背景:能够进行早期龋齿识别的数字方法可以简化研究中的数据收集,并优化幼儿的牙科检查。口内扫描仪是用于在牙科中创建牙齿3D模型的设备,并正在迅速应用于临床工作流程。将荧光技术集成到扫描仪硬件中可以支持早期龋齿检测。然而,使用具有颜色和荧光特征的3D模型的龋齿检测方法的性能尚不清楚。
目的:本研究旨在评估视觉检查(VE)之间的诊断协议,在屏幕上评估具有和不具有荧光的近似自然颜色的3D模型,并将自动龋齿评分系统应用于具有荧光的3D模型,以检测乳牙中的龋齿。
方法:研究样本将来自皇家儿童医院一项随机对照试验的合格参与者,墨尔本,澳大利亚,进行牙科评估的地方,包括使用国际龋齿检测和评估系统(ICDAS)的VE和使用TRIOS4(3ShapeTRIOSA/S)的口内扫描。将收集参与者的临床记录,所有符合资格标准的记录将由4名牙科医生在屏幕上评估3D模型。首先,将根据3D几何形状和颜色检查所有主要牙齿表面的龋齿,使用合并的ICDAS索引。第二,3D模型的屏幕上评估将包括荧光,其中龋齿将使用合并的ICDAS指数进行分类,该指数已被修改以纳入荧光标准。4周后,所有审查员将对所有3D模型重复屏幕上的评估。最后,自动龋齿评分系统将用于对主要咬合表面的龋齿进行分类。将使用Bland-Altman分析和组内相关系数来评估方法之间每人检测到的龋齿总数的一致性。在牙齿表面水平,方法之间的协议将使用多层次模型来估计,以说明牙科数据的聚类。
结果:3D模型的自动龋齿评分已于2023年10月完成,预计将于2024年7月发布结果。屏幕上的评估已经开始,预计到2024年3月完成评分和数据分析。结果将于2024年底发布。
结论:研究结果可能为使用数字模型促进牙科评估的新实践提供信息。能够在不损害VE准确性的情况下进行远程牙科检查的新方法在研究环境中具有广泛的应用。临床实践,和提供远程医疗。
背景:澳大利亚新西兰临床试验注册ACTRN12622001237774;https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=384632。
■DERR1-10.2196/51578。
BACKGROUND: Digital methods that enable early caries identification can streamline data collection in research and optimize dental examinations for young children. Intraoral scanners are devices used for creating 3D models of teeth in dentistry and are being rapidly adopted into clinical workflows. Integrating fluorescence technology into scanner hardware can support early caries detection. However, the performance of caries detection methods using 3D models featuring color and fluorescence in primary teeth is unknown.
OBJECTIVE: This study aims to assess the diagnostic agreement between visual examination (VE), on-screen assessment of 3D models in approximate natural colors with and without fluorescence, and application of an automated caries scoring system to the 3D models with fluorescence for caries detection in primary teeth.
METHODS: The study sample will be drawn from eligible participants in a randomized controlled trial at the Royal Children\'s Hospital, Melbourne, Australia, where a dental assessment was conducted, including VE using the International Caries Detection and Assessment System (ICDAS) and intraoral scan using the TRIOS 4 (3Shape TRIOS A/S). Participant clinical records will be collected, and all records meeting eligibility criteria will be subject to an on-screen assessment of 3D models by 4 dental practitioners. First, all primary tooth surfaces will be examined for caries based on 3D geometry and color, using a merged ICDAS index. Second, the on-screen assessment of 3D models will include fluorescence, where caries will be classified using a merged ICDAS index that has been modified to incorporate fluorescence criteria. After 4 weeks, all examiners will repeat the on-screen assessment for all 3D models. Finally, an automated caries scoring system will be used to classify caries on primary occlusal surfaces. The agreement in the total number of caries detected per person between methods will be assessed using a Bland-Altman analysis and intraclass correlation coefficients. At a tooth surface level, agreement between methods will be estimated using multilevel models to account for the clustering of dental data.
RESULTS: Automated caries scoring of 3D models was completed as of October 2023, with the publication of results expected by July 2024. On-screen assessment has commenced, with the expected completion of scoring and data analysis by March 2024. Results will be disseminated by the end of 2024.
CONCLUSIONS: The study outcomes may inform new practices that use digital models to facilitate dental assessments. Novel approaches that enable remote dental examination without compromising the accuracy of VE have wide applications in the research environment, clinical practice, and the provision of teledentistry.
BACKGROUND: Australian New Zealand Clinical Trials Registry ACTRN12622001237774; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=384632.
UNASSIGNED: DERR1-10.2196/51578.