Median Lingual Foramen, a new midmandibular cephalometric landmark.

PURPOSE: In asymmetrical mandibles, it is often challenging to identify the mandibular midline. The median lingual foramen (MLF) is located at the midline of the anterior mandible. The purpose of this study is to evaluate the reproducibility of identifying the MLF compared to conventional landmarks on cone beam computed tomography's (CBCT's) to mark the mandibular midline. MATERIAL AND METHODS: Ten symmetrical class II, 10 symmetrical class III, ten asymmetrical class II and 10 asymmetrical class III patients were included. On CBCTs, the cephalometric landmarks menton, pogonion, genial tubercle and MLF were identified twice by two observers. RESULTS: A high intra- and interobserver reproducibility was found for all landmarks, the highest being the MLF. The gain in accuracy is 0.998 mm, 0.824 mm and 0.361 mm compared to pogonion, genial tubercle and menton, respectively (P-value <.05). CONCLUSION: MLF is a reliable and reproducible landmark to indicate the midline of the mandible, particularly in Class II asymmetric mandibles.

Classification of caries in third molars on panoramic radiographs using deep learning.

The objective of this study is to assess the classification accuracy of dental caries on panoramic radiographs using deep-learning algorithms. A convolutional neural network (CNN) was trained on a reference data set consisted of 400 cropped panoramic images in the classification of carious lesions in mandibular and maxillary third molars, based on the CNN MobileNet V2. For this pilot study, the trained MobileNet V2 was applied on a test set consisting of 100 cropped PR(s). The classification accuracy and the area-under-the-curve (AUC) were calculated. The proposed method achieved an accuracy of 0.87, a sensitivity of 0.86, a specificity of 0.88 and an AUC of 0.90 for the classification of carious lesions of third molars on PR(s). A high accuracy was achieved in caries classification in third molars based on the MobileNet V2 algorithm as presented. This is beneficial for the further development of a deep-learning based automated third molar removal assessment in future.

Patient-reported Outcome Measures: The FACE-Q Skin Cancer Module: The Dutch Translation and Linguistic Validation.

BACKGROUND: Patient-reported outcome measures are becoming a standard component in the evaluation of surgical treatments. In 2010, the FACE-Q skin cancer module was developed: an English psychometric validated questionnaire that measures both patient quality of life and satisfaction with the surgical experience. The questionnaire consists of 11 subscales with a total of 96 questions. An officially translated version in Dutch is needed for accepted use in the Netherlands. METHODS: We translated the FACE-Q skin cancer module from English into Dutch in accordance with to the International Society for Pharmacoeconomics and Outcomes Research and World Health Organization guidelines. The translation occurs in three stages. First, a forward translation is performed by two independent professional translators, where discrepancies are solved by a third translator, a subject area expert. Secondly, a backward translation is performed and is compared with the original. Any discrepancies are solved by an expert panel. Version two is then pretested (cognitive debriefing) by 30 patients who have had a resection (Mohs surgery) of non-melanoma skin cancer in the face followed by reconstruction. The results of the pretesting exercise are evaluated and a final version of the translation was produced by the expert panel. RESULTS: In the first step, a conceptually equivalent Dutch translation of the FACE-Q was translated. In the second phase, the comparison between the forward and backward translation led to multiple retranslations. In step three, 48 annotations were evaluated by the expert panel, which led to 26 minor changes in items or instructions. CONCLUSION: We created a conceptually and linguistically similar translation of the FACE-Q Skin Cancer Module through a thorough translation and linguistic validation process.