Smile Aesthetic Evaluation on Videographs: An Intra-Rater and Inter-Rater Agreement Study.

The aim of this study was to assess on videographs the intra- and inter-rater reproducibility of the Smile Esthetic Index (SEI) that has been previously validated on photographs. Smile videographs were obtained using a smartphone associated with the Smile Lite MDP mounted on a tripod. They were then randomized and evaluated twice consecutively at a 1-week interval by three periodontists according to the SEI based on 10 variables. Cohen's Kappa and Fleiss' Kappa tests were performed to measure intra- and inter-rater agreement. Sixty-five smile videographs of 24 men and 41 women (mean age 33 ± 11.3 years) were scored. A mean intra-rater agreement of 0.68 (0.64-0.73) was obtained, representing substantial agreement. The inter-rater agreement calculated for each variable ranged from 0.31 for the variable "absence of visible excessive gingiva" to 0.90 for the variable "absence of diastema and/or missing inter-dental papilla." Within the limits of this study, we have demonstrated that it was possible to use videographs to reproducibly evaluate an aesthetic score (SEI) previously validated on photographs.

Relationship between internet research data of oral neoplasms and public health programs in the European Union.

BACKGROUND: Tobacco and alcohol are the main risk factors for oral squamous cell carcinoma, the low survival rate of which is a public health problem. European-wide health policies (a prevention campaign, tobacco packaging) have been put in place to inform the population of the risks associated with consumption. Due to the increase in smoking among women, the incidence of this disease remains high. The identification of internet research data on the population could help to measure the impact of and better position these preventive measures. The objective was to analyze a potential temporal association between public health programs and interest in oral cancers on the internet in the European Union (EU). METHODS: A search of data from Google ©, Wikipedia © and Twitter © users in 28 European countries relating to oral cancer between 2004 and 2019 was completed. Bibliometric analysis of press and scientific articles over the same period was also performed. The association between these data and the introduction of public health programs in Europe was studied. RESULTS: There was a temporal association between changes in tobacco packaging and a significant increase in internet searches for oral cancer in seven countries. Unlike national policies and ad campaigns, the European awareness program Make Sense has had no influence on internet research. There was an asymmetric correlation in internet searches between publications on oral cancer from scientific articles or "traditional" media (weak association) and those from internet media such as Twitter © or Wikipedia © (strong association). CONCLUSION: Our work highlights seven areas around which oral cancer awareness in Europe could be refocused, such as a change in the communication of health warnings on cigarette packs, the establishment of a more explicit campaign name regarding oral cancer, the involvement of public figures and associations in initiatives to be organized at the local level and the strengthening of awareness of the dangers of tobacco in the development of oral cancer.

Development of an Artificial Intelligence Model to Identify a Dental Implant from a Radiograph.

PURPOSE: The objective of this study was to develop a deep convolutional neural network (CNN) that would identify the brand and model of a dental implant from a radiograph. MATERIALS AND METHODS: A data augmentation procedure provided a total of 1,206 dental implant radiographic images of three different brands for six models (Nobel Biocare NobelActive [NNA] and Br.nemark System [NBS], Straumann Bone Level [SBL] and Tissue Level [STL], and Zimmer Biomet Dental Tapered Screw-Vent [ZTSV] and SwissPlus [ZSP]). They were divided into a test group (n = 241; 19.9%) and a training and validation group (n = 965; 80%). Preprocessing and transfer learning were applied to a pretrained GoogLeNet Inception CNN network. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) of the CNN model were evaluated. RESULTS: The diagnostic accuracy was 93.8% (95% CI: 87.2% to 99.4%), the sensitivity was 93.5% (95% CI: 84.2% to 99.3%), the specificity was 94.2% (95% CI: 83.5% to 99.4%), the positive predictive value was 92% (95% CI: 83.9% to 97.2%), and the negative predictive value was 91.5% (95% CI: 80.2% to 97.1%). The deep CNN algorithm achieved an AUC of 0.918 (95% CI: 0.826 to 0.973) on NNA, 0.922 (95% CI: 0.831 to 0.964) on NBS, 0.909 (95% CI: 0.844 to 0.982) on SBL, 0.890 (95% CI: 0.783 to 0.945) on STL, 0.931 (95% CI: 0.867 to 0.979) on ZTSV, and 0.911 (95% CI: 0.811 to 0.957) on ZSP. CONCLUSION: The deep CNN model had a very good performance in identifying a dental implant from a radiograph. A huge and varied database of radiographs would have to be built up to be able to identify any dental implant.

Stensen duct dilation: Case series of minimally invasive treatment.

Stensen's duct dilation is a rare condition characterized by a global or partial idiopathic dilation of the Stensen duct. Affected individuals usually show either aesthetically compromised features, such as a tubular-shaped swelling in the cheek, or with mildly painful inflammatory episodes. Three women between ages 61 and 67 years were diagnosed with Stensen duct dilation after sialo-magnetic resonance imaging (MRI). They were treated with botulinum toxin A (BTX-A). Our preliminary results suggest that BTX-A was efficient as a suspensive treatment. BTX-A should remain a first-line treatment of the early silent symptoms of Stensen duct dilation, such as swelling and aesthetic issues. The use of BTX-A avoids more serious procedures and further complications.