Bitewing Radiographs Research Articles

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.

Accuracy of Extraoral Bitewing Compared with Histopathology in Proximal Caries Detection of Primary Molar Teeth

An accurate diagnosis of dental caries leads to a suitable treatment plan and prevents premature loss of primary teeth. Intraoral bitewing radiography helps caries determination but has limitations in pediatric patients with severe gag reflexes while the extraoral bitewing radiograph resolved the patients who refused to undergo intraoral radiographs. The research objective was to compare the accuracy of extraoral bitewing radiography and the gold standard histopathological examination for proximal caries detection in the primary molars. Twenty-four extracted primary molars with and without proximal caries were divided into three groups and arranged in the mimetic alveolar sockets of a 3D-printed skull and mandible. Two observers evaluated extraoral bitewing images separately twice at one-week intervals. The weighted kappa coefficients showed excellent intra-observer and inter-observer agreements between each session of the extraoral bitewing radiographic assessments. The Mann-Whitney U test showed no difference between the radiographic grading scores of extraoral bitewing images and the gold standard. The sensitivity, positive predictive value (PPV), negative predictive value (NPV), and the area under the receiver operating characteristic (ROC) curve in cavitated carious lesions were higher than in non-cavitated carious lesions. In conclusion, extraoral bitewing is an alternative radiography for pediatric patients who are either uncooperative or intolerant to intraoral radiography.

Analysis of Artifacts and Errors on Intraoral Phosphor Plate Radiographs: A Retrospective Study

Introduction. The advantages of phosphor plates (PPs), including their flexibility, thinness, and wireless connectivity, have contributed to their widespread use in dentistry alongside the advancement of digital radiography techniques. Continuous use of PPs and certain errors may lead to artifacts on the images. This retrospective study aimed to determine the frequency and types of intraoral PP artifacts.
 Methods. This study was conducted on 814 intraoral PP radiographs, including periapical and bitewing images. The errors and artifacts were classified into 4 main categories: technical errors, plate-related artifacts, scanner-induced artifacts, and ambient light artifacts.
 Results. A total of 656 periapical and 158 bitewing radiographs were examined. The most observed artifacts on the radiographs were plate-related artifacts, followed by technical errors (n=542, n=461, respectively). Within the category of plate-related artifacts, the most common artifacts were cracks or scratches (n=418, 77.1%), while within the category of technical errors, these were cone-cut errors (n=188, 40.7%). Parallel or zigzag radiopaque lines were the most common scanner-induced artifacts (n=313, 98.7%) and fading was the most common ambient light artefact (n=93, 49.2%). On more than half of the PPs, more than one artifact group was observed.
 Conclusions. Cone-cut, cracks/scratches, parallel zigzag radiopaque lines, and fading were common PP artifacts and errors in this study. The analysis revealed that over half of the PPs exhibited appearance of more than one artifact group. The high frequency of artifacts poses a risk of incorrect and incomplete diagnoses in radiographs, emphasizing the crucial importance of understanding the causes and developing effective prevention strategies for artifacts to enhance the reliability of diagnostic imaging and ensure the accuracy of patient assessments.

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

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. 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. 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. The proposed model exhibited good achievement, highlighting the potential use of CNNs for tooth detection, numbering, and caries detection, concurrently. 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.

New Technique for Wedge Selection in Direct Class II Restorations: A Pilot Study.

Background: Performing an appropriate Class II direct restoration is a great challenge. The correct selection of the matrix system and the elements used for its stabilization will have a great impact on the result. The aim of this study is to show a new digital method for a predictable selection of the wedge and compare it with the conventional method. Methods: Sixty patients were randomly divided into two groups. In Group 0, the wedge was selected intraoperatively by visual examination, while in Group 1 the wedge was selected preoperatively through the measurement of the cervical embrasure on the bite-wing radiography. The number of wedges used, modifications to them, and tissue damage were registered, along with the quality of the proximal contact and the marginal adaptation. Results: Student's t-test revealed a statistical difference between the number of wedges used, which was higher in Group 0 (p < 0.05). Pearson Chi-square test showed that tissue damage was statistically higher in Group 0 (p < 0.05), while there was no statistically significant difference between groups in wedge modifications (p > 0.05). Group 1 revealed a higher frequency of satisfactory proximal contact and marginal adaptation (p < 0.05). Conclusions: This new technique reduces wedges waste and tissue damage and provides an adequate interproximal anatomy.

Automatic caries detection in bitewing radiographs—Part II: experimental comparison

ObjectiveThe 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 methodsFour 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.ResultsThe 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.ConclusionsThe automatic method consistently outperformed novices and performed as well as highly experienced dentists.Clinical significanceThe 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.

Tooth numbering and classification on bitewing radiographs: an artificial intelligence pilot study

ObjectiveThe aim of this study is to assess the efficacy of employing a deep learning methodology for the automated identification and enumeration of permanent teeth in bitewing radiographs. The experimental procedures and techniques employed in this study are described in the following section. Study DesignA total of 1248 bitewing radiography images were annotated using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset has been partitioned into 3 subsets: training (n = 1000, 80% of the total), validation (n = 124, 10% of the total), and test (n = 124, 10% of the total) sets. The images were subjected to a 3 × 3 clash operation in order to enhance the clarity of the labeled regions. ResultsThe F1, sensitivity and precision results of the artificial intelligence model obtained using the Yolov5 architecture in the test dataset were found to be 0.9913, 0.9954, and 0.9873, respectively. ConclusionThe utilization of numerical identification for teeth within deep learning-based artificial intelligence algorithms applied to bitewing radiographs has demonstrated notable efficacy. The utilization of clinical decision support system software, which is augmented by artificial intelligence, has the potential to enhance the efficiency and effectiveness of dental practitioners.

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

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). 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. 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. Intraoral scanners with the NIRI feature may be used for diagnosing interproximal caries, especially for permanent molars. 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.

Radiographic evaluation of teeth with pulp stones and pulp canal obliteration: characteristics, and associations with dental parameters

ABSTRACT The study aimed to radiographically assess the characteristics of pulp stones (PS) and pulp canal obliteration (PCO) in teeth and examined their associations with various dental parameters such as caries, restorations, periodontal status, and age. This cross-sectional observational study was conducted at the Faculty of Dentistry, King Abdulaziz University between September 2022, and May 2023, involved 101 patients exhibiting 402 teeth with PS or PCO. Data were collected from periapical and bitewing radiographs, and analyzed by two calibrated dentists. Multiple dental parameters were assessed, including caries level, presence and level of restorations, periodontal condition, and age of the patient. The study population consisted of 62 females and 39 males, with an age range of 18–65 years. Inter- and intra-examiner reliability were high (Kappa = 0.88 and 0.98 respectively). PS were more commonly found in molars (81.2%), while PCO were presented in only 115 teeth (23%). Age significantly affected the type of calcification (p < 0.001), with PS more common in the 20–30 age group and PCO more common in individuals over 40. Presence of caries was significantly associated with the type of calcification (p = 0.013), but restoration was not. The majority of teeth with PS (76%) or PCO (93%) had healthy periodontium. Around 40% of teeth with PCO showed signs of periapical changes, a finding significantly different from those with PS (p < 0.001). Pulp calcifications were significantly associated with various dental parameters, including caries presence, age, and periodontal status. The findings provide crucial insights into the epidemiology and aetiology of pulp calcifications.

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

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. 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. 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). 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.

Detection of Tooth Position by YOLOv4 and Various Dental Problems Based on CNN With Bitewing Radiograph

Detection of Tooth Position by YOLOv4 and Various Dental Problems Based on CNN With Bitewing Radiograph

Association between prosthesis contour and peri-implantitis in patients compliant with supportive periodontal therapy: A retrospective cohort study.

Poor contour of the implant restoration causes plaque accumulation and increases the risk of peri-implantitis. This study aimed to investigate whether the prosthodontic components of dental implants were associated with the prevalence of peri-implantitis. We enrolled 185 patients with 348 implants who underwent at least 1-year follow-up after the delivery of the prosthesis from February 2010 to January 2021. Demographic data of the patients and implants and the follow-up period were recorded. The emergence angle, type of cervical crown contour, and contour angle were analyzed using annual bite-wing radiographs. Peri-implantitis in this study was diagnosed if the peri-implant bone loss was greater than 2 mm between the bite-wing radiographs taken at baseline and the latest. Chi-square test, two-sample t-test, and multivariate logistic regression were used to investigate the differences and odds ratios between the peri-implantitis and non-peri-implantitis groups. The incidence of peri-implantitis was 14.9% during a follow-up period of 1509 days after the delivery of the prosthesis for at least 1-year. Based on the prevalence of non-peri-implantitis and after adjusting for confounding factors, the risk factors identified were bone types for implants (native bone vs. alveolar ridge preservation: adjusted odds ratio = 2.43, P = 0.04). Sex, arch, and guided bone regeneration vs. alveolar ridge preservation have the potential for a statistical difference. Compared with implants at alveolar ridge preservation sites, implants in the native bone were more prone to peri-implantitis. Further randomized controlled trials are required to determine these associations.

Determining the Ability of Senior Dental Students to Detect Interproximal Caries Using Different Radiographic Techniques.

Objective: To evaluate the diagnostic capability of 5th-year students using digital imaging, conventional bitewing (BW) radiographs, and printed film on paper to detect interproximal caries lesions. Methods: A cross-sectional study was conducted with senior dental students. Three different radiographs: digital, BW, and printed films on paper were used; thus, nine radiographs, each with a 2-min viewing time, were considered by students along with a questionnaire. A control group of specialists from Prosthodontics and Radiology had finalized the answers prior to conducting the study. The appropriate responses were divided into five categories: R0: Intact surface, R1: Radiolucency in the outer half of enamel, R2: Radiolucency in the inner half of enamel, R3: Radiolucency in the outer half of dentin, and R4: Radiolucency in the inner half of dentin. Students' responses were analyzed using a one-way analysis of variance (ANOVA) test and a t-test. Results: Ethics for the study was obtained from the institutional committee (Reg No: BM19/9/8). When compared with the control group using ANOVA testing, the results showed good detection accuracy with a success rate of ~64 accuracy. There was a significant difference in the outcomes when detecting the presence of the caries lesion between the three diagnostic techniques (p > 0.001). In detecting the size of the carious lesion, the students' ability was recorded as poor. The highest average for detecting the presence of the carious lesion was correspondent to the printed film on paper method. Conclusions: Senior dental students have shown good accuracy in detecting the presence but not the size of interproximal caries on all radiographs viewed. Clinical Significance: Teachings and availability of diverse radiological diagnostic techniques ensured a reasonable level of understanding and use of the diagnostic caries risk assessment methodologies as required in restorative treatment planning.

Predictors of procedural errors in class II resin composite restorations using bitewing radiographs

ObjectiveTo identify the potential factors that induce procedural errors during posterior proximal resin composite restorations placed by dental students. Materials and MethodsThis retrospective study evaluated 803 bitewing radiographs of posterior proximal resin composite restorations placed by dental students at Imam Abdulrahman bin Faisal University. Atypical radiographic signs of failure were screened, and different patient-, operator-, and clinical-related factors were recorded. Chi-square test was used to examine the relationship between procedural errors and recorded factors. Stepwise adjusted logistic regression model was performed to identify predictors of procedural errors. ResultsThe most observed errors were internal gaps at the bonding interface and internal voids. Molars had 0.39 the risk of internal voids (odds ratio [OR] = 0.39; confidence interval [CI] = 0.25–0.60; P = <0.0001), 0.41 the risk of sharp angle (OR = 0.41; CI = 0.24–0.68; P = <0.001), and 0.57 the risk of open contact (OR = 0.57; CI = 0.34–0.97; P = 0.04) compared to premolars. Those who were >40 years of age had 1.79 the risk of overhang compared to younger patients (OR = 1.79; CI = 1.04–3.11; P = <0.04). First molars and premolars had 0.64 the risk of overhang compared to second molars and premolars (OR = 0.64; CI = 0.41–1.00; P = 0.04). Junior students had 1.97 the risk of internal gap compared to their senior counterparts (OR = 1.97; CI = 1.20–3.21; P = 0.008). Mesial restorations had 0.38 the risk of external gap compared to mesio-occluso-distal (MOD) restorations (OR = 0.38; CI = 0.19–0.78; P = 0.003). Restorations with a margin coronal to the cemento-enamel junction (CEJ) had 0.44 the risk of external gap compared to those restorations with a margin apical to the CEJ (OR = 0.44; CI = 0.29–0.66; P = <0.0001). ConclusionOur findings suggested a higher incidence of procedural errors in restoring premolars and MOD cavity preparations. Therefore, it is crucial to enhance the comprehensiveness of laboratory training and expose students to diverse clinical scenarios and various techniques.

NEAR-INFRARED TRANSILLUMINATION COMPARED TO DIGITAL BITEWING RADIOGRAPHY FOR PROXIMAL CARIES DETECTION

Proximal caries is a diagnostic and therapeutic challenge. Aim: to study the diagnostic accuracy of near-infrared transillumination (DIAGNOcam, KaVo) compared to digital bitewing radiography for initial proximal caries diagnosis. Materials and methods: 1600 proximal surfaces of premolars and molars of 50 patients were examined with DIAGNOcam and digital bitewing radiography, and the level of agreement between them was established. Results: for the diagnosis of proximal caries in dentin, a complete (100%) coincidence of the findings with the type of tooth and type of surface was established. For the diagnosis of proximal caries in enamel, a high level of agreement between the two methods was found, as transillumination with near-infrared light is more sensitive than digital bitewing radiography. Conclusion: DIAGNOcam is a promising screening device in clinical practice for early proximal caries detection.

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

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

Comparative Diagnostic Accuracy of VistaCam IX Proxi and Bitewing Radiography for Detection of Interproximal Caries.

Early detection of caries and the extent of carious lesions for appropriate treatment planning are very important and lead to introduction of new diagnostic tools. This study aimed to compare the diagnostic accuracy of VistaCam IX Proxi and bitewing radiography for detection of posterior interproximal caries. This in vitro study was performed on 40 extracted posterior teeth without cavitated carious lesions. Bitewing radiographs were obtained, infrared (IR) examination was performed, and the teeth were sectioned for histopathological analysis under a stereomicroscope as the gold standard for detection of caries and determination of the extent of carious lesions. Data were analyzed with Cohen's kappa statistic, and Wilcoxon rank sum test. The specificity of VistaCam IX Proxi and bitewing radiography was 71.4% and 87.7%, respectively. Their sensitivity was 100% and 40% for enamel caries, 72.8% and 54.5% for external half dentin caries, and 82.3% and 58.8% for internal half dentin caries, respectively (p= 0.048). Bitewing radiography had a higher specificity and lower rate of false positive results. However, VistaCam IX Proxi had higher sensitivity for caries detection with lower rate of false negative results. Considering the higher sensitivity and significantly lower frequency of false negative results by VistaCam IX Proxi, it may be reliably used for caries detection specially enamel caries, and can serve as an adjunct to bitewing radiography.

Impact of artificial intelligence on dentists’ gaze during caries detection: A randomized controlled trial

ObjectivesWe aimed to understand how artificial intelligence (AI) influences dentists by comparing their gaze behavior when using versus not using an AI software to detect primary proximal carious lesions on bitewing radiographs. Methods22 dentists assessed a median of 18 bitewing images resulting in 170 datasets from dentists without AI and 179 datasets from dentists with AI, after excluding data with poor gaze recording quality. We compared time to first fixation, fixation count, average fixation duration, and fixation frequency between both trial groups. 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 compared the transitional pattern of the dentists’ gaze between the trial groups. ResultsMedian time to first fixation was shorter in all groups of teeth for dentists with AI versus without AI, although p>0.05. Dentists with AI had more fixations (median=68, IQR=31, 116) on teeth with restorations compared to dentists without AI (median=47, IQR=19, 100), p = 0.01. In turn, average fixation duration was longer on teeth with caries for the dentists with AI than those without AI; although p>0.05. The visual search strategy employed by dentists with AI was less systematic with a lower proportion of lateral tooth-wise transitions compared to dentists without AI. ConclusionsDentists with AI exhibited more efficient viewing behavior compared to dentists without AI, e.g., lesser time taken to notice caries and/or restorations, more fixations on teeth with restorations, and fixating for shorter durations on teeth without carious lesions and/or restorations. Clinical significanceAnalysis of dentists’ gaze patterns while using AI-generated annotations of carious lesions demonstrates how AI influences their data extraction methods for dental images. Such insights can be exploited to improve, and even customize, AI-based diagnostic tools, thus reducing the dentists’ extraneous attentional processing and allowing for more thorough examination of other image areas.

Sensitivity and specificity of the ICDAS II system and bitewing radiographs for detecting occlusal caries using the Spectra™ caries detection system as the reference test in children

BackgroundMost studies assessing the sensitivity and specificity of caries detection methods have been performed in vitro using the histological method as the gold standard showing inconsistent values. The aim of this study was to compare the sensitivity and specificity in detecting occlusal caries using the International Caries Detection and Assessment System (ICDAS II) with the radiographic method (RM), while using the Spectra™ Caries Detection System (SCDS) as the reference test.MethodsOne hundred sixty children, ages 7–12 years, participated in the study. Five zones in the occlusal surfaces of 859 primary and 632 first permanent molars were examined visually using ICDAS-II, the RM using bitewing radiographs and SCDS. The descriptive statistics of sensitivity and specificity were calculated and compared.ResultsFor all molars combined and for primary molars only, the sensitivity of ICDAS II was higher for detecting total caries (p < 0.001), caries in enamel (p < 0.001), and caries in dentin (p = 0.016), but it was not different for detecting caries in the dentin of permanent first molars (p = 0.214), and primary second molars (p = 0.761). The specificity of RM was higher for detecting total caries, caries in enamel for all molars combined and for permanent first molars (p < 0.001). For caries in dentin, the specificity of ICDAS II was higher for all molars combined and for primary molars only (p < 0.001). For total caries in primary molars only, and caries in dentin of permanent first molars only, the specificity was not different (p = 0.156 and p = 0.181 respectively).ConclusionsThe sensitivity and specificity of ICDAS II and RM changes depending on whether the carious lesion compromises the enamel or dentin, and if the caries detection is performed in the primary molars or permanent first molars.

The value of bitewing radiographs in the management of carious primary molars - the impact on treatment planning.

Introduction Bitewing radiographs (BWs) with visual examination (VE) are critical for the early detection of caries in primary molars. However, their influence on treatment planning is not extensively studied. Given they involve exposure to ionising radiation, it is essential to understand their impact on treatment decisions.Aim To explore the impact of BWs in the management of carious primary molars.Design Cross-sectional study.Materials and methods A total of 30 children (mean age 6.2 years) referred for caries management in a dental hospital setting were recruited. One of six paediatric dental specialists performed VEs and scored pre-BW treatment needs (TN) for each primary molar, followed by reviewing the BWs, re-scoring TN and recording their opinion on treatment modality. Data were assessed using descriptive analyses. Five treatment planning descriptors detailing the reasons for change were identified.Results In total, 240 primary molars were examined. BWs resulted in an overall change in treatment plan for 24/30 (80.0%) children, with the majority (20/30; 66.7%) becoming more invasive. General anaesthesia was the commonest proposed modality (13/30; 43.3%) children), followed by inhalation sedation (10/30; 33.3%) and local anaesthesia (6/30; 20.0%), with only one child (3.3%) in the 'no analgesia' modality.Conclusion BWs play an important role in treatment planning carious primary molars.