Clinical Trial of the Canary System for Proximal Caries Detection: A Comparative Study

Background: Detecting initial caries on the proximal surfaces of teeth in an intact dentition is a problem in dental practice since radiograph has been shown to have poor sensitivity with this stage of caries lesions. Hence there is need for an alternative technology. Objectives: The aim of this study was to investigate the efficacy of the Canary System (CS) to detect proximal caries in a clinical setting, comparing it with bitewing radiography (BWR). Methodology: 33 subjects, age 18 years and above, were recruited from a mixed population of low, moderate, and high caries risk patients. BWR and the CS were used to detect proximal caries lesions in these subjects. Teeth were separated by 48 hours insertion of rubber rings, and the proximal surfaces were examined by direct visual examination using the International Caries Detection and Assessment System II (ICDAS-II) scoring system. The Sensitivity (se), specificity (sp), positive (ppv) and negative predictive (npv) values of the CS and BWR in detecting caries on proximal surfaces were calculated by evaluating each method alone against ICDAS-II system (used as bronze standard). The two methods were compared statistically using their Area Under the Receiver Operating Curve (AUC). The sensitivities and specificities were compared using a test of proportions and AUC values were compared using DeLong’s method of nonparametric testing of AUC values. Results: The se, sp, ppv and npv for the CS are 0.92, 0.78, 0.89, 0.84 respectively, and for BWR are 0.67, 0.54, 0.78, and 0.40 respectively. The AUC of the Canary System (0.77) was statistically significantly higher than the AUC of the radiography (0.53, P < .001). Conclusions: This study demonstrated the efficacy of the Canary System in detecting proximal caries lesions to be greater than that of bitewing radiography. Clinical Significance: The Canary System can be a valuable clinical device for detecting and monitoring proximal caries lesions in clinical practice.

Detecting of Proximal Caries in Primary Molars using Pen-type QLF Device

The purpose of this in vivo study was to assess the clinical screening performance of a quantitative light-induced fluorescence (QLF) device in detecting proximal caries in primary molars. Fluorescence loss, red autofluorescence and a simplified QLF score for proximal caries (QS-proximal) were evaluated for their validity in detecting proximal caries in primary molars compared to bitewing radiography. Three hundred and forty-four primary molar surfaces were included in the study. Carious lesions were scored according to lesion severity assessed by visual-tactile and radiographic examinations. The QLF images were analyzed for two quantitative parameters, fluorescence loss and red autofluorescence, as well as for QS-proximal. For both quantitative parameters and QS-proximal, the sensitivity, specificity and area under receiver operating curve (AUROC) were calculated as a function of the radiographic scoring index at enamel and dentin caries levels. Both quantitative parameters showed fair AUROC values for detecting dentine level caries (△F = 0.794, △R = 0.750). QS-proximal showed higher AUROC values (0.757 - 0.769) than that of visual-tactile scores (0.653) in detecting dentine level caries. The QLF device showed fair screening performance in detecting proximal caries in primary molars compared to bitewing radiography.

Diversity of Site-Specific Microbes of Occlusal and Proximal Lesions in Severe- Early Childhood Caries (S-ECC)

Background: Severe-early childhood caries (S-ECC) is a global problem of significant concern, commonly manifest as a dentinal lesion on the occlusal and proximal surfaces of the affected deciduous dentition. Although there are major ecological differences between these two niches, it is unclear whether these are reflected in the composition of their dysbiotic cariogenic microbiome. Therefore, we compared the compositional differences in the microbiota of occlusal and proximal caries lesions in S-ECC. Methods: Deep-dentine caries samples (19-occlusal and 19-proximal) from asymptomatic primary molars of children with S-ECC (n=19) belonging to caries-code 5/6, according to ICDAS classification, were evaluated. Employing two primer pools, we amplified and compared the bacterial 16S rRNA gene sequences of the seven hypervariable regions (V2 to V4 and V6 to V9) using a next-generation sequencing based assay.Results: Bray-Curtis dissimilarity data indicated that occlusal lesions had a more homogeneous microbial community structure than the proximal lesions with significant compositional differences at species level (p=0.01; R-value of 0.513). Together, the occlusal and proximal niches harbored 263 species, of which 202 (76.8%) species were common to both locales, while 49 (18.6%) and 12 (4.6%) disparate species were exclusively isolated from the proximal and occlusal niches, respectively. The most commonly found genera at both locales included Streptococcus, Prevotella, and Lactobacillus, with 33, 27, and 22 species each, respectively. In addition, Streptococcus mutans predominated in the proximal cavities (p≤0.05), as opposed to Atopobium parvulum (p=0.01) in the occlusal niches, while Vellonella alcalescens was present in similar proportions in both habitats (p≥0.05). Conclusions: Distinct differences between the caries microbiota of occlusal and proximal caries in S-ECC exist. The former niche appears to provide a habitat for a more homogeneous growth of communal microbiota than the latter. This may be due to the conditions prevalent in relatively quiescent inter-proximal regions, as opposed to the occlusal regions exposed to the ebb and flow of salivary and masticatory forces, and/or the anatomical and structural differences in the two locales. The clinical implications of these findings in terms of the rate and severity of caries progression remain to be determined.

Diagnostic validity of early proximal caries detection using near-infrared imaging technology on 3D range data of posterior teeth

Objectives This in vitro study analysed potential of early proximal caries detection using 3D range data of teeth consisting of near-infrared reflection images at 850 nm (NIRR). Materials and methods Two hundred fifty healthy and carious permanent human teeth were arranged pairwise, examined with bitewing radiography (BWR) and NIRR and validated with micro-computed tomography. NIRR findings were evaluated from buccal, lingual and occlusal (trilateral) views according to yes/no decisions about presence of caries. Reliability assessments included kappa statistics and revealed high agreement for both methods. Statistical analysis included cross tabulation and calculation of sensitivity, specificity and AUC. Results Underestimation of caries was 24.8% for NIRR and 26.4% for BWR. Overestimation was 10.4% for occlusal NIRR and 0% for BWR. Trilateral NIRR had overall accuracy of 64.8%, overestimation of 15.6% and underestimation of 19.6%. NIRR and BWR showed high specificity and low sensitivity for proximal caries detection. Conclusions NIRR achieved diagnostic results comparable to BWR. Trilateral NIRR assessments overestimated presence of proximal caries, revealing stronger sensitivity for initial caries detection than BWR. Clinical relevance NIRR provided valid complement to BWR as diagnostic instrument. Investigation from multiple angles did not substantially improve proximal caries detection with NIRR.

The ADEPT study: a comparative study of dentists' ability to detect enamel-only proximal caries in bitewing radiographs with and without the use of AssistDent artificial intelligence software

Introduction Reversal of enamel-only proximal caries by non-invasive treatments is important in preventive dentistry. However, detecting such caries using bitewing radiography is difficult and the subtle patterns are often missed by dental practitioners.Aims To investigate whether the ability of dentists to detect enamel-only proximal caries is enhanced by the use of AssistDent artificial intelligence (AI) software.Materials and methods In the ADEPT (AssistDent Enamel-only Proximal caries assessmenT) study, 23 dentists were randomly divided into a control arm, without AI assistance, and an experimental arm, in which AI assistance provided on-screen prompts indicating potential enamel-only proximal caries. All participants analysed a set of 24 bitewings in which an expert panel had previously identified 65 enamel-only carious lesions and 241 healthy proximal surfaces.Results The control group found 44.3% of the caries, whereas the experimental group found 75.8%. The experimental group incorrectly identified caries in 14.6% of the healthy surfaces compared to 3.7% in the control group. The increase in sensitivity of 71% and decrease in specificity of 11% are statistically significant (p <0.01).Conclusions AssistDent AI software significantly improves dentists' ability to detect enamel-only proximal caries and could be considered as a tool to support preventive dentistry in general practice.

Radiographic Proximal Caries Detection and Multiple Intelligences

Introduction: The accuracy of radiographic caries detection is a multifactorial diagnostic task. One the important factors are the Inter-observer variability. One of the suggested procedures to estimate the variations is the Multiple Intelligence (MI) theory of Howard Gardner. Aim: The current research aims to study the relation among radiographic proximal caries detection accuracy and MI for dental undergraduate and new graduates (trainees) Patients and Methods: Twenty dental students who have just terminated caries detection training and 19 interns were employed. The sample composed of seventeen men and twenty two women. Caries detection precision was estimated as the region under Receiver Operating Characteristic (ROC) curve (Az) scores, and MI scores were estimated by sing a modified, bilingual MIS self-recorded questionnaire. MS Excel software was applied to calculate means, percentages and correlation. Results: There was little and mild positive correlation among Az and Spatial/Visual (0.29) and Intrapersonal (0.31) domains of MI respectively. The current correlation was mild for students (0.38) and (0.40), and it was lowered for interns (0.28) and (0.26) respectively. Conclusions: There is positive weak relationship between Az and the domains of Spatial/Visual and Intrapersonal of MI. This relationship might be diminished by education and training.