Bitewing Radiographs Research Articles

Diagnostic performance of approximal caries in bitewing radiographs from different monitors and room illuminances.

To compare the accuracy, duration, and certainty of diagnosing approximal caries in bitewing radiographs displayed in three monitors under two luminance conditions. A total of 39 teeth without evident caries were selected from 11 patients undergoing partial jaw resection. Before the operation, 13 bitewing radiographs were captured by a digital imaging system. Eight observers evaluated the images under the dark (9 lux) and bright (200 lux) conditions, using two medical-grade monitors and a commercial monitor. Using histological results as the gold standard, the areas under the receiver operating characteristic curves under different conditions were compared using the Z-test. Multivariate analysis of variance was conducted to assess the impact of various factors on diagnostic duration, while ordinal logistic regression was used to examine factors influencing diagnostic certainty level. It was considered significant when P＜0.05. No significant difference was found in the diagnostic accuracy or duration for diagnosis approximal caries under two luminance conditions with the three distinct monitors (P > 0.05). Ambient light, clinical experience and the pathological grade of approximal caries have influence on the degree of diagnostic confidence (P＜0.05). Different monitors and ambient luminance didn't influence the diagnostic accuracy or evaluation duration. Ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence. The study employing bitewing radiographs from real patients indicates that ambient luminance, clinical experience, and the depth of caries affect the degree of diagnostic confidence.

Classifying Three-Wall Intrabony Defects from Intraoral Radiographs Using Deep Learning-Based Convolutional Neural Network Models.

Intraoral radiographs are used in periodontal therapy to understand interdental bony health and defects. However, identifying three-wall bony defects is challenging due to their variations. Therefore, this study aimed to classify three-wall intrabony defects using deep learning-based convolutional neural network (CNN) models to distinguish between three-wall and non-three-wall bony defects via intraoral radiographs. A total of 1,369 radiographs were obtained from 556 patients who had undergone periodontal surgery. These radiographs, each featuring at least one area of intrabony defect, were categorized into 15 datasets based on the presence of three-wall or non-three-wall intrabony defects. We then trained six CNN models-InceptionV3, InceptionResNetV2, ResNet50V2, MobileNetV3Large, EfficientNetV2B1, and VGG19-using these datasets. Model performance was assessed based on the area under curve (AUC), with an AUC value ≥ 0.7 considered acceptable. Various metrics were thoroughly examined, including accuracy, precision, recall, specificity, negative predictive value (NPV), and F1 score. In datasets excluding circumferential defects from bitewing radiographs, InceptionResNetV2, ResNet50V2, MobileNetV3Large, and VGG19 achieved AUC values of 0.70, 0.73, 0.77, and 0.75, respectively. Among these models, the VGG19 model exhibited the best performance, with an accuracy of 0.75, precision of 0.78, recall of 0.82, specificity of 0.67, NPV of 0.88, and an F1 score of 0.75. The CNN models used in the study showed an AUC value of 0.7 to 0.77 for classifying three-wall intrabony defects. These values demonstrate the potential clinical application of this approach for periodontal examination, diagnosis, and treatment planning for periodontal surgery.

Pulpitis tendency in teeth with vital pulp restored with composite resins: a cross-sectional retrospective study

Background: This study evaluates pulpitis tendency in vital teeth restored with composite resin after at least one year, focusing on the effect of cavity depth and the use of a base material on pulp status. Methods: A retrospective analysis was conducted on 212 patients with one tooth restored by dental senior students. At the follow-up, periapical and bitewing radiographs, cold tests, percussion, and palpation were performed. Cavity depth was measured from bitewing radiographs, and the periapical index (PAI) was recorded. The restored teeth were compared with contralateral sound teeth to assess the impact of the resin on pulp tissue. Results: Composite resin restorations significantly affected pulp status compared to intact teeth (p=0.002). Five out of 212 teeth became necrotic, and four developed symptomatic irreversible pulpitis (SIP). Four necrotic cases occurred in cavities deeper than 3.60 mm, and all SIP cases were associated with cavities deeper than 3.10 mm. Cavity depth was a significant predictor of adverse pulpal outcomes. However, using calcium hydroxide as a base did not significantly affect pulp health. Conclusions: The study concludes that composite resin restorations, mainly in deep cavities, can negatively impact pulp vitality. Cavity depth is a key factor in the risk of pulp necrosis and SIP, while calcium hydroxide base did not reduce pulpitis incidence. Larger studies with longer follow-up periods are recommended to confirm these findings and explore potential protective factors.

Validity assessment of a third-generation light-induced fluorescence device in detecting proximal and occlusal caries lesions: A cross-sectional study

ObjectivesThe objective of this study was to evaluate the efficacy of a quantitative light-induced fluorescence (QLF) device (QrayCam Pro, AIOBIO, Seoul, Republic of Korea) in detecting and differentiating the severity of posterior proximal and occlusal primary caries. Materials and methodsThe study included a total of 120 teeth in 60 patients (of both genders, aged between 21 and 38 years) between January and March 2024, one carious tooth and one sound tooth were selected at random in each patient. All occlusal surfaces were evaluated in accordance with the International Caries Detection and Assessment System (ICDAS) Nyvad Criteria, using the visual tactile method. The ICDAS employs a scoring system ranging from 0 to 6, while the Nyvad Criteria employs a similar scoring system. Addionally, the depth of the proximal lesions was scored (0–6) using bite-wing radiography. The quantitative values representing the maximum loss of fluorescence (∆Fmax) and the maximum change in the ratio of red and green fluorescence (∆Rmax) were obtained using the Q-ray Clinical software v 1.45. To assess the validity, the sensitivity, specificity, and area under the receiver operating characteristics curve (AUROC) were calculated. Spearman correlation coefficient was used to investigate the correlation between the findings of the traditional and QLF examination methods. ResultsThe AUROC values for both ∆Fmax and ∆Rmax were found to be excellent. The sensitivity of QrayCam Pro was found to be 84–88.6%, while the specificity was 100%. The correlation analysis demonstrated a strong correlation between scores obtained from the traditional examination and the QLF values. ConclusionsBased on the findings of this study, QrayCam Pro exhibits potential as a valuable additional tool for not only detecting caries but also for assessing their severity and activity. Clinical relevanceThis study revealed that QrayCam Pro might be suggested as a precise tool for enhancing caries detection and assessment compared to traditional methods. By providing quantitative data on caries severity and activity, it enables more informed treatment decisions, potentially optimizing outcomes for patients.

Diagnostic accuracy of artificial intelligence for approximal caries on bitewing radiographs: A systematic review and meta-analysis

ObjectivesThis systematic review and meta-analysis aimed to investigate the diagnostic accuracy of Artificial Intelligence (AI) for approximal carious lesions on bitewing radiographs. MethodsThis study included randomized controlled trials (RCTs) and non-randomized controlled trials (non-RCTs) reporting on the diagnostic accuracy of AI for approximal carious lesions on bitewing radiographs. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. A systematic search was conducted on November 4, 2023, in PubMed, Cochrane, and Embase databases and an updated search was performed on August 28, 2024. The primary outcomes assessed were sensitivity, specificity, and overall accuracy. Sensitivity and specificity were pooled using a bivariate model. ResultsOf the 2,442 studies identified, 21 met the inclusion criteria. The pooled sensitivity and specificity of AI were 0.94 (confidence interval (CI): ± 0.78–0.99) and 0.91 (CI: ± 0.84–0.95), respectively. The positive predictive value (PPV) ranged from 0.15 to 0.87, indicating a moderate capacity for identifying true positives among decayed teeth. The negative predictive value (NPV) ranged from 0.79 to 1.00, demonstrating a high ability to exclude healthy teeth. The diagnostic odds ratio was high, indicating strong overall diagnostic performance. ConclusionsAI models demonstrate clinically acceptable diagnostic accuracy for approximal caries on bitewing radiographs. Although AI can be valuable for preliminary screening, positive findings should be verified by dental experts to prevent unnecessary treatments and ensure timely diagnosis. AI models are highly reliable in excluding healthy approximal surfaces. Clinical significanceAI can assist dentists in detecting approximal caries on bitewing radiographs. However, expert supervision is required to prevent iatrogenic damage and ensure timely diagnosis.

Clinical, radiographic and biochemical assessment of proximal cavities restored with composite resin using incremental vs. bulk packing techniques: One-year randomized clinical trial

BackgroundBulk-fill resin composites may suffer from recurrent caries around compound proximal restorations in posterior teeth, especially at the proximo-gingival interface.Over 12 months, will the bulk fill technique affect the caries recurrence rate at gingival margins when compared to the conventional incremental packing technique? How early will the first clinical, radiographical, and biochemical evidence of caries recurrence occur?MethodsAfter randomization, in 30 patients with two compound (OM or OD) supragingival lesions, one tooth was restored using the bulk fill technique on one side (group 1) (n = 15). In contrast, the other tooth on the other side was restored utilizing the incremental layering technique (group 2) (n = 15). Both teeth received restorative material (X-tra fil, Voco, Cuxhaven, Germany). The FDI criteria were used to evaluate restorations. As for the periodontal assessment, the gingival index, plaque index, papillary bleeding scoring index and periodontal pocket depth were evaluated. The gingival crevicular fluid (GCF) specimens were gathered, and MMP-9 was extracted and quantitated by ELISA. A customized radiographic template was designed, and 3D printed digital bitewing radiographs were taken. Assessments were done clinically, radiographically and biochemically at baseline (1 week) and after 3, 6 and 12 months. Data was statistically analyzed.ResultsThe null hypothesis was accepted clinically; no statistically significant differences appeared between bulk and incrementally filled posterior restorations. As for the radiographic assessment, the null hypothesis was accepted except for increased periodontal ligament width at 3 months. The null hypothesis for the biochemical evaluation was rejected as there were significant changes in levels of MMP-9 at different testing times.Conclusions1. With similar results but less sensitivity and significant time saving, the bulk fill technique can be considered an efficient alternative to the incremental fill technique in restoring proximal cavities. 2. Early evidence of caries recurrence can be correlated to an increase in the MMP-9 level in gingival crevicular fluid, followed by an increase in radiographic periodontal ligament width measurement.Trial registrationAn ethical approval from the Research Ethics Committee at the Faculty of Dentistry, October 6 University, (Approval No. RECO6U/5-2022). The study was registered at the Pan African Clinical Trials Registry on 24/07/2023 with an identification number (PACTR202307573531455).

In vitro early proximal caries detection using trilateral short-wave infrared reflection at 1050 and 1550 nm.

This in vitro study evaluated the diagnostic potential of short-wave infrared reflection (SWIRR) at 1050 and 1550 nm for proximal caries detection from the occlusal, buccal and lingual surfaces of posterior teeth under clinically relevant conditions. Bitewing radiography (BWR) was the alternative index test and micro-computed tomography (μCT) the reference standard. 250 proximal surfaces of extracted human teeth were examined using SWIRR at 1050 and 1550 nm and BWR. SWIRR, BWR and μCT findings were evaluated twice by two trained examiners. SWIRR images were evaluated from occlusal and trilateral (occlusal, buccal and lingual combined) views. Sensitivity, specificity and AUC were calculated. Reliability assessment was performed using κ statistics. SWIRR (1050 nm) showed sensitivity of 0.44 for occlusal and 0.55 for trilateral assessment, paired with specificity of 0.96 and 0.90, whereas SWIRR (1550 nm) showed sensitivity of 0.73 and 0.85 paired with specificity of 0.76 and 0.59. Compared to occlusal view, trilateral SWIRR view revealed ≈10% higher sensitivity and lower specificity. BWR revealed lowest sensitivity (0.30) and highest specificity (0.99). Over-and underestimation of caries demonstrated opposite trends: from 1050-1550 nm, overestimation of trilateral SWIRR increased (0.08-0.29), while underestimation decreased (0.15-0.06). Trilateral SWIRR has higher sensitivity and lower specificity for proximal caries, than occlusal SWIRR. 1050 nm are more suitable for trilateral SWIRR and 1550 nm for occlusal examinations. A combination of SWIRR at 1050 and 1550 nm may exhibit a balanced sensitivity and specificity for proximal caries.

Evaluation of Proximal Contact Tightness and Contact Area of Posterior Composite Resin Restorations

The purpose of this study is to evaluate the influence of the matrix system on proximal contact tightness (PCT) of posterior composite resin restorations. Standardized class II cavities on 180 first lower molar dentiform model teeth (Frasaco GmbH, Tettnang, Germany) were prepared. Three groups were formed considering the matrix system: Group-IM: Ivory matrix (Hahnenkratt GmbH, Königsbach-Stein, Germany), Group-OM: Omni matrix (Ultradent, South Jordan, UT, USA), and Group-PM: Palodent V3 sectional matrix (Dentsply, Charlotte, NC, USA). Teeth were restored with resin composite mounted in a manikin head to simulate the clinical environment. Proximal contact tightness (PCT) was measured using a custom-made portable dental pressure meter (PDPM), and the validation of the PCT results was performed with a histogram analysis acquired from bite-wing radiography. All data were statistically analyzed by ANOVA and t-test in SPSS software (v.27.0) (p < 0.05). PM group showed statistically tighter contacts on both mesial (PCTm: 228.28 ± 59.17 N) and distal surfaces (PCTd: 254.91 ± 65.69 N) (p > 0.05). Mesial contacts were found to be significantly tighter than distal contacts among all (p < 0.05). According to the histogram results, only in the PM group, the difference between the mesial and distal areas is significant (p < 0.05). Histogram results confirmed that the tightest contact values were achieved in the PM group, followed by the OM and IM groups, respectively (p < 0.05). The use of sectional matrix systems and separation rings is more effective in creating tighter contact than conventional matrix systems. The use of anatomical wedges will help to create a more natural approximal contour and narrow contact area.

Comparison of proximal contact and contours of class II restoration in premolars and molars restored with three different matrix systems using FDI criteria: An in vivo study.

Sectional matrix systems, namely, Garrison and Bioclear with contact rings, are valuable aids to establish proximal contact tightness and contours in Class II composite restorations when compared with the circumferential matrix system Tofflemire. This study aimed to compare the proximal contact and contours of Class II restoration in premolars and molars restored with Tofflemire, Garrison, and Bioclear matrix system by FDI criteria of approximal anatomical form using floss or 25-µm metal blade and radiographic examination. Sixty Class II patients were selected, and a preoperative bitewing radiograph was taken. Initially, caries removal was done by round diamond bur, followed by 245 carbide bur. Samples were divided randomly into three groups based on the type of matrix system used for restoration. Group 1 (n = 20): Tofflemire, Group 2 (n = 20): Garrison matrix system, and Group 3 (n = 20): Bioclear matrix system. Matrix systems were applied according to the manufacturer's instructions, and composite restoration was done. The clinical evaluation of proximal contact and contour was done by a blinded observer immediately after placement of composite restoration using FDI criteria by passing dental floss or 25-µm metal blade, whereas marginal gaps and overhangs were checked using a radiograph. Results were statistically analyzed. The statistical analysis was done using the Statistical Package for the Social Sciences (SPSS for Windows, Version 19.0). Descriptive statistics was calculated as frequency and percentage. The comparison of characteristics of the contact points, contour, and radiographic examination was carried out using the Chi-square test. Multiple comparisons were done by comparing column proportions with Bonferroni adjustments. The level of significance for the present study was fixed at P < 0.05. There were significantly higher proportions of normal contacts in Garrison (60%) and Bioclear (70%) compared to that in the Tofflemire (20%) matrix system (Bioclear > Garrison > Tofflemire). Comparison of contours showed normal contours with Garrison (50%), Bioclear (50%), and Tofflemire (15%) matrix system (Bioclear = Garrison > Tofflemire). In radiographic examination, harmonious transitions between the restoration and the tooth were similar in Garrison (30%) and Bioclear (30%) compared to the Tofflemire (15%) matrix system (Bioclear = Garrison > Tofflemire). Garrison and Bioclear sectional matrix systems established superior contacts and contours than the Tofflemire circumferential system.

Deep Convolutional Neural Network for Automated Staging of Periodontal Bone Loss Severity on Bite-wing Radiographs: An Eigen-CAM Explainability Mapping Approach.

Periodontal disease is a significant global oral health problem. Radiographic staging is critical in determining periodontitis severity and treatment requirements. This study aims to automatically stage periodontal bone loss using a deep learning approach using bite-wing images. A total of 1752 bite-wing images were used for the study. Radiological examinations were classified into 4 groups. Healthy (normal), no bone loss; stage I (mild destruction), bone loss in the coronal third (< 15%); stage II (moderate destruction), bone loss is in the coronal third and from 15 to 33% (15-33%); stage III-IV (severe destruction), bone loss extending from the middle third to the apical third with furcation destruction (> 33%). All images were converted to 512 × 400 dimensions using bilinear interpolation. The data was divided into 80% training validation and 20% testing. The classification module of the YOLOv8 deep learning model was used for the artificial intelligence-based classification of the images. Based on four class results, it was trained using fivefold cross-validation after transfer learning and fine tuning. After the training, 20% of test data, which the system had never seen, were analyzed using the artificial intelligence weights obtained in each cross-validation. Training and test results were calculated with average accuracy, precision, recall, and F1-score performance metrics. Test images were analyzed with Eigen-CAM explainability heat maps. In the classification of bite-wing images as healthy, mild destruction, moderate destruction, and severe destruction, training performance results were 86.100% accuracy, 84.790% precision, 82.350% recall, and 84.411% F1-score, and test performance results were 83.446% accuracy, 81.742% precision, 80.883% recall, and 81.090% F1-score. The deep learning model gave successful results in staging periodontal bone loss in bite-wing images. Classification scores were relatively high for normal (no bone loss) and severe bone loss in bite-wing images, as they are more clearly visible than mild and moderate damage.

Automatic deep learning detection of overhanging restorations in bitewing radiographs.

This study aimed to assess the effectiveness of deep convolutional neural network (CNN) algorithms for the detecting and segmentation of overhanging dental restorations in bitewing radiographs. A total of 1160 anonymized bitewing radiographs were used to progress the artificial intelligence (AI) system for the detection and segmentation of overhanging restorations. The data were then divided into three groups: 80% for training (930 images, 2399 labels), 10% for validation (115 images, 273 labels), and 10% for testing (115 images, 306 labels). A CNN model known as You Only Look Once (YOLOv5) was trained to detect overhanging restorations in bitewing radiographs. After utilizing the remaining 115 radiographs to evaluate the efficacy of the proposed CNN model, the accuracy, sensitivity, precision, F1 score, and area under the receiver operating characteristic curve (AUC) were computed. The model demonstrated a precision of 90.9%, a sensitivity of 85.3%, and an F1 score of 88.0%. Furthermore, the model achieved an AUC of 0.859 on the receiver operating characteristic (ROC) curve. The mean average precision (mAP) at an intersection over a union (IoU) threshold of 0.5 was notably high at 0.87. The findings suggest that deep CNN algorithms are highly effective in the detection and diagnosis of overhanging dental restorations in bitewing radiographs. The high levels of precision, sensitivity, and F1 score, along with the significant AUC and mAP values, underscore the potential of these advanced deep learning techniques in revolutionizing dental diagnostic procedures.

A RETROSPECTIVE CLINICAL AUDIT OF QUALITY OF INDIRECT DIGITAL INTRAORAL RADIOGRAPHS TAKEN BY STUDENTS AND DENTAL ASSISTANTS IN AN UNDERGRADUATE DENTAL CLINIC

Background: Intraoral radiographs are essential diagnostic tools in dentistry. Ensuring their quality is crucial for accurate diagnosis and treatment planning. This study compared the quality of radiographs produced by undergraduate dental students and qualified dental assistants to identify common errors and assess improvements following feedback and interventions. Methods: This retrospective, observational study compared the quality of digital bitewing and periapical radiographs taken by students and assistants across two audit cycles, with feedback provided between cycles. The radiographs were assessed using a modified quality assessment scale based on the Faculty of General Dental Practice (FGDP, UK) guidelines. Data were collected from the radiology imaging software, and a total of 100 intraoral digital radiographs were analyzed for each group in both cycles. Results: Initially, radiographs from both groups had issues, primarily positioning errors. After providing feedback and additional training, significant improvements were observed from Cycle 1 to Cycle 2. Dental assistants had a higher percentage of acceptable periapical radiographs, while students excelled in bitewing radiographs. Both groups demonstrated marked improvements, reflecting the effectiveness of the interventions. Conclusions: Continuous training, feedback, and quality assurance measures are vital for improving radiograph quality. A multifaceted approach, including updated equipment and adherence to quality control protocols, can significantly enhance patient care. The study highlights the importance of regular calibration and training for dental professionals to maintain high standards in radiographic practices. Further research is recommended to identify additional strategies for improving radiographic quality.

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

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). 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. 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. 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. When compared to BWR, NIDIT showed a high specificity but a low sensitivity for proximal caries detection in primary teeth.

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.

AI-Assisted Detection of Interproximal, Occlusal, and Secondary Caries on Bite-Wing Radiographs: A Single-Shot Deep Learning Approach.

Tooth decay is a common oral disease worldwide, but errors in diagnosis can often be made in dental clinics, which can lead to a delay in treatment. This study aims to use artificial intelligence (AI) for the automated detection and localization of secondary, occlusal, and interproximal (D1, D2, D3) caries types on bite-wing radiographs. The eight hundredand sixty bite-wing radiographs were collected from the School of Dentistry database. Pre-processing and data augmentation operations were performed. Interproximal (D1, D2, D3), secondary, and occlusal caries on bite-wing radiographs were annotated by two oral radiologists. The data were split into 80% for training, 10% for validation, and 10% for testing. The AI-based training process was conducted using the YOLOv8 algorithm. A clinical decision support system interface was designed using the Python PyQT5 library, allowing for the use of dental caries detection without the need for complex programming procedures. In the test images, the average precision, average sensitivity, and average F1 score values for secondary, occlusal, and interproximal caries were obtained as 0.977, 0.932, and 0.954, respectively. The AI-based dental caries detection system yielded highly successful results in the test, receiving full approval from dentists for clinical use. YOLOv8 has the potential to increase sensitivity and reliability while reducing the burden on dentists and can prevent diagnostic errors in dental clinics.

Conquering class imbalances in deep learning-based segmentation of dental radiographs with different loss functions

ObjectiveThe imbalanced nature of real-world datasets is an ongoing challenge in the field of machine and deep learning. In medicine and in dentistry, most data samples represent patients not affected by pathologies, and on imagery, pathologic image areas are often smaller than healthy ones. Selecting suitable loss functions during deep learning is essential and may help to overcome the resulting imbalance. We assessed six different loss functions for one exemplary task, tooth structure segmentation on bitewing radiographs, for their performance. MethodsSix different loss functions (Focal Loss, Dice Loss, Tversky Loss and hybrid losses of Cross-Entropy and Dice Loss, Focal and Dice Loss, Focal and Generalized Dice Loss) were compared on a tooth structure segmentation task of 1,625 bitewing radiographs. Training was performed using three different model architectures (U-Net, Linknet, DeepLavbV3+) over a 5-fold cross-validation. Tooth structures consisted of the classes (occurrence in% of samples/captures areas measured on pixel level) enamel (100 %/25 %), dentin (100 %/50 %), root canal (100 %/10 %), filling (81 %/8 %) and crown (28 %/5 %). ResultsHybrid loss functions significantly outperformed standalone ones and provided robust results over the different architectures for the classes enamel, dentin, root canal and filling. Specifically, the Dice Focal loss reached high performance to conquer both image level and pixel level class imbalance, respectively. Clinical SignificanceIn dental use cases it is often important to predict minority classes such as pathologies accurately. Using specific loss function may be an effective strategy to overcome data imbalance when training deep learning models.

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

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. 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). 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. Brightness and contrast setting was more efficient in improving the diagnostic potential of bitewing radiographs compared to other adjustments.

Case Report on Caries Assessment Using Intraoral Scanner Compared with Bitewing Radiographs.

Dental caries remains one of the main reasons patients seeing their dentist. They either show up for preventive measures, diagnostics, or treatment of present caries lesions. To date, diagnostics are performed visually, supported by using bitewing radiographs. While radiographic diagnostics should only be performed on a biannual basis, and some caries process will not be seen with visual diagnostics, there remains a lack in regular checkups. Therefore, different technical applications can be used for regular diagnostics. One of those is the near-infrared imaging (NIRI) technology. In this case report, a patient presented with incipient caries lesions. These lesions were diagnosed visually, radiographically, and using NIRI. After diagnosis of incipient caries lesions, the patient was advised to use a hydroxyapatite toothpaste and a hydroxyapatite gel for the remineralization of the lesions and prevention of caries progression. The patient was followed up for 6 months with regular checkups in between. Visual diagnostics and NIRI were used to detecting the caries progress. After 6 months, bitewing radiographs were used in addition. In this clinical investigation we were able to show for the first time that NIRI and bitewing radiographs are able to detect and follow incipient caries lesions. Additionally, this study highlights that hydroxyapatite-containing oral care products are able to arrest and remineralize the caries process.

Near-infrared imaging in orthodontic intraoral scanners for early interproximal caries detection

Intraoral scanners commonly used in orthodontic offices now offer near-infrared imaging (NIRI) technology, advertised as a screening tool to identify interproximal caries. This study aimed to evaluate the reliability and validity of NIRI detection of interproximal carious lesions in a common intraoral scanner (iTero Element 5D; Align Technology, San Jose, Calif) with and without bitewing radiograph complement, compared with a microcomputed tomography (micro-CT) reference standard. Extracted human posterior teeth (premolars and molars) were selected for early (noncavitated) interproximal carious lesions (n= 39) and sound control surfaces (n= 47). The teeth were scanned via micro-CT for evaluation by 2 blinded evaluators using consensus scoring. The teeth were mounted to simulate anatomic interproximal contacts and underwent a NIRI scan using iTero Element 5D and bitewing radiographs. Two trained, calibrated examiners independently evaluated (1) near-infrared images alone with clinical photograph, (2) bitewing radiograph alone with clinical photograph, and (3) near-infrared images with bitewing radiograph and clinical photograph in combination, after at least a 10-day washout period between each evaluation. Interrater reliability was highest for NIRI alone (k= 0.533) compared with bitewing radiograph alone (k= 0.176) or in combination (k= 0.256). NIRI alone showed high specificity (0.83-0.96) and moderate sensitivity (0.42-0.63) compared with a micro-CT reference standard. Dentin lesions were significantly more reliably detected than enamel lesions. After rigorous training and calibration, NIRI can be used with moderate reliability, high specificity, and moderate sensitivity to detect noncavitated interproximal carious lesions.

Association between the concentration of salivary lactoferrin, smoking, and periodontal disease parameters: A randomized observational study

Abstract Introduction: Saliva, a versatile biological fluid, has gained recognition as an invaluable medium for point-of-care diagnostics, offering insight into the complex dynamics of periodontal tissue inflammation. Aims: This study aimed to assess salivary lactoferrin (LF) concentration among individuals diagnosed with periodontitis (PDS) compared to a control group. Materials and Methods: A randomized, observational study enrolled 441 participants residing in Southern Sweden, aged 20 and 89 years and diagnosed with PDS. Clinical examination was performed following a standardized periodontal protocol, measuring key periodontal parameters such as plaque index (PI), bleeding index (BI), periodontal pocket depth (PPD), and marginal alveolar bone level/bone loss using posterior intraoral bitewing radiographs. Stimulated saliva samples were analyzed for LF concentration using enzyme e-linked immunosorbent assay. Statistical analysis included Pearson correlation and ANOVA variance with a 95% confidence interval. Results and Discussion: We found no significant difference among the studied groups in mean salivary LF concentration (P &gt; 0.05), whereas we observed a negative association between PI and salivary LF concentration (Pearson correlation = 0.110, P = 0.024), suggesting that increased plaque scores were associated with decreased LF level. In addition, smokers exhibited lower LF levels (P = 0.001). Notably, salivary LF concentration did not correlate with selected clinical parameters of PDS, including BI and PPD. Conclusions: These results underscore the multifaceted nature of salivary components and emphasize the need to consider various factors when evaluating the impact of PDS on saliva. Further research is imperative to validate risk factors contributing to the development of PDS and the diagnostic potential of salivary biomarkers, considering the intricate interplay between saliva and periodontal status. This study contributes valuable insight into the potential utility of saliva as a diagnostic medium.