detecting incipient carious lesions remains challenging.

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.

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.

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.

Comparison of diagnostic effects of infrared imaging, visual inspection, and bitewing radiography in primary occlusal caries of permanent teeth.

Background: This study compared the diagnostic efficacy of VistaCam iX infrared camera, visual inspection, and bitewing-radiographs for the detection of primary occlusal caries of permanent teeth. Materials and Methods: In this in vitro experimental study, 80 extracted human premolars were evaluated. The occlusal surfaces of these teeth were demineralized by immersion in a demineralizing agent. Then, the International Caries Detection and Assessment System (ICDAS II), bitewing-radiography, and Proxi head of VistaCam iX were used to inspect them. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for each diagnostic modality. Data were analyzed using SPSS. Twenty-five at P < 0.05 level of significance with one-way analysis of variance and Games-Howell test. Results: Bitewing-radiography had significantly lower sensitivity than ICDAS II and VistaCam (P < 0.05). ICDAS II was comparable to VistaCam, with no significant difference in sensitivity (P > 0.05). ICDAS II had a significantly higher PPV than bitewing-radiography and VistaCam (P < 0.05). The sensitivity of bitewing radiography was significantly lower than that of ICDAS II and VistaCam (P < 0.05). ICDAS II was comparable to that of VistaCam with no significant differences in sensitivity (P > 0.05). ICDAS II had a considerably higher PPV than bitewing-radiography and VistaCam (P < 0.05). The NPV of ICDAS II visual inspection was significantly higher than that of bitewing-radiography and VistaCam (P < 0.05). The ICDASS II and VistaCam had a repeatability coefficient of 47.4%. For bitewing-radiography and VistaCam, this value was 44.2% and 83.4% for ICDAS II and bitewing-radiography. Conclusion: Visual inspection seems to be superior to bitewing-radiography and VistaCam in detecting primary occlusal caries of permanent teeth.

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.

Radiation Exposure and Frequency of Dental, Bitewing and Occlusal Radiographs in Children and Adolescents.

Dental radiographs are valuable diagnostic aids for oral healthcare, but exposure to ionizing radiation carries health risks, especially in children due to their high radio-sensitivity. Valid reference values for intraoral radiographs in children and adolescents are still missing. This study aimed to investigate the radiation dose values and underlying justifications of dental, bitewing and occlusal X-rays in children and adolescents. Data from routinely executed intraoral radiographs between 2002 and 2020 with conventional and digital tube-heads were extracted from the Radiology Information System. The effective exposure was calculated from technical parameters and statistical tests performed. A total number of 4455 intraoral (3128 dental, 903 bitewing and 424 occlusal) radiographs were investigated. For dental and bitewing radiographs, the dose area product (DAP) was 2.57 cGy × cm2 and the effective dose (ED) 0.77 µSv. For occlusal radiographs, the DAP was 7.43 cGy × cm2 and the ED 2.22 µSv. Overall, 70.2% of all intraoral radiographs were dental, 20.3% bitewing and 9.5% occlusal radiographs. The most frequent indication for intraoral radiographs was trauma (28.7%), followed by caries (22.7%) and apical diagnostics (22.7%). Moreover, 59.7% of all intraoral radiographs were taken in boys, especially for trauma (66.5%) and endodontics (67.2%) (p ≤ 0.00). Girls were significantly more frequently X-rayed for caries diagnostics than boys (28.1% vs. 19.1%, p ≤ 0.00). The average ED of 0.77 µSv for intraoral dental and bitewing radiographs in this study was within the range of other reported values. The technical parameters of the X-ray devices were found at the lowest recommended levels to best limit the radiation exposure and to assure acceptable diagnostic efficacy. Intraoral radiographs were performed predominantly for trauma, caries and apical diagnostics-reflecting general recommendations for the use of X-rays in children. For improved quality assurance and radiation protection, further studies are necessary to determine the meaningful dose reference level (DRL) for children.