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.

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.

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.

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.

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.

Feasibility of deep learning for dental caries classification in bitewing radiographs based on the ICCMS™ radiographic scoring system.

OBJECTIVE: To evaluate the potential of deep learning models for categorization of dental caries in bitewing radiographs based on the International Caries Classification and Management System (ICCMS™) radiographic scoring system (RSS). STUDY DESIGN: In total, 2758 annotated bitewing radiographs were randomly divided into 3 experiments to assess the ResNet-18, -50, -101, and -152. Experiment A tested 4-class ICCMS™-RSS training and validation using Carestream (CS) radiographs; experiment B tested training and validation using CS and VistaScan radiographs; experiment C tested 7-class ICCMS™-RSS training and validation using CS and VistaScan radiographs. The performance matrices and the areas under the receiver operating characteristic curves were analyzed to assess all procedures. RESULTS: In experiment A, ResNet-50 and ResNet-152 were equally accurate (71.11%) and approximately 78% sensitive. The latter presented the highest specificity (56.90%). In experiment B, ResNet-50 presented the highest sensitivity (79.51%) but ResNet-152 had the highest specificity (60.71%). In experiment C, all models markedly underperformed in distinguishing the 7-class ICCMS™-RSS with specificities of 16.46% to 22.41%. They had fewer classification errors in the 4-class classification (28.89%-35.56%) than in the 7-class classification (42.34%-53.06%). The areas under the receiver operating characteristic curves of all models were unanimously comparable. CONCLUSIONS: The ResNet models were able to classify dental caries according to the ICCMS™-RSS with average performances. The models underperformed in complicated classification tasks.

Assessment of YOLOv3 for caries detection in bitewing radiographs based on the ICCMS™ radiographic scoring system.

OBJECTIVES: To assess the feasibility of the YOLOv3 model under the intersection over union (IoU) thresholds of 0.5 (IoU50) and 0.75 (IoU75) for caries detection in bitewing radiographs based on the International Caries Classification and Management System (ICCMS™). MATERIALS AND METHODS: We trained the YOLOv3 model by feeding 994 annotated radiographs with the IoU50 and IoU75 thresholds. The testing procedure (n = 175) was subsequently conducted to evaluate the model's prediction metrics on caries classification based on the ICCMS™ radiographic scoring system. RESULTS: Regarding the 4-class classification representing caries severity, YOLOv3 could accurately detect and classify enamel caries and initial dentin caries (class RA) (IoU50 vs IoU75: precision, 0.75 vs 0.71; recall, 0.67 vs 0.64). Concerning the 7-class classification signifying specific caries depth (class 0, healthy tooth; classes RA1-3, initial caries affecting outer half, inner half of enamel, and the outer 1/3 of dentin; class RB4, caries extending to the middle 1/3 of dentin; classes RC5-6, extensively cavitated caries affecting the inner 1/3 of dentin and involving the pulp chamber), YOLOv3 could accurately detect and classify caries with pulpal exposure (class RC6) (IoU50 vs IoU75: precision, 0.77 vs 0.73; recall, 0.61 vs 0.57) but it failed to predict the outer half of enamel caries (class RA1) (IoU50 vs IoU75: precision, 0.35 vs 0.32; recall, 0.23 vs 0.21). CONCLUSIONS: YOLOv3 yielded acceptable performances in both IoU50 and IoU75. Although the performance metrics decreased in the 7-class detection, the two thresholds revealed comparable results. However, the model could not consistently detect initial-stage caries affecting the outermost surface of the enamel. CLINICAL RELEVANCE: YOLOv3 could be implemented to detect and classify dental caries according to the ICCMS™ classification with acceptable performances to assist dentists in making treatment decisions.

Diagnosis of Interproximal Caries Lesions in Bitewing Radiographs Using a Deep Convolutional Neural Network-Based Software.

The aim of this study was to evaluate the diagnostic reliability of a web-based artificial intelligence program for the detection of interproximal caries in bitewing radiographs. Three hundred bitewing radiographs of patients were subjected to the evaluation of a convolutional neural network. First, the images were visually evaluated by a previously trained and calibrated operator with radiodiagnosis experience. Then, ground truth was established and was clinically validated. For enamel caries, clinical assessment included a combination of clinical-visual and radiography evaluations. For dentin caries, clinical validation was performed by instrumentally accessing the cavity. Second, the images were uploaded and analyzed by the web-based software. Four different models were established to analyze its evaluations according to the confidence threshold (0-100%) offered by the program: model 1 (values >0% were considered positive and values of 0% were considered negative), model 2 (values ≥25% were considered positive and values <25% were considered negative), model 3 (values ≥50% were considered positive and values <50% were considered negative), and model 4 (values ≥75% were considered positive and values <75% were considered negative). The accuracy rate (A), sensitivity (S), specificity (E), positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR), and areas under receiver operating characteristic curves (AUC) were calculated for the four models of agreement with the software. Models showed the following results respectively: A = 70.8%, 82%, 85.6%, 86.1%; S = 87%, 69.8%, 57%, 41.6%; E = 66.3%, 85.4%, 93.7%, 98.5%; PPV = 42%, 57.2%, 71.6%, 88.6%; NPV = 94.8%, 91%, 88.6%, 85.8%; PLR = 2.58, 4.78, 9.05, 27.73; NLR = 0.2, 0.35, 0.46, 0.59; AUC = 0.767, 0.777, 0.753, 0.701. Findings in the present study suggest that the artificial intelligence web-based software provides a good diagnostic reliability on the detection of dental caries. Our study highlighted model 2 for showing the best results to differentiate between healthy teeth and decayed teeth.

Detection of Proximal Caries Lesions on Bitewing Radiographs Using Deep Learning Method.

This study aimed to evaluate the validity of a deep learning-based convolutional neural network (CNN) for detecting proximal caries lesions on bitewing radiographs. A total of 978 bitewing radiographs, 10,899 proximal surfaces, were evaluated by two endodontists and a radiologist, of which 2,719 surfaces were diagnosed and annotated with proximal caries and 8,180 surfaces were sound. The data were randomly divided into two datasets, with 818 bitewings in the training and validation dataset and 160 bitewings in the test dataset. Each annotation in the test set was then classified into 5 stages according to the extent of the lesion (E1, E2, D1, D2, D3). Faster R-CNN, a deep learning-based object detection method, was trained to detect proximal caries in the training and validation dataset and then was assessed on the test dataset. The diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and receiver operating characteristic curve were calculated. The performance of the network in the overall and different stages of lesions was compared with that of postgraduate students on the test dataset. A total of 388 carious lesions and 1,435 sound surfaces were correctly identified by the neural network; hence, the accuracy was 0.87. Furthermore, 27.6% of lesions went undetected, and 7% of sound surfaces were misdiagnosed by the neural network. The sensitivity, specificity, PPV, and NPV of the neural network were 0.72, 0.93, 0.77, and 0.91, respectively. In contrast with the network, 52.8% of lesions went undetected by the students, yielding a sensitivity of only 0.47. The F1-score of the students was 0.57, while the F1-score of the network was 0.74 despite the accuracy of 0.82. A significant difference in the sensitivity was found between the model and the postgraduate students when detecting different stages of lesions (p < 0.05). For early lesions which limited in enamel and the outer third of dentin, the neural network had sensitivities all above or at 0.65, while students showed sensitivities below 0.40. From our results, we conclude that the CNN may be an assistant in detecting proximal caries on bitewings.

New Caries Diagnostic Tools in Intraoral Scanners: A Comparative In Vitro Study to Established Methods in Permanent and Primary Teeth.

The aim of this in vitro study was to systematically investigate new caries diagnostic tools, including three intraoral scanners, and compare them to established diagnostic methods. For a standardized analysis of occlusal and proximal caries lesions, human permanent and primary teeth (n = 64) were embedded in models and investigated in a phantom head using six different caries diagnostic methods: visual examination, bitewing radiography, Diagnocam (KaVo, Biberach, Germany), Trios 4 (3Shape, Copenhagen, Denmark), iTero Element 5D (Align Technology, San José, CA, USA), and Planmeca Emerald S (Planmeca, Helsinki, Finland). The diagnostic methods were investigated and compared to reference µ-CT for permanent and primary teeth separately. For occlusal caries diagnostics in permanent teeth, the best agreement to the reference (reliability) was obtained for Planmeca Emerald S (ĸ = 0.700), whereas in primary teeth, for visual examination (ĸ = 0.927), followed by Trios 4 (ĸ = 0.579). Regarding proximal caries diagnostics, bitewing radiography, as the gold standard, exhibited the highest agreement for permanent (ĸ = 0.643) and primary teeth (ĸ = 0.871). Concerning the analysis of the diagnostic quality (sensitivity and specificity) using receiver operating characteristic (ROC) curve analysis, comparable findings were obtained for area under curve (AUC) values as for reliability. No diagnostic method could be identified that is generally suitable for occlusal and proximal lesions in both dentitions. Overall, caries diagnostics with intraoral scanners seem to be interesting tools that should be further investigated in clinical studies.

Validity of near-infrared light transillumination for the assessment of proximal caries in permanent teeth.

PURPOSE: The aim of this study was to evaluate the performance of DIAGNOcam (DC) in diagnosing proximal caries and to compare its effectiveness with the International Caries Detection and Assessment System (ICDAS) and bitewing radiography (BWR). METHODS: 118 premolars extracted for orthodontic reasons were included and examined using three detection methods and validated by histological sections as the gold standard. The sensitivity, specificity and areas under the ROC curve (Az value) at the outer half enamel (D1), inner half enamel (D2) and dentine (D3) thresholds were compared between different methods. RESULTS: At all categories, the specificity of DC was almost as high as ICDAS and BWR. DC showed a significantly higher sensitivity (0.68) than both visual (0.33) and radiographic examination (0.47) at the D1 threshold. DC presented the highest Az value (area under the ROC curve) at the D1 and D2 threshold (0.81, 0.86), while BWR showed the greatest Az values at D3 (0.94). Furthermore, DC had the highest association strength with the gold standard (Spearman's ρ = 0.80). CONCLUSIONS: It can be concluded that DC could detect proximal caries effectively and showed comparable or even better performance than ICDAS and BWR.

Reflected near-infrared light versus bite-wing radiography for the detection of proximal caries: A multicenter prospective clinical study conducted in private practices.

OBJECTIVES: The aim of the present prospective multicenter clinical study was to compare the detection of proximal caries with near-infrared light reflection (NILR) versus bitewing radiography (BWR). MATERIALS AND METHODS: Intraoral scans were performed on 100 patients in five dental clinics using an intraoral scanner (iTero Element 5D, Align Technology, Tempe, AZ, USA) that includes a near-infrared light source (850 nm) and sensor. Reflected near-infrared light images of posterior teeth were used by the individual dentists to detect proximal caries and the results were compared to the BWRs. In a total of 3499 proximal surfaces of molars and premolars which were examined, 223 carious lesions were detected by BWR, while NILR detected 549 carious lesions. Caries detection using both methods was also done by an expert team of five dentists, highly experienced in NILR image interpretation, who used the same sets of clinically-obtained data. Sensitivity, specificity, and accuracy were calculated for caries detection by both the dentists and the expert team. Fifty-nine of the detected carious lesions were clinically treated and the observations during caries excavation were compared with those done with NILR and BWR. Statistical analysis to compare between NILR and BWR diagnosis was performed using non-parametric two-sided McNemar's Chi-Square test with the significance level set at p < 0.05. Kappa coefficients were calculated to assess the level of agreement between the two caries detection methods. RESULTS: Accuracy of NILR detection of early enamel lesions was 88% and that of carious lesions involving the dentino-enamel junction (DEJ) was 97%. Accuracy was found to be higher at 96% and 99%, respectively, when the same data were examined by the expert team. Direct observation during caries-excavation treatment suggested that NILR detected early enamel lesions that were not detectable with BWR alone. CONCLUSIONS: Within the limitations of the present study, NILR was more sensitive than BWR in detecting early enamel lesions and comparable to BWR in detecting lesions that involved the DEJ. CLINICAL RELEVANCE: Reflected near-infrared light images that are generated simultaneously with 3D intra-oral scanning may be used reliably for detection, screening, and monitoring of proximal caries, thus potentially minimizing the traditional use of ionizing radiation.

Deep-learning approach for caries detection and segmentation on dental bitewing radiographs.

OBJECTIVES: The aim of this study is to recommend an automatic caries detection and segmentation model based on the Convolutional Neural Network (CNN) algorithms in dental bitewing radiographs using VGG-16 and U-Net architecture and evaluate the clinical performance of the model comparing to human observer. METHODS: A total of 621 anonymized bitewing radiographs were used to progress the Artificial Intelligence (AI) system (CranioCatch, Eskisehir, Turkey) for the detection and segmentation of caries lesions. The radiographs were obtained from the Radiology Archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry of Ordu University. VGG-16 and U-Net implemented with PyTorch models were used for the detection and segmentation of caries lesions, respectively. RESULTS: The sensitivity, precision, and F-measure rates for caries detection and caries segmentation were 0.84, 0.81; 0.84, 0.86; and 0.84, 0.84, respectively. Comparing to 5 different experienced observers and AI models on external radiographic dataset, AI models showed superiority to assistant specialists. CONCLUSION: CNN-based AI algorithms can have the potential to detect and segmentation of dental caries accurately and effectively in bitewing radiographs. AI algorithms based on the deep-learning method have the potential to assist clinicians in routine clinical practice for quickly and reliably detecting the tooth caries. The use of these algorithms in clinical practice can provide to important benefit to physicians as a clinical decision support system in dentistry.

Accuracy of digital bitewing radiographs with and without applying horizontal tube shift in the diagnosis of residual excess cement around dental implants: an in vitro study / Precisión de las radiografías digitales bitewing con y sin aplicación de desplazamiento horizontal del tubo en el diagnóstico del exceso de cemento residual alrededor de los implantes dentales: un estudio in vitro

Purpose: This study was designed to evaluate the diagnostic value of digital Bitewing (BW) radiographs with and without horizontal tube shift in detecting Residual excess cement (REC) on the proximal and non-proximal surfaces of implant restorations. Material and Methods: Eight mandibular models were fabricated with two implants placed on each side in the premolar and first molar positions. Excess cement was applied to either proximal or non-proximal surfaces of the restorations intentionally during the process of crown cementation. BW radiographs with and without applying horizontal tube shift were acquired. Three maxillofacial radiologists were asked to determine the presence and location of REC in the radiographs. Sensitivity and specificity of the radiographic technique were assessed according to the restoration surface that contained REC. Results: Sensitivity of BW radiographs was 100% for the detection of REC on the proximal surfaces and 41-18, 80% on the non-proximal surfaces. Specificity of the technique was 85.71%-100% for the proximal surfaces and 75-94. 12% for the non-proximal areas. Specificity of the radiographic method was generally greater than its sensitivity for the non-proximal surfaces while in the proximal areas, the two variables had quite similar values. Conclusion: Digital BW radiography is generally more useful for detection of REC on the proximal surfaces. Higher specificity of this technique for the bucco-lingual surfaces suggests more reliability of the negative diagnoses in the non-proximal areas.

Objetivo: Evaluar el valor diagnóstico de las radiografías digitales bitewing (BW), con y sin desplazamiento horizontal del tubo, para detectar el exceso de cemento residual (ECR) en las superficies proximales y no proximales de las restauraciones con implantes. Material y Métodos: Se fabricaron ocho modelos mandibulares con dos implantes colocados a cada lado en las posiciones premolar y primer molar. El exceso de cemento se aplicó intencionalmente en las superficies proximales o no proximales de las restauraciones durante el proceso de cementación de la corona. Se adquirieron radiografías BW con y sin aplicación de desplazamiento horizontal del tubo. Se pidió a tres radiólogos maxilofaciales que determinaran la presencia y ubicación de ECR en las radiografías. La sensibilidad y la especificidad de la técnica radiográfica se evaluaron según la superficie de restauración que contenía ECR. Resultados: La sensibilidad de las radiografías de BW fue del 100% para la detección de ECR en las superficies proximales y del 41,18-80% en las superficies no proximales. La especificidad de la técnica fue 85-71, 100% para las superficies proximales y 75-94, 12% para las áreas no proximales. La especificidad del método radiográfico fue generalmente mayor que su sensibilidad para las superficies no proximales, mientras que en las áreas proximales, las dos variables tuvieron valores bastante similares. Conclusión: La radiografía digital BW es generalmente más útil para la detección de ECR en las superficies proximales. La mayor especificidad de esta técnica para las superficies buco-linguales sugiere una mayor confiabilidad de los diagnósticos negativos en las áreas no proximales.

Application of Near-infrared Light Transillumination in Restorative Dentistry: A Review.

This paper presents the various applications of near-infrared light transillumination (NILT) in dentistry. Untreated dental caries is considered the most prevalent health condition affecting both children and adults worldwide. Increased awareness and a paradigm shift toward utilization of minimally invasive treatment procedures and nonionizing radiation led to the discovery of newer techniques for screening and early diagnosis of demineralized lesions. Demineralized lesions detected early can be treated with minimally invasive treatment procedures such as the usage of fluoridated dentifrice to encourage remineralization and resin infiltration to arrest caries progression. NILT procedure involves noninvasive, nonionizing radiation and helps in the identification of early demineralized lesions using light transillumination. At near-infrared wavelengths, the enamel appears translucent and helps in visualizing and detecting demineralized lesions when long-wave light transilluminated against the tooth surface. The wavelength in the range of 1310 nm is considered best for the transillumination of lesions. This technique has been proven to be successful in the detection of carious and demineralized lesions. NILT can be used as a screening tool for the early detection of demineralized lesions and can be considered as an adjunct to bitewing radiographs. It can be advantageous in screening pregnant, growing adolescent patients and in cases where multiple follow-ups are needed and ionizing radiation must be avoided. Keywords: Dental caries, Early diagnosis, Ionizing radiation, Minimally invasive, Near-infrared, Occlusal caries, Transillumination.

Simulation of three intraoral radiographic techniques in pediatric dental patients: subjective comfort assessment using the VAS and Wong-Baker FACES Pain Raiting Scale.

BACKGROUND: Perception of pain associated with intraoral radiography in pediatric patients was evaluated through statistical comparisons of data obtained using the Wong-Baker FACES Pain Raiting Scale (WBFPRS) and visual analog scale (VAS) scoring. METHODS: A total of 75 pediatric patients aged 6-12 years were included in this study. Simulations of each of three radiological methods (analog films, CCD sensor and phosphorus plates) were performed on 25 pediatric patients. Following the simulations, the meaning of each facial expression on the WBFPRS and the numbers on the VAS were explained to each child. For the comparison between groups, the homogeneity of the variances was tested with Levene's test; because the variances were not homogeneous, Welch's test was used. Tamhane's T2 test was used because the homogeneity assumption was not provided to determine the source of the difference between the groups. RESULTS: When the conventional method was compared to the PSPL (photostimulable phosphor luminescence) method, no significant differences were noted in either the WBFPRS or VAS results (p >0.05). The results obtained from both of the scales were significantly different between the conventional method and the CCD sensor method (p < 0.05). When the PSPL and CCD sensors were compared, a significant difference was observed for the WBFPRS (p < 0.05). It was found the highest level of pain scores when used the CCD sensor method than the analog film and PSPL methods (p < 0.05). CONCLUSIONS: It is expected that digital radiographic techniques will be improved in the future and that their disadvantages will be eliminated, resulting in imaging devices that are more comfortable for pediatric patients.

Accuracy of the DIAGNOcam and bitewing radiographs in the diagnosis of cavitated proximal carious lesions in primary molars.

AIMS: To evaluate the diagnostic abilities of near-infrared light transillumination (using the DIAGNOcam) and bitewing radiographs in detecting cavitated proximal carious lesions in primary molars. SUBJECTS AND METHODS: The study was a cross-sectional analytical, clinical study. The proximal surfaces of primary molars of healthy 5- to 8-year-old children were radiographically screened for the presence of carious lesions in the enamel or outer third of dentin (D1). Two trained and calibrated examiners evaluated the depth of caries in bitewing radiographs and DIAGNOcam images and then verified the presence of cavitation by direct visual examination using the "International Caries Detection and Assessment System" after temporary tooth separation. RESULTS: A total of 236 proximal lesions were included in the study. Most of the clinically cavitated lesions (51.9%) were D1 radiographically and in outer dentin lesions (scores 3 and 4) by the DIAGNOcam (37% and 48.1%, respectively). Although DIAGNOcam showed higher sensitivity (0.852) compared to the radiographs (0.519), it showed slightly less specificity (0.569) compared to the radiographs (0.579). However, DIAGNOcam showed higher value of the area under the curve (AUC = 0.722; P < 0.001) compared to the radiographic method (AUC = 0.561; P = 0.308). CONCLUSIONS: The DIAGNOcam showed higher sensitivity and better accuracy than bitewing radiographs in diagnosing cavitated proximal lesions in primary molars and can be generally considered as an alternative to radiographs to detect cavitation without the hazards of ionizing radiation in children.

Comparison of Efficiency and Image Quality of Photostimulable Phosphor Plate and Charge-Coupled Device Receptors in Dental Radiography.

The photostimulable phosphor (PSP) plate and charge-coupled device (CCD) are receptors commonly used for intraoral radiography in U.S. dental schools. However, it is unclear which receptor is more beneficial for radiology education and patient care in an academic setting. The aim of this study was to compare the time efficiency, image quality, and operator performance for student-operated PSP plate and CCD receptors. At one U.S. dental school in 2018, 20 dental hygiene and dental students (n=10 each) were recruited as operators. They each exposed anterior and posterior periapical and bitewing radiographs on dental radiograph teaching and training replica using the PSP plate and CCD as receptors. The time taken to expose the radiographs was recorded. Image sharpness/definition, brightness/contrast, and technical errors, including placement, angulation, and cone cut errors, were evaluated on a three-point scale with 0=non-diagnostic, 1=diagnostic acceptable with minor errors, and 2=perfect diagnostic quality. The results showed that it was generally faster for the students to expose intraoral radiographs with CCDs than with PSP plates, although the difference was not significant (p>0.05). Image quality and technical accuracy, especially angulation, were significantly superior for PSP relative to CCD (p<0.05). This study found that PSP imaging was of higher quality and accuracy than CCD, whereas CCD was more efficient. Dental and dental hygiene students would benefit from being trained on both receptors to be able to adapt to a diversified workplace.