OBJECTIVE: To investigate the in vivo diagnostic agreement between visual examination (VE) using the International Caries Detection and Assessment System (ICDAS) and an automated scanner system for detecting and classifying carious lesions in primary teeth.
METHODS: 5-year-old children (n = 216) underwent VE and intraoral scanning (TRIOS 4, 3Shape TRIOS A/S, Copenhagen, Denmark). Dental caries experience was recorded for each tooth surface using ICDAS. An automated, fluorescence-based caries scoring system was applied to eligible primary teeth occlusal surfaces on the 3D models using commercially available software. The automated system classified surfaces as sound, initial caries (ICDAS 01/02), or moderate-extensive caries (ICDAS ≥03). The diagnostic agreement was investigated using multi-level modelling and intraclass correlation coefficients. Analyses were repeated at both the initial threshold (ICDAS ≥01) and the moderate-extensive threshold (ICDAS ≥03).
RESULTS: 213 participants were included in the study, and 1525 primary molar occlusal surfaces were included in the analysis. The odds of detecting caries using the automated system were 46 % lower at the initial disease threshold (OR 0.54, 95 % CI 0.39-0.74) and 70 % lower at the moderate-extensive disease threshold (OR 0.30, 95 % CI 0.16-0.58) compared to VE. The intraclass correlation estimates at the initial and moderate-extensive thresholds were 0.90 (95 % CI 0.70-0.96) and 0.76 (95 % CI 0.22-0.94) respectively.
CONCLUSIONS: The automated system is less likely to detect initial lesions and is more likely to underestimate lesion severity relative to visual examination using ICDAS.
CONCLUSIONS: Clinically, using the automated tool to replace thorough visual inspection in primary teeth could result in missed opportunities to provide professional or self-care to arrest or reverse early disease. Additionally, it could misclassify moderate lesions as initial caries, potentially leading to complications associated with the delayed management of dental caries.

In comparison to conventional combustible cigarettes, Electronic Nicotine Delivery Systems (ENDS) including both e-cigarettes (ECs) and heated tobacco products (HTPs) significantly reduce exposure to toxic chemical emissions. However, their impact on dental plaque remains unclear. This study measures dental plaque in ENDS (ECs and HTPs) users using quantitative light-induced fluorescence (QLF) technology, comparing them with current, former, and never smokers.
This cross-sectional study compared dental plaque measurements using QLF technology (Q-ray cam™ Pro) among current smokers (≥10 cigarettes/day), former smokers (quit ≥6 months), never smokers, and exclusive ENDS users (quit ≥6 months). Dental plaque measurements were expressed as ΔR30 (total area of mature dental plaque) and ΔR120 (greater plaque thickness/maturation-calculus). The Simple Oral Hygiene (SOH) score was calculated by the QLF proprietary software. Statistical analyses including ANCOVA was performed by R version (4.2.3) with p < 0.05.
A total 30 smokers, 24 former smokers, 29 never smokers, and 53 ENDS users were included. Current smokers had significantly higher ΔR30 and ΔR120 values compared to other groups (p < 0.001). ENDS users showed plaque levels similar to never and former smokers (p > 0.05) but significantly lower than current smokers (p < 0.01). Although ENDS users showed a lower SOH score than smokers, this difference was not statistically significant. Daily toothbrushing and mouthwash usage were significant covariates.
ENDS users exhibited reduced accumulation of dental plaque and calculus compared with current smokers.
Exclusive ENDS use could less impact dental plaque accumulation compared to cigarette smoking. Further research is needed to confirm these findings and fully understand ENDS impact on dental plaque formation.

This bibliometric analysis evaluated the top 100 most-cited articles on the application of quantitative light-induced fluorescence (QLF) technology in caries research.
The following data were collected: title, authors, country, institution, citations count, title and year of article, study design, topic and keywords. Networks among authors and keywords were constructed by VOSviewer software.
Scopus database on April 25, 2024.
A global citation score of 4633 (average 46.33 citations) was calculated with publication years ranged from 1999 to 2020. Caries Research emerged as the top contributing journal. Pretty IA was the most prolific author (18 %). United Kingdom had the highest number of most-cited papers (32 %), followed by Netherlands and USA (20 % each). Laboratory studies constituted the predominant study design (45 %), followed by randomized clinical trials (20 %) and non-systematic reviews (11 %). The keywords \"dental caries\" and \"fluorescence\" had 81 and 79 occurrences, respectively. The main topic was QLF use for caries detection (45 %).
This paper provides an update summary of the scientific impact of QLF technology application in caries research. QLF has gained increasing attention worldwide, accompanied by a consistent rise in scientific investigations exploring its application in caries research.
The findings offer valuable insights into the most influential articles in QLF technology for caries assessment, serving as a critical resource for researchers, clinicians, and students. Understanding the trends in this field can aid in informed decision-making and the advancement of evidence-based practices in caries management and prevention.

OBJECTIVE: This study investigated the effectiveness of a deep convolutional neural network (CNN) in diagnosing and staging caries lesions in quantitative light-induced fluorescence (QLF) images taken by a self-manufactured handheld device.
METHODS: A small toothbrush-like device consisting of a 400 nm UV light-emitting lamp with a 470 nm filter was manufactured for intraoral imaging. A total of 133 cases with 9,478 QLF images of teeth were included for caries lesion evaluation using a CNN model. The database was divided into development, validation, and testing cohorts at a 7:2:1 ratio. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated for model performance.
RESULTS: The overall caries prevalence was 19.59%. The CNN model achieved an AUC of 0.88, an accuracy of 0.88, a specificity of 0.94, and a sensitivity of 0.64 in the validation cohort. They achieved an overall accuracy of 0.92, a sensitivity of 0.95 and a specificity of 0.55 in the testing cohort. The model can distinguish different stages of caries well, with the best performance in detecting deep caries followed by intermediate and superficial lesions.
CONCLUSIONS: Caries lesions have typical characteristics in QLF images and can be detected by CNNs. A QLF-based device with CNNs can assist in caries screening in the clinic or at home.
BACKGROUND: The clinical trial was registered in the Chinese Clinical Trial Registry (No. ChiCTR2300073487, Date: 12/07/2023).

This in vitro study aimed to evaluate the feasibility of quantitative light-induced fluorescence (QLF) technology for detecting the presence and severity of microleakage of pit and fissure sealants. The areas of interest (AOIs) were 160 pits and fissures of 40 extracted permanent teeth. Fluorescent images were acquired using a QLF device, and the maximum fluorescence loss ΔFmax of each AOI was analyzed. After staining and cross-sectioning of the teeth, histological dye penetration was scored on a scale of 0 to 3. The relationship between ΔFmax and microleakage depth was analyzed, and the areas under the curve (AUCs) were calculated. The │ΔFmax│ increased as microleakage depth increased. The ΔFmax values of microleakage areas showed a strong significant correlation with the histological scores of dye penetration (r = - 0.72, P = 0.001). AUC analysis showed a high diagnostic accuracy for microleakage depth (AUC = 0.83-0.91). The highest AUC of 0.91 was found when differentiating the outer half microleakage of the sealant (histological score 0 vs. 1-3). QLF technology is effective in assessing the presence and severity of microleakage, suggesting its potential for noninvasive detection and monitoring of sealant microleakage in clinical settings.

BACKGROUND: Oral biofilms are a critical component in dental caries formation. However, current remineralization studies often overlook the impact of microbial factors. Therefore, a comprehensive clinically relevant assessment of caries is needed. This study aimed to develop a novel in vitro model capable of generating non-cavitated carious lesions that incorporates both mineral loss and microbial activity using quantitative light-induced fluorescence-digital (QLF-D) technology.
METHODS: A total of 44 artificial early carious lesions were formed using bovine incisors. The extent of fluorescence loss (ΔF) was analyzed using a QLF-D camera. Oral microcosm biofilms were then employed to construct 22 active and 22 inactive carious lesions. The red fluorescence emission rate (ΔR) and bacterial viability (RatioG/G+R) was measured using QLF-D camera and a live-dead bacterial assay, respectively. Independent t-tests were performed to compare ΔF, ΔR, and bacterial viability of artificial carious lesions according to their activity status.
RESULTS: No significant difference in ΔF between the lesions was found based on activity status (p = 0.361). However, the ΔR of active lesions was 1.82 times higher than that of inactive lesions, and the RatioG/G+R was 1.49 times higher in active lesions than in inactive lesions (both p < 0.001).
CONCLUSIONS: The significant differences observed in ΔR and RatioG/G+R between active and inactive lesions emphasize the importance of considering lesion activity status when evaluating the potential efficacy of remineralization agents. This study presents a novel in vitro remineralization assessment model that reflects carious lesion activity while controlling baseline mineral distributions of lesions.

BACKGROUND: The tongue coating is a grayish-white deposit that quickly reflects the state of health or disease of the human body. Quantitative light-induced fluorescence (QLF) is a novel digital imaging system that objectively quantifies tongue coating.
OBJECTIVE: The present study aims to evaluate the correlation between the visual assessment of tongue coating and tongue coating by analysis of QLF-digital (QLF-D) images.
METHODS: This was an in vivo explorative study.
METHODS: Fifty children aged 11-13 years with clinically visible tongue coating were selected for the study. Tongue coating was assessed clinically by the Tongue Coating Index (Shimizu et al., 2007) and digitally by QLF-D Biluminator™ 2, C3 software.
METHODS: Data obtained were subjected to statistical analysis using SPSS 23.0 software. Spearman\'s rho correlation test was done, and P < 0.05 was considered statistically significant.
RESULTS: A statistically significant correlation was found between the visual assessment scoring and the QLF image analysis for the evaluation of tongue coating.
CONCLUSIONS: The Digital QLF tongue imaging system was found to be reliable due to its correlation with the clinical score and objective nature.

OBJECTIVE: Evaluating early carious lesion activity with an objective and clinically valid approach is crucial for developing effective treatment plans. Therefore, we here assessed the activity of non-cavitated carious lesions using a quantitative light-induced fluorescence-digital (QLF-D) camera and compared the remineralization efficiency after fluoride treatment according to the lesion\'s activity level.
METHODS: Red fluorescence emission rate (ΔR) and fluorescence loss (ΔF) were evaluated in 44 non-cavitated carious lesions by using a QLF-D camera. Based on the ΔR level, the lesions were classified into 22 active (ΔR ≥37.55) and 22 inactive carious lesions (ΔR <37.55). Each lesion was treated with 1.23 % fluoride gel for 60 s and then immersed into artificial saliva for 7 days. Subsequently, ΔR and ΔF changes in the lesions were measured.
RESULTS: Significant interactions between lesion activity and time were found for both ΔR and ΔF (p < 0.001). ΔR of active lesions declined faster and ΔF increased more steeply than did inactive lesions. Specifically, on day 7 post-fluoride treatment, the ΔR reduction rate was 1.40-times higher in active lesions, and the ΔF recovery rate was 2.50-times higher, indicating that active lesions respond more markedly to fluoride application.
CONCLUSIONS: This study highlighted the significance of ΔR in predicting remineralization efficiency in non-cavitated carious lesions after fluoride application. It underscored the importance of accurately assessing caries activity when formulating effective treatment plans. Lesion activity, as determined by ΔR, not only influences the outcome of remineralization treatments but also provides a more objective measure for tailoring caries management strategies.

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.

Owing to the remarkable advancements of artificial intelligence (AI) applications, AI-based detection of dental caries is continuously improving. We evaluated the efficacy of the detection of dental caries with quantitative light-induced fluorescence (QLF) images using a convolutional neural network (CNN) model.
Overall, 2814 QLF intraoral images were obtained from 606 participants at a dental clinic using Qraypen C® (QC, AIOBIO, Seoul, Republic of Korea) from October 2020 to October 2022. These images included all the types of permanent teeth of which surfaces were smooth or occlusal. Dataset were randomly assigned to the training (56.0%), validation (14.0%), and test (30.0%) subsets of the dataset for caries classification. Moreover, masked images for teeth area were manually prepared to evaluate the segmentation efficacy. To compare diagnostic performance for caries classification according to the types of teeth, the dataset was further classified into the premolar (1,143 images) and molar (1,441 images) groups. As the CNN model, Xception was applied.
Using the original QLF images, the performance of the classification algorithm was relatively good showing 83.2% of accuracy, 85.6% of precision, and 86.9% of sensitivity. After applying the segmentation process for the tooth area, all the performance indics including 85.6% of accuracy, 88.9% of precision, and 86.9% of sensitivity were improved. However, the performance indices of each type of teeth (both premolar and molar) were similar to those for all teeth.
The application of AI to QLF images for caries classification demonstrated a good performance regardless of teeth type among posterior teeth. Additionally, tooth area segmentation through background elimination from QLF images exhibited a better performance.