Pain during primary molar local anaesthesia with SleeperOne5 computerized device versus conventional syringe: A randomized, split-mouth, crossover, controlled trial.

BACKGROUND: Because of controversial results from clinical studies comparing different dental local anesthesia methods in children, the primary objective of this randomized, split-mouth, crossover, controlled trial was to compare pain intensity during local anaesthesia (LA) performed with a computer-controlled LA delivery system (C-CLADS) versus a conventional syringe (CONV). Secondary objectives included comparisons during dental treatment. METHODS: Participants (4-8 years) with tooth pair requiring similar treatment were recruited from five French hospitals. The right primary molar, which was treated at the first visit, was randomly allocated to one of the anaesthesia groups (either intraosseous with C-CLADS or infiltration with CONV), whereas the contralateral molar (treated at the second visit) was assigned to the other group. Pain intensity and behaviour outcomes, assessed with the Faces Pain and Venham revised scales, respectively, were compared between groups using Proc mixed. Stratified analyses were performed on dentition and location. RESULTS: Among 107 participants, the analysis revealed reduced pain perception during LA in the C-CLADS group compared with the CONV group (-0.72, 95% CI: -1.43, -0.006), but not during dental treatment. Stratified analyses showed that this effect was observed only in primary dentition (p = .006) and mandibular molars (p = .005). Behavioural issues were fewer in the C-CLADS group than in the CONV group (p = .05) only during injection. CONCLUSION: C-CLADS emerged as the preferable system in primary dentition.

Improving radiographic diagnosis of pulpo-periodontal complications in primary molars by training: Application in education and clinical research.

INTRODUCTION: The objective of this study was to assess an original learning intervention to train students and paediatric dentistry teachers in radiographic diagnostic accuracy of pulpo-periodontal complications in primary molars. MATERIALS AND METHODS: The learning intervention was based on 250 different randomly ordered radiographs of primary molars within three quizzes (A, B and C) for 5 sessions (S): quiz A (50 X-rays), B and C (100 X-rays) were, respectively, completed in S1 to assess the extent of agreement with 5 experts' diagnoses, in S2 and S3 (B at days 8 and 23) and in S4 and S5 (C at days 90 and 105). During S1 and at the end of S3 and S5, the participants (48 students and 16 teachers) were informed of correct diagnoses. A satisfaction questionnaire was completed by all the students. Alongside the descriptive analyses, generalised linear mixed model (GLMM) analyses assessed the odds of participants' correct diagnosis over the study duration. RESULTS: At S1, the odds of diagnostic accuracy among students were significantly lower than those among the teachers. After receiving feedback at S1, GLMM analyses showed that among all the participants, accuracy improved over time with the odds of correct diagnoses higher in S2-5 than in S1; and there were similar increases across sessions between teachers and students, except in S3, where the improvement among teachers tended to be greater than that among the students. All students were satisfied though one-third reported that quizzes with 100 radiographs felt too long. CONCLUSION: The online case-based learning was a good training format for dental education.

Impact of haptic simulators in preclinical dental education: A systematic review.

BACKGROUND: Haptic technologies have opened a new avenue in preclinical dental education, with evidence that they can be used to improve student performance. The aim of this systematic review was to (1) determine the effect of haptic simulators on motor skill acquisition during preclinical dental training, (2) explore students' perception, and (3) explore the ability of haptic systems to distinguish users based on their initial level of manual dexterity. METHODS: A comprehensive search of articles published up to February 2023 was performed using five databases (i.e., PubMed/Medline, ScienceDirect, Web of Sciences, Scopus, and Cochrane Library) and specialized journals. The Preferred Reporting Items for Systematic Review and Meta-Analysis 2020 guidelines were followed, and the risk of bias was assessed. Only studies on the application of haptic simulators in dentistry preclinical training were included. Qualitative synthesis of data was performed, and the protocol was registered in PROSPERO (ID = CRD42022337177). RESULTS: Twenty-three clinical studies, including 1303 participants, were included. The authors observed a statistically significant improvement in dental students' motor skills in various dental specialties, such as restorative dentistry, pediatric, prosthodontics, periodontics, implantology, and dental surgery, after haptic training. Haptic technologies were perceived well by all participants, with encouraging data regarding their ability to differentiate users according to their initial level of manual dexterity. CONCLUSIONS: Our work suggests that haptic simulators can significantly improve motor skill acquisition in preclinical dental training. This new digital technology, which was well perceived by the participants, also showed encouraging results in discriminating users according to their level of experience.

Amelogenesis imperfecta: Next-generation sequencing sheds light on Witkop's classification.

Amelogenesis imperfecta (AI) is a heterogeneous group of genetic rare diseases disrupting enamel development (Smith et al., Front Physiol, 2017a, 8, 333). The clinical enamel phenotypes can be described as hypoplastic, hypomineralized or hypomature and serve as a basis, together with the mode of inheritance, to Witkop's classification (Witkop, J Oral Pathol, 1988, 17, 547-553). AI can be described in isolation or associated with others symptoms in syndromes. Its occurrence was estimated to range from 1/700 to 1/14,000. More than 70 genes have currently been identified as causative. Objectives: We analyzed using next-generation sequencing (NGS) a heterogeneous cohort of AI patients in order to determine the molecular etiology of AI and to improve diagnosis and disease management. Methods: Individuals presenting with so called "isolated" or syndromic AI were enrolled and examined at the Reference Centre for Rare Oral and Dental Diseases (O-Rares) using D4/phenodent protocol (www.phenodent.org). Families gave written informed consents for both phenotyping and molecular analysis and diagnosis using a dedicated NGS panel named GenoDENT. This panel explores currently simultaneously 567 genes. The study is registered under NCT01746121 and NCT02397824 (https://clinicaltrials.gov/). Results: GenoDENT obtained a 60% diagnostic rate. We reported genetics results for 221 persons divided between 115 AI index cases and their 106 associated relatives from a total of 111 families. From this index cohort, 73% were diagnosed with non-syndromic amelogenesis imperfecta and 27% with syndromic amelogenesis imperfecta. Each individual was classified according to the AI phenotype. Type I hypoplastic AI represented 61 individuals (53%), Type II hypomature AI affected 31 individuals (27%), Type III hypomineralized AI was diagnosed in 18 individuals (16%) and Type IV hypoplastic-hypomature AI with taurodontism concerned 5 individuals (4%). We validated the genetic diagnosis, with class 4 (likely pathogenic) or class 5 (pathogenic) variants, for 81% of the cohort, and identified candidate variants (variant of uncertain significance or VUS) for 19% of index cases. Among the 151 sequenced variants, 47 are newly reported and classified as class 4 or 5. The most frequently discovered genotypes were associated with MMP20 and FAM83H for isolated AI. FAM20A and LTBP3 genes were the most frequent genes identified for syndromic AI. Patients negative to the panel were resolved with exome sequencing elucidating for example the gene involved ie ACP4 or digenic inheritance. Conclusion: NGS GenoDENT panel is a validated and cost-efficient technique offering new perspectives to understand underlying molecular mechanisms of AI. Discovering variants in genes involved in syndromic AI (CNNM4, WDR72, FAM20A … ) transformed patient overall care. Unravelling the genetic basis of AI sheds light on Witkop's AI classification.