Osteochondroma of Mandibular Condyle

Osteochondroma is one of the most common benign bone tumor of axial skeleton. In oral and maxillofacial region, osteochondroma is rare. This tumor most often involves mandibularcoronoid process however the osteochondroma of mandibular condyle is extremely rare. The anterior and medial half of mandibular condyle is involved more than lateral and superior half. We report a case of osteochondroma of mandibular condyle in 27-year-old female with clinic-radiologic correlation

An Unusual Osseous Lesion of Mandibular Condyle

Peripheral osteomas are rare tumors of jaws arising from proliferation of cancellous or compact bone increasing in size by continuous bone formation. These lesions appears as unilateral, pedunculated or seesile mushroom-like masses, well-marginated and varying in diameter from 10 to 40 mm. The first case of peripheral osteoma involving condylar process was reported by Ivy in 1927. Since then only 13 cases of osteoma arising in condylar process have been reported in literature. Osteoma of condyle is relatively rare and it may cause a slow progressive shift in patient’s occlusion with deviation of the midline of the chin toward unaffected side. Here, a case of peripheral osteoma of mandibular condyle is presented

Computer Simulation and the Practice of Oral Medicine and Radiology

The practice of Oral Medicine and Radiology has long been considered an art form. Collecting and collimating the enormous amount of information each patient brings has always tested the best of our abilities as diagnosticians. However, as the tide of smartphones, cheaper data access, and automation rises, it threatens to wash away all that we have held sacrosanct about conventional clinical practices. In this tussle between what is traditional and what is tantalizing, it is time to question, as diagnosticians, how much can we accede to the invasion of algorithms. How does computer simulation affect the practice of diagnosis in the field of Oral Medicine and Radiology

Comparative Evaluation of role of Lysyl oxidase gene (LOXG473A) expression in pathogenesis and malignant transformation of Oral Submucous Fibrosis

Identification and comparison of gene expression of Lysyl oxidase (LOX) in oral submucous fibrosis and controls and to determine its role in Pathogenesis of Oral submucous fibrosis. Of total sample size (n=127), the whole blood sample were collected from case and control group in citrate vial. It is centrifused and stored at -800C. We collected and isolated RNA from blood of case group (n=127) and age and sex matched control group (n=127) recruited on the basis of inclusion criteria. The cDNA was prepared for 127 samples which were processed for gene expression of Lysyl oxidase (LOX) in relation to housekeeping genes (Beta actin and 18srRNA) and its role in pathogenesis of Oral submucous fibrosis. In relative expression (Normalized ratio),relatively 11 cases shown down-regulation of lysyl oxidase gene while 27 cases shows up-regulation of lysyl oxidase gene while in 89 cases there were no regulation i.e expression of lysyl oxidase gene in case group was of same degree of control. In non-relative expression results (Non-normalized ratio), the 38 cases shown down regulation of LOX gene while in 53 cases, it was up-regulated however in remaining 36 cases there was neither up-regulation nor down-regulation of Lysyl oxidase gene i.e the expression of LOX gene is null. In oral submucous fibrosis, the expression of Lysyl oxidase gene is mixed type i.e either it will down regulate/upregulate or there will be no expression at all comparatively. However in majority of cases the upregulation of lysyl oxidase is relatively more common than down-regulation or non expression of Lysyl oxidase gene

Mercury or Mercury Free Restorations in Oral Cavity

Amalgam is basically a concoction of metals that has been used as a potent filling material in dentistry for the last 150 years. Amalgam usually consists of silver, mercury, tin and copper. Dental amalgam is a material used to fill cavities of tooth. Over the years, amalgam has become a topic of concern because it contains mercury. Mercury is a naturally occurring metal in the environment. Mercury exists as a liquid in room temperature but when heated, it becomes a gas. Flexibility of amalgam as a filling material is due Mercury. An alloy powder, a compound that is soft in nature when mixed with mercury makes it enough to mix and condense into the tooth. It hardens quickly and offers strong resistance to the forces of biting and chewing. There are studies reported on the safety of amalgam fillings. In 2005, European Union launched a comprehensive mercury strategy to reduce use of mercury. In 2008, countries like Norway and Denmark restricted the use of dental amalgam containing mercury. In 2009, this research was evaluated by U.S. Food and Drug Administration (FDA) and found no rationale to limit the use of amalgam. There are certain restorative materials that are available commercially that are mercury free in nature like Gold, Porcelain, Gallium alloys, Composite resin restoratives etc. They offer many advantages over amalgams containing mercury like: seals the dentin from future decay, reinforces remaining tooth structure, provides smooth and bonded margins, conservative and it blends naturally

Mercury or Mercury Free Restorations in Oral Cavity

Amalgam is basically a concoction of metals that has been used as a potent filling material in dentistry for the last 150 years. Amalgam usually consists of silver, mercury, tin and copper. Dental amalgam is a material used to fill cavities of tooth. Over the years, amalgam has become a topic of concern because it contains mercury. Mercury is a naturally occurring metal in the environment. Mercury exists as a liquid in room temperature but when heated, it becomes a gas. Flexibility of amalgam as a filling material is due Mercury. An alloy powder, a compound that is soft in nature when mixed with mercury makes it enough to mix and condense into the tooth. It hardens quickly and offers strong resistance to the forces of biting and chewing. There are studies reported on the safety of amalgam fillings. In 2005, European Union launched a comprehensive mercury strategy to reduce use of mercury. In 2008, countries like Norway and Denmark restricted the use of dental amalgam containing mercury. In 2009, this research was evaluated by U.S. Food and Drug Administration (FDA) and found no rationale to limit the use of amalgam.  There are certain restorative materials that are available commercially that are mercury free in nature like Gold, Porcelain, Gallium alloys, Composite resin restoratives etc. They offer many advantages over amalgams containing mercury like: seals the dentin from future decay, reinforces remaining tooth structure, provides smooth and bonded margins, conservative and it blends naturally

Machine Learning in Dentistry: A Scoping Review

Machine learning (ML) is being increasingly employed in dental research and application. We aimed to systematically compile studies using ML in dentistry and assess their methodological quality, including the risk of bias and reporting standards. We evaluated studies employing ML in dentistry published from 1 January 2015 to 31 May 2021 on MEDLINE, IEEE Xplore, and arXiv. We assessed publication trends and the distribution of ML tasks (classification, object detection, semantic segmentation, instance segmentation, and generation) in different clinical fields. We appraised the risk of bias and adherence to reporting standards, using the QUADAS-2 and TRIPOD checklists, respectively. Out of 183 identified studies, 168 were included, focusing on various ML tasks and employing a broad range of ML models, input data, data sources, strategies to generate reference tests, and performance metrics. Classification tasks were most common. Forty-two different metrics were used to evaluate model performances, with accuracy, sensitivity, precision, and intersection-over-union being the most common. We observed considerable risk of bias and moderate adherence to reporting standards which hampers replication of results. A minimum (core) set of outcome and outcome metrics is necessary to facilitate comparisons across studies

Generalizability of deep learning models for dental image analysis

We assessed the generalizability of deep learning models and how to improve it. Our exemplary use-case was the detection of apical lesions on panoramic radiographs. We employed two datasets of panoramic radiographs from two centers, one in Germany (Charite, Berlin, n=650) and one in India (KGMU, Lucknow, n=650): First, U-Net type models were trained on images from Charite (n=500) and assessed on test sets from Charite and KGMU (each n=150). Second, the relevance of image characteristics was explored using pixel-value transformations, aligning the image characteristics in the datasets. Third, cross-center training effects on generalizability were evaluated by stepwise replacing Charite with KGMU images. Last, we assessed the impact of the dental status (presence of root-canal fillings or restorations). Models trained only on Charite images showed a (mean +/- SD) F1-score of 54.1 +/- 0.8% on Charite and 32.7 +/- 0.8% on KGMU data (p<0.001/t-test). Alignment of image data characteristics between the centers did not improve generalizability. However, by gradually increasing the fraction of KGMU images in the training set (from 0 to 100%) the F1-score on KGMU images improved (46.1 +/- 0.9%) at a moderate decrease on Charite images (50.9 +/- 0.9%, p<0.01). Model performance was good on KGMU images showing root-canal fillings and/or restorations, but much lower on KGMU images without root-canal fillings and/or restorations. Our deep learning models were not generalizable across centers. Cross-center training improved generalizability. Noteworthy, the dental status, but not image characteristics were relevant. Understanding the reasons behind limits in generalizability helps to mitigate generalizability problems

Deep learning for cephalometric landmark detection: systematic review and meta-analysis

Objectives: Deep learning (DL) has been increasingly employed for automated landmark detection, e.g., for cephalometric purposes. We performed a systematic review and meta-analysis to assess the accuracy and underlying evidence for DL for cephalometric landmark detection on 2-D and 3-D radiographs. Methods: Diagnostic accuracy studies published in 2015-2020 in Medline/Embase/IEEE/arXiv and employing DL for cephalometric landmark detection were identified and extracted by two independent reviewers. Random-effects meta-analysis, subgroup, and meta-regression were performed, and study quality was assessed using QUADAS-2. The review was registered (PROSPERO no. 227498). Data: From 321 identified records, 19 studies (published 2017-2020), all employing convolutional neural networks, mainly on 2-D lateral radiographs (n=15), using data from publicly available datasets (n=12) and testing the detection of a mean of 30 (SD: 25; range.: 7-93) landmarks, were included. The reference test was established by two experts (n=11), 1 expert (n=4), 3 experts (n=3), and a set of annotators (n=1). Risk of bias was high, and applicability concerns were detected for most studies, mainly regarding the data selection and reference test conduct. Landmark prediction error centered around a 2-mm error threshold (mean; 95% confidence interval: (-0.581; 95 CI: -1.264 to 0.102 mm)). The proportion of landmarks detected within this 2-mm threshold was 0.799 (0.770 to 0.824). Conclusions: DL shows relatively high accuracy for detecting landmarks on cephalometric imagery. The overall body of evidence is consistent but suffers from high risk of bias. Demonstrating robustness and generalizability of DL for landmark detection is needed. Clinical significance: Existing DL models show consistent and largely high accuracy for automated detection of cephalometric landmarks. The majority of studies so far focused on 2-D imagery; data on 3-D imagery are sparse, but promising. Future studies should focus on demonstrating generalizability, robustness, and clinical usefulness of DL for this objective

Molecular Signatures of Tumour and Its Microenvironment for Precise Quantitative Diagnosis of Oral Squamous Cell Carcinoma: An International Multi-Cohort Diagnostic Validation Study

Supplementary Materials: The following supporting information can be downloaded at: www.mdpi.com/xxx/s1, Table ST1 – qMIDSV2 Gene panel primer sequences; Figure S1 – qMIDSV1 vs qMIDSV2 384-well assay format and protocols; Figure S2. Individual target gene expression pattern in 1761 samples; Figure S3. Various statistical methods used for gene selection analysis on 1761 clinical samples; Figure S4. Diagnostic performance comparison between qMIDSV2 vs qMIDSV2* (with 4 less effective genes removed from the panel of 14 target genes of qMIDSV2); Figure S5. Effect of removing individual genes from the 14-target gene panel qMIDSV2 (qV2) on diagnostic test performance based on the UK patient cohort data