BiofilmQuant: A Computer-Assisted Tool for Dental Biofilm Quantification
This tool addresses the need for efficient and accurate biofilm quantification in dental clinical practice, offering a semi-automated solution that balances automation with human input, though it is incremental as it builds on existing methods.
The authors tackled the problem of quantifying dental biofilm in QLF images by developing BiofilmQuant, a semi-automated software tool that uses statistical modeling for segmentation, achieving high consistency and precision on over 200 test scans while allowing clinicians to correct misclassifications with a single click.
Dental biofilm is the deposition of microbial material over a tooth substratum. Several methods have recently been reported in the literature for biofilm quantification; however, at best they provide a barely automated solution requiring significant input needed from the human expert. On the contrary, state-of-the-art automatic biofilm methods fail to make their way into clinical practice because of the lack of effective mechanism to incorporate human input to handle praxis or misclassified regions. Manual delineation, the current gold standard, is time consuming and subject to expert bias. In this paper, we introduce a new semi-automated software tool, BiofilmQuant, for dental biofilm quantification in quantitative light-induced fluorescence (QLF) images. The software uses a robust statistical modeling approach to automatically segment the QLF image into three classes (background, biofilm, and tooth substratum) based on the training data. This initial segmentation has shown a high degree of consistency and precision on more than 200 test QLF dental scans. Further, the proposed software provides the clinicians full control to fix any misclassified areas using a single click. In addition, BiofilmQuant also provides a complete solution for the longitudinal quantitative analysis of biofilm of the full set of teeth, providing greater ease of usability.