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Objectives 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. Trial registration The clinical trial was registered in the Chinese Clinical Trial Registry (No. ChiCTR2300073487, Date: 12/07/2023).

The aim of this study was to present an optimal diagnostic protocol by comparing and analyzing a conventional examination and the quantitative light-induced fluorescence (QLF) technique. Selected were 297 teeth of 153 patients to take QLF images and bitewing radiographs. Occlusal dental caries, proximal dental caries and cracks were evaluated and scored using QLF, X-ray and/or visual criteria. The sensitivity, specificity, and area under the curve (AUC) of a receiver operating characteristic analysis were calculated. Two fluorescence parameters (|ΔFmax| and ΔRmax) were utilized to evaluate the fluorescence pattern according to the severity of lesions based on QLF or X-ray criteria. QLF showed higher scores for detecting occlusal dental caries and cracks than the conventional method. ΔRmax increased more clearly than ΔFmax did with occlusal dental caries. The |ΔFmax| values of occlusal dental caries, proximal dental caries and cracks showed good AUC levels (0.84, 0.81 and 0.83, respectively). The ΔRmax of occlusal dental caries showed the highest AUC (0.91) and the ΔRmax of proximal dental caries showed a fail level (0.59) compared to bitewing radiographs. The QLF image could visualize and estimate the degree of occlusal dental caries or cracks. Consequently, the QLF technique may be an adjunct tool to conventional methods for the detection of occlusal caries and peripheral cracks.

The aim of this study was to evaluate periodontal risk factors with oral health habits and fluorescent plaque index (FPI) using quantitative light-induced fluorescence (QLF) images, and to evaluate their effect on the degree of radiographic bone loss (RBL). Selected were 276 patients over 19 years of age to complete the questionnaire for oral health habit and take QLF images, periapical and panoramic radiographs. Oral health habit score, age, and sex showed a statistically significant correlation with FPI. FPI showed a lower value as the oral health habit score increased and the age decreased. Moreover, females showed lower FPI values than did males. RBL showed a statistically significant positive correlation with age but did not show any correlation with oral health habit scores and sex. There was no correlation between FPI and RBL. The results of this study suggest that the clinical use of QLF allows plaque detection by non-invasive procedures and can aid in a more objective estimation for oral hygiene status.

In vivo repeatability and reproducibility of the quantitative light-induced fluorescence (QLF) method were tested with respect to three variables: lesion area, and average and maximum changes in lesion fluorescence. To test the image-capturing stages, three analysts each captured images of 15 incipient smooth surface lesions in vivo, and the images were analysed by one of the analysts. To test the analytical stage of the method, three analysts analysed the images of 15 in vivo incipient smooth surface lesions. For the image-capturing stage, inter-examiner reliability showed an intra-class correlation coefficient (r) between 0.95 and 0.98. For the analytical stage, intra-examiner reliability for all three analysts showed a value of r between 0.93 and 0.99. Inter-examiner reliability showed a value of r between 0.95 and 0.99. It was concluded that the in vivo repeatability and reproducibility of the QLF method are excellent.

When using quantitative light-induced fluorescence a number of factors can influence illumination level. The purpose of this study was to investigate, using a high-resolution camera and fibre-optic light source, the impact of illumination level and focal distance on common quantitative light-induced fluorescence outcomes. Twenty-four extracted teeth were examined using 6 illumination levels and 4 focal distances. Analysis was conducted using multiple linear regression models fitted to log ΔQ, log ΔF and log area with clustering of teeth and robust standard errors. Separate models were used for the different light and focal levels. The regression coefficients were significant for both ΔQ and ΔF but not area. Despite the significant regressions the actual effect was very small, and unlikely to confound clinical trial or practice results.

The aim of the study was to find the optimal illumination and camera angulations for interproximal use of quantitative light-induced fluorescence (QLF). A multiaxis optical bench was developed and interproximal tooth assemblies were investigated using a modified version of QLF. Extracted human premolars without caries (n = 8) and with interproximal D1, D2 and D3 caries (n = 20) were selected. Tooth-pair models without caries and with interproximal caries of matching size, location, and shape were imaged with varying camera and illumination directions from buccal (0°) to occlusal (90°) to lingual (180°) in steps of 30° using a PC and framegrabber and examined for observed presence. Interproximal lesions could be detected in all teeth, but observed presence was dependent on camera angulation (p < 0.05), rather than on illumination angulation (p = 0.32). No caries could be detected with the camera in the 90° position.