Caries Detection in Primary Teeth Using Intraoral Scanners Featuring Fluorescence: Protocol for a Diagnostic Agreement Study

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.

TRIAL REGISTRATION

Australian New Zealand Clinical Trials Registry ACTRN12622001237774; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=384632.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/51578.

Oral health research: A source for innovative new statistical developments

This article reviews the challenges and opportunities that oral health research may offer to a statistician. To illustrate this, we focus on the Signal Tandmobiel® study, a longitudinal oral health survey that triggered many statistical explorations and developments over the last two decades. For example, non-standard distributions are more the rule than the exception in oral health research. In addition, often measurement error problems need to be addressed. The hierarchical structure of the oral health data also poses non-standard challenges. For instance, caries experience in the mouth is spatially correlated with, however, a specific metric defining the distance between two occurrences. In addition, since caries experience in the context of an epidemiological study is only measured at intervals, analysis of survival involves interval-censoring. Finally, when analyzing a realistic data set in oral health, all the above issues may have to be dealt with simultaneously.

Hierarchical modeling of agreement

Kappa-like agreement indexes are often used to assess the agreement among examiners on a categorical scale. They have the particularity of correcting the level of agreement for the effect of chance. In the present paper, we first define two agreement indexes belonging to this family in a hierarchical context. In particular, we consider the cases of a random and fixed set of examiners. Then, we develop a method to evaluate the influence of factors on these indexes. Agreement indexes are directly related to a set of covariates through a hierarchical model. We obtain the posterior distribution of the model parameters in a Bayesian framework. We apply the proposed approach on dental data and compare it with the generalized estimating equations approach.

Examiner performance in calibration exercises compared with field conditions when scoring caries experience

The objective of this study was to verify how valid misclassification measurements obtained from a 'pre-survey' calibration exercise are by comparing them to validation scores obtained in 'field' conditions. Validation data were collected from the 'Smile for Life' project, an oral health intervention study in Flemish children. A calibration exercise was organized under 'pre-survey' conditions (32 age-matched children examined by eight examiners and the benchmark scorer). In addition, using a pre-determined sampling scheme blinded to the examiners, the benchmark scorer re-examined between six and 11 children screened by each of the dentists during the survey. Factors influencing sensitivity and specificity for scoring caries experience (CE) were investigated, including examiner, tooth type, surface type, tooth position (upper/lower jaw, right/left side) and validation setting (pre-survey versus field). In order to account for the clustering effect in the data, a generalized estimating equations approach was applied. Sensitivity scores were influenced not only by the calibration setting (lower sensitivity in field conditions, p < 0.01), but also by examiner, tooth type (lower sensitivity in molar teeth, p < 0.01) and tooth position (lower sensitivity in the lower jaw, p < 0.01). Factors influencing specificity were examiner, tooth type (lower specificity in molar teeth, p < 0.01) and surface type (the occlusal surface with a lower specificity than other surfaces) but not the validation setting. Misclassification measurements for scoring CE are influenced by several factors. In this study, the validation setting influenced sensitivity, with lower scores obtained when measuring data validity in 'field' conditions. Results obtained in a pre-survey calibration setting need to be interpreted with caution and do not (always) reflect the actual performance of examiners during the field work.

Measurement, analysis and interpretation of examiner reliability in caries experience surveys: some methodological thoughts

Data obtained from calibration exercises are used to assess the level of agreement between examiners (and the benchmark examiner) and/or between repeated examinations by the same examiner in epidemiological surveys or large-scale clinical studies. Agreement can be measured using different techniques: kappa statistic, percentage agreement, dice coefficient, sensitivity and specificity. Each of these methods shows specific characteristics and has its own shortcomings. The aim of this contribution is to critically review techniques for the measurement and analysis of examiner agreement and to illustrate this using data from a recent survey in young children, the Smile for Life project. The above-mentioned agreement measures are influenced (in differing ways and extents) by the unit of analysis (subject, tooth, surface level) and the disease level in the validation sample. These effects are more pronounced for percentage agreement and kappa than for sensitivity and specificity. It is, therefore, important to include information on unit of analysis and disease level (in validation sample) when reporting agreement measures. Also, confidence intervals need to be included since they indicate the reliability of the estimate. When dependency among observations is present [as is the case in caries experience data sets with typical hierarchical structure (surface-tooth-subject)], this will influence the width of the confidence interval and should therefore not be ignored. In this situation, the use of multilevel modelling is necessary. This review clearly shows that there is a need for the development of guidelines for the measurement, interpretation and reporting of examiner reliability in caries experience surveys.