Tests to detect and inform the diagnosis of root caries

Accuracy of different approaches for detecting proximal root caries lesions in vitro

OBJECTIVES

The objective was to evaluate the diagnostic accuracy of radiographic evaluation (XR), visual-tactile assessment (VT), laser-fluorescence (LF) (DIAGNOdent Pen/KaVo), and near-infrared-light transillumination (NILT) (DIAGNOcam/KaVo) on proximal root caries lesions in vitro.

METHODS

Two-hundred extracted permanent premolars and molars with and without proximal root caries lesions were allocated to 50 diagnostic models simulating the proximal contacts between teeth and mounted in a phantom dummy head. Two independent examiners used the diagnostic approaches to detect any or advanced root caries lesions, with histologic evaluation of the lesions serving as reference. Receiver operating characteristic (ROC) curves were employed, and sensitivity, specificity, and the area under the ROC curve (AUC) are calculated. Significant differences in mean AUCs between approaches were assumed if p < 0.05 (two-sample t-test).

RESULTS

NILT was not applicable for proximal root caries detection. The sensitivity/specificity to detect any lesions was 0.81/0.63 for XR, 0.76/0.88 for VT and 0.81/0.95 for LF, and the sensitivity/specificity to detect advanced lesions was 0.43/0.94 for XR, 0.66/0.99 for VT, and 0.83/0.78 for LF, respectively. For both, any and advanced root caries lesions, mean AUCs for LF and VT were significantly higher compared to XR (p < 0.05). For any root caries lesions, LF was significantly more accurate than VT (p = 0.01), but not for advanced root caries lesions (p = 0.59).

CONCLUSIONS

Under the in vitro conditions chosen, LF and VT were more accurate than XR to detect proximal root caries lesions, with LF being particularly useful for initial lesion stages.

CLINICAL RELEVANCE

LF might be a useful diagnostic aid for proximal root caries diagnosis. Clinical studies are necessary to corroborate the findings.

Development of a Root Caries Prediction Model in a Population of Dental Attenders

Root caries prevalence is increasing as populations age and retain more of their natural dentition. However, there is generally no accepted practice to identify individuals at risk of disease. There is a need for the development of a root caries prediction model to support clinicians to guide targeted prevention strategies. The aim of this study was to develop a prediction model for root caries in a population of regular dental attenders. Clinical and patient-reported predictors were collected at baseline by routine clinical examination and patient questionnaires. Clinical examinations were conducted at the 4-year timepoint by trained outcome assessors blind to baseline data to record root caries data at two thresholds - root caries present on any teeth (RC > 0) and root caries present on three or more teeth (RC ≥ 3). Multiple logistic regression analyses were performed with the number of participants with root caries at each outcome threshold utilized as the outcome and baseline predictors as the candidate predictors. An automatic backwards elimination process was conducted to select predictors for the final model at each threshold. The sensitivity, specificity, and c-statistic of each model's performance was assessed. A total of 1,432 patient participants were included within this prediction model, with 324 (22.6%) presenting with at least one root caries lesion, and 97 (6.8%) with lesions on three or more teeth. The final prediction model at the RC >0 threshold included increasing age, having ≥9 restored teeth at baseline, smoking, lack of knowledge of spitting toothpaste without rinsing following toothbrushing, decreasing dental anxiety, and worsening OHRQoL. The model sensitivity was 71.4%, specificity 69.5%, and c-statistic 0.79 (95% CI: 0.76, 0.81). The predictors included in the final prediction model at the RC ≥ 3 threshold included increasing age, smoking, and lack of knowledge of spitting toothpaste without rinsing following toothbrushing. The model sensitivity was 76.5%, specificity 73.6%, and c-statistic 0.81 (95% CI: 0.77, 0.86). To the authors' knowledge, this is the largest published root caries prediction model, with statistics indicating good model fit and providing confidence in its robustness. The performance of the risk model indicates that adults at risk of developing root caries can be accurately identified, with superior performance in the identification of adults at risk of multiple lesions.

A deep learning model using convolutional neural networks for caries detection and recognition with endoscopes

Background

Caries are common, especially in economically undeveloped countries with limited access to medical resources. Sometimes patient cannot even realize that they have oral problems until they feel obvious pain. Deep convolutional neural networks (CNNs) have been widely adopted for medical image analysis and management and have yielded some progress in stomatology while the endoscopes are cheap and easily used in daily life for families or other non-medical situations. Therefore, we created a deep learning model to detect and recognize caries using endoscopic images.

Methods

We used 194 images of non-caries and 1,059 images of permanent molar and premolar caries to build a classification and a segmentation model in patients of endoscope images from the Department of Stomatology of People's Liberation Army General Hospital (PLAGH). A classification model combined with an end-to-end semantic segmentation model, DeepLabv3+ was used for segmenting the caries, then we evaluated with a 5-fold cross-validation protocol whereby each fold was used once.

Results

In the classification model, the mean area under the curve (AUC) [90% confidence interval (CI)] was 0.9897 (0.9821-0.9956) (P<0.01) In the segmentation model, the mean accuracy was 0.9843 (0.9820-0.9871), the recall was 0.6996 (0.6810-0.7194), the specificity was 0.9943 (0.9937-0.9954), the Dice coefficient was 0.7099 (0.6948-0.7343), and the intersection over union (IoU) was 0.5779 (0.5646-0.6006).

Conclusions

We used a deep learning model to monitor caries and encourage their early diagnosis and treatment.

Are direct restorations an effective treatment strategy in the management of root caries lesions?

Data sources PubMed, Cochrane Library/Embase and Web of science.Study selection Randomised controlled trials and non-randomised trials reporting success or failure rates of direct restorative materials for managing root caries lesions and in vitro studies assessing performance of restorations for root caries lesions published between September 1990 and October 2021 and written in English were included.Data extraction and synthesis Data was extracted by one reviewer and quality assessment of the included clinical studies performed using the Cochrane risk-of-bias in non-randomised studies of interventions tools. Restoration success, survival and failure rates were presented for each included study with no data synthesis.Results A total of 11 clinical studies and 31 in vitro studies were included. Restoration failure rates ranged from 14-55% at 24-60 months. Success rates of atraumatic restorative technique (ART) ranged from 65-87% at 12-60 months.Conclusion There is a lack of high-quality evidence to provide a recommendation on the most appropriate restorative material for restoring root caries lesions. The survival rate of ART and conventional techniques are comparable and ART could be an optional treatment where conventional treatment is challenging.

Diagnostic Accuracy of CBCT for Detection of Second Canal of Permanent Teeth: A Systematic Review and Meta-Analysis

INTRODUCTION

Missed canal is one of the common reasons for nonsurgical endodontic retreatments. The missed canals were frequently associated with periapical pathology. The aim of this systematic review was to find the diagnostic accuracy of CBCT for detection of the second canal of the root canal system of permanent teeth.

MATERIALS AND METHODS

The articles were selected from seven electronic databases according to selection criteria. All eligible studies were judged by the reviewers. The selected studies were checked with the QUADAS-2 tool for risk of bias and applicability concerns. Finally, 12 studies were selected for qualitative and quantitative analyses. The summary estimates of sensitivities and specificities and SROC curves were calculated and drawn by RevMan 5.3 and MetaDTA software.

RESULTS

Summary estimates of CBCT for detection of second canal anatomy in permanent teeth were 94% sensitivity and 93.1% specificity. 96.6% sensitivity of MB2 was followed by 88.8% sensitivity of maxillary and mandibular premolars and 81% that of mandibular molars. The specificity of 97.6% for premolars was trialed by 85% specificity of mandibular molars and MB2. For permanent mandibular canines, 67% sensitivity and 100% specificity were estimated. CBCT showed more agreement with detecting the second canal with micro-CT, estimating 100% sensitivity and 95.6% specificity. The highest prevalence of the second canal comprised the highest sensitivity of 99.1% and lowest specificity of 77.5%. After the exclusion of case-control studies, a 3% drop of sensitivity from the summary estimate was observed. Multiple spectrum of the second canal had 8.6% higher sensitivity and 4.4% lower specificity than single spectrum.

CONCLUSION

CBCT is informative for detecting the second canal. Clinicians should keep in mind that the accuracy can vary in different types of teeth, with the prevalence of second canal across different populations, and with the spectrum of second canal anatomy in spite of the reviewers having postulated overestimation of the findings.

Visual or visual-tactile examination to detect and inform the diagnosis of enamel caries

BACKGROUND

The detection and diagnosis of caries at the initial (non-cavitated) and moderate (enamel) levels of severity is fundamental to achieving and maintaining good oral health and prevention of oral diseases. An increasing array of methods of early caries detection have been proposed that could potentially support traditional methods of detection and diagnosis. Earlier identification of disease could afford patients the opportunity of less invasive treatment with less destruction of tooth tissue, reduce the need for treatment with aerosol-generating procedures, and potentially result in a reduced cost of care to the patient and to healthcare services.

OBJECTIVES

To determine the diagnostic accuracy of different visual classification systems for the detection and diagnosis of non-cavitated coronal dental caries for different purposes (detection and diagnosis) and in different populations (children or adults).

SEARCH METHODS

Cochrane Oral Health's Information Specialist undertook a search of the following databases: MEDLINE Ovid (1946 to 30 April 2020); Embase Ovid (1980 to 30 April 2020); US National Institutes of Health Ongoing Trials Register (ClinicalTrials.gov, to 30 April 2020); and the World Health Organization International Clinical Trials Registry Platform (to 30 April 2020). We studied reference lists as well as published systematic review articles.

SELECTION CRITERIA

We included diagnostic accuracy study designs that compared a visual classification system (index test) with a reference standard (histology, excavation, radiographs). This included cross-sectional studies that evaluated the diagnostic accuracy of single index tests and studies that directly compared two or more index tests. Studies reporting at both the patient or tooth surface level were included. In vitro and in vivo studies were considered. Studies that explicitly recruited participants with caries into dentine or frank cavitation were excluded. We also excluded studies that artificially created carious lesions and those that used an index test during the excavation of dental caries to ascertain the optimum depth of excavation.

DATA COLLECTION AND ANALYSIS

We extracted data independently and in duplicate using a standardised data extraction and quality assessment form based on QUADAS-2 specific to the review context. Estimates of diagnostic accuracy were determined using the bivariate hierarchical method to produce summary points of sensitivity and specificity with 95% confidence intervals (CIs) and regions, and 95% prediction regions. The comparative accuracy of different classification systems was conducted based on indirect comparisons. Potential sources of heterogeneity were pre-specified and explored visually and more formally through meta-regression.

MAIN RESULTS

We included 71 datasets from 67 studies (48 completed in vitro) reporting a total of 19,590 tooth sites/surfaces. The most frequently reported classification systems were the International Caries Detection and Assessment System (ICDAS) (36 studies) and Ekstrand-Ricketts-Kidd (ERK) (15 studies). In reporting the results, no distinction was made between detection and diagnosis. Only two studies were at low risk of bias across all four domains, and 15 studies were at low concern for applicability across all three domains. The patient selection domain had the highest proportion of high risk of bias studies (49 studies). Four studies were assessed at high risk of bias for the index test domain, nine for the reference standard domain, and seven for the flow and timing domain. Due to the high number of studies on extracted teeth concerns regarding applicability were high for the patient selection and index test domains (49 and 46 studies respectively). Studies were synthesised using a hierarchical bivariate method for meta-analysis. There was substantial variability in the results of the individual studies: sensitivities ranged from 0.16 to 1.00 and specificities from 0 to 1.00. For all visual classification systems the estimated summary sensitivity and specificity point was 0.86 (95% CI 0.80 to 0.90) and 0.77 (95% CI 0.72 to 0.82) respectively, diagnostic odds ratio (DOR) 20.38 (95% CI 14.33 to 28.98). In a cohort of 1000 tooth surfaces with 28% prevalence of enamel caries, this would result in 40 being classified as disease free when enamel caries was truly present (false negatives), and 163 being classified as diseased in the absence of enamel caries (false positives). The addition of test type to the model did not result in any meaningful difference to the sensitivity or specificity estimates (Chi²(4) = 3.78, P = 0.44), nor did the addition of primary or permanent dentition (Chi²(2) = 0.90, P = 0.64). The variability of results could not be explained by tooth surface (occlusal or approximal), prevalence of dentinal caries in the sample, nor reference standard. Only one study intentionally included restored teeth in its sample and no studies reported the inclusion of sealants. We rated the certainty of the evidence as low, and downgraded two levels in total for risk of bias due to limitations in the design and conduct of the included studies, indirectness arising from the in vitro studies, and inconsistency of results.

AUTHORS' CONCLUSIONS

Whilst the confidence intervals for the summary points of the different visual classification systems indicated reasonable performance, they do not reflect the confidence that one can have in the accuracy of assessment using these systems due to the considerable unexplained heterogeneity evident across the studies. The prediction regions in which the sensitivity and specificity of a future study should lie are very broad, an important consideration when interpreting the results of this review. Should treatment be provided as a consequence of a false-positive result then this would be non-invasive, typically the application of fluoride varnish where it was not required, with low potential for an adverse event but healthcare resource and finance costs. Despite the robust methodology applied in this comprehensive review, the results should be interpreted with some caution due to shortcomings in the design and execution of many of the included studies. Studies to determine the diagnostic accuracy of methods to detect and diagnose caries in situ are particularly challenging. Wherever possible future studies should be carried out in a clinical setting, to provide a realistic assessment of performance within the oral cavity with the challenges of plaque, tooth staining, and restorations, and consider methods to minimise bias arising from the use of imperfect reference standards in clinical studies.