Dental caries is the most prevalent chronic condition worldwide. Early detection can significantly improve treatment outcomes and reduce the need for invasive procedures. Recently, near-infrared transillumination (TI) imaging has been shown to be effective for the detection of early stage lesions. In this work, we present a deep learning model for the automated detection and localization of dental lesions in TI images. Our method is based on a convolutional neural network (CNN) trained on a semantic segmentation task. We use various strategies to mitigate issues related to training data scarcity, class imbalance, and overfitting. With only 185 training samples, our model achieved an overall mean intersection-over-union (IOU) score of 72.7% on a 5-class segmentation task and specifically an IOU score of 49.5% and 49.0% for proximal and occlusal carious lesions, respectively. In addition, we constructed a simplified task, in which regions of interest were evaluated for the binary presence or absence of carious lesions. For this task, our model achieved an area under the receiver operating characteristic curve of 83.6% and 85.6% for occlusal and proximal lesions, respectively. Our work demonstrates that a deep learning approach for the analysis of dental images holds promise for increasing the speed and accuracy of caries detection, supporting the diagnoses of dental practitioners, and improving patient outcomes.

To investigate the safety and efficacy of Self-Assembling Peptide P11-4 (SAP P11-4) compared to placebo or fluoride varnish (FV), a randomized, controlled, blinded, split-mouth study with sequential design was conducted. Subjects presenting two teeth with White-Spot-Lesions (WSLs) were included and teeth were randomly assigned to test or control. Control received placebo at baseline (D0) and test SAP P11-4, all received FV at Day 90 (D90). Standardized photographs were taken at each visit, and WSL size changes were morphometrically assessed. Hierarchical Linear Modelling, considering paired and sequential design, was used to test four hypotheses. SAP P11-4 lesions (test, D90–D0) showed significant WSL size reduction compared to placebo (control, D90–D0; p = 0.008) or FV (control, D180–D90; p = 0.001). Combination of SAP P11-4 and delayed FV after 90 days (test, D180–D0), showed a significant difference compared to FV alone (control D270–D90; p = 0.003). No significant difference on FV efficacy was found when SAP P11-4 was applied 3 months before FV (test D270–D90; control D270–D90, p = 0.70). SAP P11-4 treatment resulted in superior caries regression compared to either placebo or FV, and FV efficacy seems not to be affected by SAP P11-4. SAP P11-4 was found to be a safe and effective WSL treatment.

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Significance: It is not sufficient to detect caries lesions on tooth surfaces; it is also necessary to measure the activity of the lesions to determine if intervention is needed. Changes in the reflectivity of lesion areas during dehydration with forced air at short wavelength infrared (SWIR) wavelengths can be used to assess lesion activity since these changes represent the evaporation dynamics of water from the lesion.Aim: The aim of this study is to develop new optical methods for assessing lesion activity on tooth surfaces utilizing the strong water absorption band near 1950-nm.Approach: The time-resolved reflectivity of 20 active and arrested caries lesions on the surfaces of human extracted teeth was monitored at 1300 to 2000 nm using broadband light sources and an extended range InGaAs camera during drying with air.Results: Multiple parameters representing the rate of change of the lesion reflectivity correlated with the presence of a highly mineralized outer surface zone indicative of lesion arrest measured with x-ray microtomography (microCT). Performance at 1950-nm was higher than for other wavelengths.Conclusions: This study demonstrates that SWIR imaging near 1950-nm has great potential for the assessment of lesion activity.

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