Preclinical motor chunking and fine motor skill learning in fixed prosthodontics: Contribution of 3D printing and satisfaction of dental students.

INTRODUCTION: In fixed prosthodontics, simulators are essential to students for a progressive transition from preclinical to clinical condition. With the 3D printing technology, we developed resin bars allowing students to better visualise by motor chunking technique. Main objectives of this work were to describe this teaching methodology used in preclinic among different promotions of second, third and fourth dental years and to evaluate students' feedback. MATERIALS AND METHODS: Two hundred seventy resin strips were digitally designed and printed in resin. All participants from second, third and fourth had to fulfil a User Experience Questionnaire (UEQ) after the preclinical work. The scales of this questionnaire covered the complete impression of the user experience. Both classical aspects of usability (efficiency, insight and reliability) and aspects of user experience (originality, stimulation) were measured. RESULTS: For the second dental years, 'Attractiveness', 'Stimulation' and 'Novelty' were considered 'Excellent'. For the third dental year, novelty average was considered as 'Excellent'. For the fourth dental year, 'novelty' was considered as 'Good'. DISCUSSION: The resin plates used in this study are original and stimulating for the students, especially for the second-year dental students who found the exercises useful for their learning. This method can also be used by creating scenarios close to the clinical situations encountered in dentistry departments (more dilapidated teeth, preparation of inlays, post and core, etc.). This 3D printed simulation model is not intended to replace the Frasaco® models but is a complement to the learning process.

Comparison of different machine learning approaches to predict dental age using Demirjian's staging approach.

CONTEXT: Dental age, one of the indicators of biological age, is inferred by radiological methods. Two of the most commonly used methods are using Demirjian's radiographic stages of permanent teeth excluding the third molar (Demirjian's and Willems' method). The major drawbacks of these methods are that they are based on population-specific conversion tables and may tend to over- or underestimate dental age in other populations. Machine learning (ML) methods make it possible to create complex data schemas more simply while keeping the same annotation system. The objectives of this study are to compare (1) the capacity of ten machine learning algorithms to predict dental age in children using the seven left permanent mandibular teeth compared to reference methods and (2) the capacity of ten machine learning algorithms to predict dental age from childhood to young adulthood using the seven left permanent mandibular teeth and the four third molars. METHODS: Using a large radiological database of 3605 orthopantomograms (1734 females and 1871 males) of healthy French patients aged between 2 and 24 years, seven left permanent mandibular teeth and the 4 third molars were assessed using Demirjian's stages. Dental age estimation was then performed using Demirjian's reference method and various ML regression methods. Two analyses were performed: with the 7 left mandibular teeth without third molars for the under 16 age group and with the third molars for the entire study population. The different methods were compared using mean error, mean absolute error, root mean square error as metrics, and the Bland-Altman graph. RESULTS: All ML methods had a mean absolute error (MAE) under 0.811 years. With Demirjian's and Willems' methods, the MAE was 1.107 and 0.927 years, respectively. Except for the Bayesian ridge regression that gives poorer accuracy, there was no statistical difference between all ML tested. CONCLUSION: Compared to the two reference methods, all the ML methods based on the maturation stages defined by Demirjian were more accurate in estimating dental age. These results support the use of ML algorithms instead of using standard population tables.

Volume of unsupported peri-implant soft tissue over time: A cross-sectional observation study.

STATEMENT OF PROBLEM: Supraimplant emergence profile changes have revealed that, when not supported, peri-implant soft tissue collapses. However, this phenomenon has not been described qualitatively and quantitatively in 3 dimensions. PURPOSE: The purpose of this cross-sectional clinical study was to analyze changes in the supraimplant emergence profile digitally over time when the peri-implant soft tissue was not supported. MATERIAL AND METHODS: A data set consisting of 16 participants with 16 implants was used. After removal of the healing abutment or the interim restoration, 4 intraoral optical impressions were made successively on the patient to record the supraimplant emergence profile at 4 different times: 0 second (T0), 30 seconds (T0.5 min), 2 minutes (T2 min), and 5 minutes (T5 min). The supraimplant emergence profile surfaces obtained were closed with a 3D meshing software program (Meshmixer) to obtain the volume of the supraimplant emergence profile at each time. The reference volume at T0 (Vol T0) corresponded to the volume created during the mucosal healing phase, which, theoretically, should be preserved for the fabrication of the definitive crown. The different volumes (Vol T0.5 min, Vol T2 min, and Vol T5 min) were compared with Vol T0 to analyze the volumetric changes, kinetics, and topography of this variation by using a 3D visualization and analysis software program (Avizo). A nonparametric Wilcoxon test for paired samples was used for comparison. RESULTS: The supraimplant emergence profile volume decreased by an average of 5% at 30 seconds (P<.001), 10% at 2 minutes (P<.001), and 14% at 5 minutes (P<.001). CONCLUSIONS: The supraimplant emergence profile collapsed significantly in 3D when unsupported for 30 seconds or more.

Incidental Findings in Dental Radiology are Concerning for Family Doctors.

Cone Beam Computerized Tomography (CBCT) is an imaging technology increasingly used in dentistry. Depending on the size of the examination area, visualization of anatomical structures outside the indication area may reveal incidental findings (IF). The aims of this systematic review and meta-analysis were to 1) evaluate the frequency, location and different types of incidental findings (IF) revealed during CBCT examinations; 2) identify potential influencing factors such as gender or age; 3) highlight what the family doctor should know about CBCT and the benefits for medical care. 70 retrospective studies were included. 60% of IF are in the naso-oropharyngeal airway and paranasal sinuses. Carotid calcifications were observed with a mean prevalence of 9% CI95% [2-21]). Meta-regression showed a significant association of this prevalence with age, irrespective of gender. Given the high frequency of IF, with varying severity, the whole medical community is fully involved, and its opinion should be sought to ensure the best possible management for the patients. Physicians may also require CBCT examinations that would have been previously prescribed by a dentist, that may serve to better orientate investigations toward another imaging technique. The family doctor is therefore the dentist's main interlocutor and the main coordinator of the follow-up of IF.

Assessment of automatic segmentation of teeth using a watershed-based method.

OBJECTIVE: Tooth 3D automatic segmentation (AS) is being actively developed in research and clinical fields. Here, we assess the effect of automatic segmentation using a watershed-based method on the accuracy and reproducibility of 3D reconstructions in volumetric measurements by comparing it with a semi-automatic segmentation(SAS) method that has already been validated. METHODS: The study sample comprised 52 teeth, scanned with micro-CT (41 µm voxel size) and CBCT (76; 200 and 300 µm voxel size). Each tooth was segmented by AS based on a watershed method and by SAS. For all surface reconstructions, volumetric measurements were obtained and analysed statistically. Surfaces were then aligned using the SAS surfaces as the reference. The topography of the geometric discrepancies was displayed by using a colour map allowing the maximum differences to be located. RESULTS: AS reconstructions showed similar tooth volumes when compared with SAS for the 41 µm voxel size. A difference in volumes was observed, and increased with the voxel size for CBCT data. The maximum differences were mainly found at the cervical margins and incisal edges but the general form was preserved. CONCLUSION: Micro-CT, a modality used in dental research, provides data that can be segmented automatically, which is timesaving. AS with CBCT data enables the general form of the region of interest to be displayed. However, our AS method can still be used for metrically reliable measurements in the field of clinical dentistry if some manual refinements are applied.

Comparison of the accuracy of 3-dimensional cone-beam computed tomography and micro-computed tomography reconstructions by using different voxel sizes.

INTRODUCTION: Cone-beam computed tomography (CBCT) data are, in principle, metrically exact. However, clinicians need to consider the precision of measurements of dental morphology as well as other hard tissue structures. CBCT spatial resolution, and thus image reconstruction quality, is restricted by the acquisition voxel size. The aim of this study was to assess geometric discrepancies among 3-dimensional CBCT reconstructions relative to the micro-CT reference. METHODS: A total of 37 permanent teeth from 9 mandibles were scanned with CBCT 9500 and 9000 3D and micro-CT. After semiautomatic segmentation, reconstructions were obtained from CBCT acquisitions (voxel sizes 76, 200, and 300 µm) and from micro-CT (voxel size 41 µm). All reconstructions were positioned in the same plane by image registration. The topography of the geometric discrepancies was displayed by using a color map allowing the maximum differences to be located. RESULTS: The maximum differences were mainly found at the cervical margins and on the cusp tips or incisal edges. Geometric reconstruction discrepancies were significant at 300-µm resolution (P = .01, Wilcoxon test). CONCLUSIONS: To study hard tissue morphology, CBCT acquisitions require voxel sizes smaller than 300 µm. This experimental study will have to be complemented by studies in vivo that consider the conditions of clinical practice.