Referral Patterns of General Dental Practitioners for Implant Surgery Procedures.

PURPOSE: The growing demand for implants has led to their implementation by general dental practitioners (GDPs) in clinical practice. The present study assessed referral patterns of GDPs for the surgical phase of implant dental treatment. MATERIALS AND METHODS: One hundred fifty GDPs were asked to fill out a structured questionnaire containing their demographic data and answer six questions characterising their referral patterns for implant dentistry. RESULTS: Forty-one (41%) percent performed the surgical phase, and 87% provided implant restoration. Gender was the only influencing factor for the surgical phase, as 51.4% of male GDPs and 6.5% of female GDPs performed implant surgery themselves. Experience and practice set-up did not influence the referring decision. Fifty-four percent of the practitioners referred 0 to 5 patients per month, and the chosen specialists were: 80% oral and maxillofacial surgeon, 11% periodontist, and 9% selected a specialist depending on the individual case. The major reasons influencing the referral pattern were the complexity of the surgical procedure, followed by systemic medical compromise of the patient. CONCLUSIONS: Most implant surgeries in Israel are still performed by specialists.

Dental Loupe's Role in Detection of Caries of Molars in Children by Student and Dentists.

PURPOSE: The objectives of this study were to examine the effectiveness of diagnosing occlusal caries in molar teeth in children under the use of loupes and, secondarily, to examine whether there is a difference in the diagnosis between permanent and primary teeth using dental loupes. In addition, to check whether the student's diagnosis using loupes improves caries diagnosis compared to dentists' diagnosis in both methods. METHODS: The data were collected from 163 patients aged 6-14 who sought treatment in the Pedodontic Department of the Faculty of Dentistry at Tel-Aviv University during 2020-2021. The first and second permanent molars and second primary molars with deep groves were examined. A student and dentists made the diagnosis with and without loupes while using the ICDAS criteria. RESULTS: The student's examinations without the loupes detected no caries in 60% of the cases compared to 76.9% in the examinations with the loupes and found initial caries without cavitation (ICDAS1) in only 17.6% of teeth without loupes examination compared to 33% using loupes. The dentist correctly diagnosed no caries (ICDAS0) in 82.1% of cases without loupes and initial caries without cavitation (ICDAS1) in 62.5% of cases. The dentist correctly diagnosed distinct caries without cavitation (ICDAS2) in 90.8% of cases. No differences were observed in caries diagnosis between primary and permanent teeth when the examiner was a specialist/intern using loupes; however, there was a statistically significant difference (p = 0.047) when the diagnosis was made by a student using loupes. CLINICAL SIGNIFICANCE: The use of dental loupes is an effective method for the correct and early diagnosis of occlusal caries lesions in children's molar teeth by both dentists and students, and this is in accordance with the principle of minimally invasive dentistry. There is a justification for the use of dental loupes for the diagnosis of initial occlusal caries in primary and permanent molars in children in a precise way. Using loupes especially improves the correct diagnosis of initial caries in primary teeth by students.

Possible Association between Methylphenidate and Mandibular Bone Characteristics Detected by Dental Panoramic Radiograph in Children and Adolescents with ADHD.

Some studies have shown that children treated with psychostimulants for attention-deficit hyperactivity disorder (ADHD) have decreased bone mineral density (BMD). Mandibular cortical width (MCW) may be used as a surrogate measure for evaluating BMD. We compared the MCW measured on digital panoramic radiographs (DPR) of 38 children and adolescents with ADHD who were treated with methylphenidate for at least 12 months to the MCW of 58 children and adolescents without ADHD (control). The two groups had a similar mean age (p = 0.3). Mean MCW was significantly lower among children with ADHD compared to those in the control group (2.77 ± 0.33 mm vs. 3.04 ± 0.46 mm, p = 0.004). Additionally, each of the MCW sides were significantly smaller in the group with ADHD compared with the control group. In conclusion, treatment with methylphenidate is associated with low MCW in children and adolescents with ADHD. Analysis of MCW on DPR may help in screening children that are at risk of bone health alterations that may result in low BMD in adulthood. Dentists may be the first to identify bone health abnormalities and should be aware of their role in referring their patients to further follow-up.

Trifid Mandibular Condyle: Case Report and Current Review of the Literature.

Trifid mandibular condyle (TMC) is a rare anatomical variation characterized by the duplication of the mandibular condyle. The aim of this study is to report a new case of a 26-year-old female patient with a left TMC and to review the current existing literature on TMC, the relevant cases, etiology, symptoms and different treatment modalities. The database engines PubMed, EMBASE, Scopus, Web of science, Scientific Electronic Library Online, Cochrane and CINAHL were searched for TMC cases from inception until April of 2022. Only 13 previous cases of TMC were found. Although it is a rare anatomical entity, TMC is increasingly being detected due to more advanced imaging techniques, especially computed tomography (CT), cone beam CT (CBCT) and magnetic resonance imaging (MRI) emerging in the field of dentistry. The etiology and pathogenesis of TMC and its relationship with TMD are still unclear. Further studies and follow-up may help to better understand this anatomic variant and possible interactions with local pathologies.

Coronally Advanced Flap with Connective Tissue Graft for Treating Orthodontic-Associated Miller Class III Gingival Recession of the Lower Incisors: A One-Year Retrospective Study.

(1) Background: To assess the clinical outcome of coronally advanced flap combined with connective tissue graft for the treatment of orthodontic-associated Miller Class III gingival recession of the lower incisors. (2) Methods: This study included 15 patients who had undergone orthodontic treatment prior to development of recession. Measurements of recession depth, recession width, probing depth, and width of keratinized tissue were performed clinically immediately before surgery and after one year. In addition, digital measurements of recession depth, recession width, and root coverage esthetic score were performed on intraoral photographs. (3) Results: Significant reduction was observed for probing depth, recession depth, and recession width at one year, with significant increase in width of keratinized tissue. Mean root coverage was 83 ± 24% for recession depth, while complete root coverage was achieved in 10 out of 21 recessions (48%). The average root coverage esthetic score at 12 months was 7.1 ± 2.6. An interaction was found between initial recession depth and mean root coverage. (4) Conclusions: Within the limitations of this study, our results confirm that combination of coronally advanced flap and connective tissue graft is effective in reducing post-orthodontic Miller Class III recessions of the mandibular incisors, even when the correction of the tooth malposition, is unattainable.

Conceptual changes in ameloblastoma: Suggested re-classification of a "veteran" tumor.

OBJECTIVES: The merging of ameloblastoma (AM) with mural unicystic ameloblastoma (UAM-M) was suggested by the 2017 WHO based on similar treatment needs. In an international multicenter study, we investigated the characteristics of their merged product (merged-AM) and raised the possibility of unifying AM and UAM (total-AM). MATERIALS AND METHODS: AM and UAM (luminal/intraluminal/mural), separate and combined, were analyzed for demographic/clinical/radiological features. ANOVA and chi-square tests were followed by univariate and multivariate analyses, and significance was set at p < .05. RESULTS: The patients' mean age was 39.6 ± 20.3 years in merged-AM (147 AM, 76 UAM-M), 45.1 ± 19.4 years in AM (p = .009). Merged-AM comprised 51.3% multilocular/48.7% unilocular tumors, AM comprised 72.5%/27.5%, respectively (p < .001). Merged-AM was associated with impacted teeth in 30.8%, AM in 18% (p = .023). The probability of merged-AM for multilocularity increased by 2.4% per year of age (95%CI 0.6-4.2, p = .009). Association with impacted teeth decreased by 7.9% per year of age (95%CI 1.9-14.39, p = .009). Merged-AM did not differ from total-AM (p > .05). CONCLUSIONS: Merged-AM partially differed from AM, but differences appeared to diminish in an age/time-wise manner. Merged-AM and total-AM were nearly indistinguishable. Therefore, AM and UAM may be considered a continuous spectrum of one type of tumor, further necessitating revision of the treatment approaches.

Transfer Deep Learning for Dental and Maxillofacial Imaging Modality Classification: A Preliminary Study.

OBJECTIVE: To apply the technique of transfer deep learning on a small data set for automatic classification of X-ray modalities in dentistry. STUDY DESIGN: For solving the problem of classification, the convolution neural networks based on VGG16, NASNetLarge and Xception architectures were used, which received pre-training on ImageNet subset. In this research, we used an in-house dataset created within the School of Dental Medicine, Tel Aviv University. The training dataset contained anonymized 496 digital Panoramic and Cephalometric X-ray images for orthodontic examinations from CS 8100 Digital Panoramic System (Carestream Dental LLC, Atlanta, USA). The models were trained using NVIDIA GeForce GTX 1080 Ti GPU. The study was approved by the ethical committee of Tel Aviv University. RESULTS: The test dataset contained 124 X-ray images from 2 different devices: CS 8100 Digital Panoramic System and Planmeca ProMax 2D (Planmeca, Helsinki, Finland). X-ray images in the test database were not pre-processed. The accuracy of all neural network architectures was 100%. Following a result of almost absolute accuracy, the other statistical metrics were not relevant. CONCLUSIONS: In this study, good results have been obtained for the automatic classification of different modalities of X-ray images used in dentistry. The most promising direction for the development of this kind of application is the transfer deep learning. Further studies on automatic classification of modalities, as well as sub-modalities, can maximally reduce occasional difficulties arising in this field in the daily practice of the dentist and, eventually, improve the quality of diagnosis and treatment.

Automatic detection of image sharpening in maxillofacial radiology.

BACKGROUND: Improvement of image quality in radiology, including the maxillofacial region, is important for diagnosis by enhancing the visual perception of the original image. One of the most used modification methods is sharpening, in which simultaneously with the improvement, due to edge enhancement, several artifacts appear. These might lead to misdiagnosis and, as a consequence, to improper treatment. The purpose of this study was to prove the feasibility and effectiveness of automatic sharpening detection based on neural networks. METHODS: The in-house created dataset contained 4290 X-ray slices from different datasets of cone beam computed tomography images were taken on 2 different devices: Ortophos 3D SL (Sirona Dental Systems GmbH, Bensheim, Germany) and Planmeca ProMax 3D (Planmeca, Helsinki, Finland). The selected slices were modified using the sharpening filter available in the software RadiAnt Dicom Viewer software (Medixant, Poland), version 5.5. The neural network known as "ResNet-50" was used, which has been previously trained on the ImageNet dataset. The input images and their corresponding sharpening maps were used to train the network. For the implementation, Keras with Tensorflow backend was used. The model was trained using NVIDIA GeForce GTX 1080 Ti GPU. Receiver Operating Characteristic (ROC) analysis was performed to calculate the detection accuracy using MedCalc Statistical Software version 14.8.1 (MedCalc Software Ltd, Ostend, Belgium). The study was approved by the Ethical Committee. RESULTS: For the test, 1200 different images with the filter and without modification were used. An analysis of the detection of three different levels of sharpening (1, 2, 3) showed sensitivity of 53%, 93.33%, 93% and specificity of 72.33%, 84%, 85.33%, respectively with an accuracy of 62.17%, 88.67% and 89% (p < 0.0001). The ROC analysis in all tests showed an Area Under Curve (AUC) different from 0.5 (null hypothesis). CONCLUSIONS: This study showed a high performance in automatic sharpening detection of radiological images based on neural network technology. Further investigation of these capabilities, including their application to different types of radiological images, will significantly improve the level of diagnosis and appropriate treatment.

Visual Examination, Fluorescence-Aided Caries Excavation (FACE) Technology, Bitewing X-Ray Radiography in the Detection of Occlusal Caries in First Permanent Molars in Children.

OBJECTIVES: To compare the effectiveness of visual examination, radiographic examination and fluorescence-aided caries excavation (FACE) in detecting occlusal caries in first permanent molars in 150 children aged 6-14 years with intact occlusal surface with caries lesions without cavitation, or with darkened or deep fissures that had no clear diagnosis. STUDY DESIGN: Two dentists independently performed a visual oral examination, FACE and bitewing radiography. The inter-rater reliability of each detection method was determined and their specificity and sensitivity. RESULTS: All caries detection methods showed high inter-rater reliability with absolute agreement between raters above 90%. Most caries lesions were detected by visual (75.8%) and FACE (79.1%), while only 28.8% of lesions were detected by radiography. Detection by visual examination was strongly correlated with detection by FACE (X2=37.9, Phi=0.498, p<0.001). A lower, yet statistically significant, correlation was found between visual examination and X-ray radiography (X2=5.53, Phi=0.190, p<0.001). FACE had higher sensitivity (87%) and specificity (65%) for detecting occlusal caries in comparison with radiography (60% specificity and 55% sensitivity). CONCLUSION: Although visual examination remains the best method to detect occlusal caries in young permanent molars in children, FACE is an effective and accurate diagnostic tool that may aid in detection and treatment decisions.

Neural Network Detection and Segmentation of Mental Foramen in Panoramic Imaging.

Objective: To apply the technique of deep learning on a small dataset of panoramic images for the detection and segmentation of the mental foramen (MF). Study design: In this study we used in-house dataset created within the School of Dental Medicine, Tel Aviv University. The dataset contained randomly chosen and anonymized 112 digital panoramic X-ray images and corresponding segmentations of MF. In order to solve the task of segmentation of the MF we used a single fully convolution neural network, that was based on U-net as well as a cascade architecture. 70% of the data were randomly chosen for training, 15% for validation and accuracy was tested on 15%. The model was trained using NVIDIA GeForce GTX 1080 GPU. The SPSS software, version 17.0 (Chicago, IL, USA) was used for the statistical analysis. The study was approved by the ethical committee of Tel Aviv University. Results: The best results of the dice similarity coefficient ( DSC), precision, recall, MF-wise true positive rate (MFTPR) and MF-wise false positive rate (MFFPR) in single networks were 49.51%, 71.13%, 68.24%, 87.81% and 14.08%, respectively. The cascade of networks has shown better results than simple networks in recall and MFTPR, which were 88.83%, 93.75%, respectively, while DSC and precision achieved the lowest values, 31.77% and 23.92%, respectively. Conclusions: Currently, the U-net, one of the most used neural network architectures for biomedical application, was effectively used in this study. Methods based on deep learning are extremely important for automatic detection and segmentation in radiology and require further development.

Oral lichen planus and thyroid gland diseases: possible associations.

BACKGROUND: Lichen planus (LP) is a chronic inflammatory mucocutaneous disease that commonly affects the oral cavity. Previous reports have suggested a possible association between LP and thyroid gland diseases (TGDs). The purpose of this study was to investigate possible associations between oral lichen planus (OLP) and TGDs. METHODS: Patients diagnosed with OLP, both clinically and histopathologically (N = 102), were classified according to clinical course (symptomatic/asymptomatic), type (reticular/plaque, atrophic and erosive) and location of lesions. Data on TGDs was compared to age- and gender-matched controls (N = 102) without OLP. Diagnosis of any type of TGD and related medication for study and control groups was recorded from the medical files provided by patients' physicians. Statistical analysis used Student's t-test and Fisher's exact test; significance was set at p < 0.05. RESULTS: TGDs (all), hypothyroidism and related medications were found in 16.6, 12.7 and 12.7% of patients with OLP, respectively. These findings were similar to the control group: TGDs (all) -15.7%, hypothyroidism - 9.8% and thyroid gland disease-related medication - 9.8% (p > 0.05). No significant associations were found between different characteristics of OLP and hypothyroidism or other TGD (p > 0.05). CONCLUSIONS: We found no significant associations between the co-existence of OLP and TGD or related-medications. Our findings are in agreement with some of the previously published similar studies but in controversy with others. Further well-designed, multicenter studies with large groups of patients and controls may help to establish the nature of the associations between OLP and TGDs.

Atherosclerotic carotid plaque on panoramic radiographs: neural network detection.

AIM: Atherosclerotic carotid plaques (ACPs) constitute the main etiological factor in about 15% of strokes. ACPs can be detected on routine dental panoramic radiographs. As these are one of the most commonly performed dental images, they can be used as a source of available data for computerized methods of automatic detection of ACPs in order to significantly increase their timely diagnosis. The aim of this study was to present the potential of applying deep learning methodology to detect ACPs on routine panoramic radiographs with the ultimate goal of preventing strokes. METHODS: The Faster Region-based Convolutional Neural Network (Faster R-CNN) for deep learning was used. The operation of the algorithm was assessed on a small dataset of 65 panoramic images. As the available training data was limited, data augmentation was performed by changing the brightness and randomly flipping and rotating cropped regions of interest in multiple angles. Receiver operating characteristic (ROC) analysis was performed to calculate the accuracy of detection. RESULTS: ACPs were detected with a sensitivity of 75%, a specificity of 80%, and an accuracy of 83%. The ROC analysis showed a significant area under curve (AUC), different from 0.5. CONCLUSIONS: The novelty of the study lies in showing the efficiency of a deep learning method for the detection of ACPs on routine panoramic images based on a small dataset. Further improvement is needed as regards the application of the algorithm to the level of introducing this methodology in routine dental practice for stroke prevention.