There is growing interest in the use of probiotic bifidobacteria for enhancement of the therapy, and in the prevention, of oral microbial diseases. However, the results of clinical studies assessing the effects of bifidobacteria on the oral microbiota are controversial, and the mechanisms of actions of probiotics in the oral cavity remain largely unknown. In addition, very little is known about the role of commensal bifidobacteria in oral health. Our aim was to study the integration of the probiotic Bifidobacterium animalis subsp. lactis Bb12 and of oral Bifidobacterium dentium and Bifidobacterium longum isolates in supragingival and subgingival biofilm models and their effects on other bacteria in biofilms in vitro using two different in vitro biofilms and agar-overlay assays. All bifidobacteria integrated well into the subgingival biofilms composed of Porphyromonas gingivalis, Actinomyces naeslundii, and Fusobacterium nucleatum and decreased significantly only the number of P. gingivalis in the biofilms. The integration of bifidobacteria into the supragingival biofilms containing Streptococcus mutans and A. naeslundii was less efficient, and bifidobacteria did not affect the number of S. mutans in biofilms. Therefore, our results suggest that bifidobacteria may have a positive effect on subgingival biofilm and thereby potential in enhancing gingival health; however, their effect on supragingival biofilm may be limited.

Increased MMP-9 and decreased TIMP-1 levels in saliva may indicate that probiotics have immunomodulatory effects in the oral cavity. Furthermore, increased salivary MMP-9 levels may be an indication of the defensive potential of matrix metalloproteinases.

The most common imaging methods used in dentistry are X-ray imaging and RGB color photography. However, both imaging methods provide only a limited amount of information on the wavelength-dependent optical properties of the hard and soft tissues in the mouth. Spectral imaging, on the other hand, provides significantly more information on the medically relevant dental and oral features (e.g. caries, calculus, and gingivitis). Due to this, we constructed a spectral imaging setup and acquired 316 oral and dental reflectance spectral images, 215 of which are annotated by medical experts, of 30 human test subjects. Spectral images of the subjects’ faces and other areas of interest were captured, along with other medically relevant information (e.g., pulse and blood pressure). We collected these oral, dental, and face spectral images, their annotations and metadata into a publicly available database that we describe in this paper. This oral and dental spectral image database (ODSI-DB) provides a vast amount of data that can be used for developing, e.g., pattern recognition and machine vision applications for dentistry.

The aim of this work is automatic and efficient detection of medically-relevant features from oral and dental hyperspectral images by applying up-to-date deep learning convolutional neural network techniques. This will help dentists to identify and classify unhealthy areas automatically and to prevent the progression of diseases. Hyperspectral imaging approach allows one to do so without exposing the patient to ionizing X-ray radiation. Spectral imaging provides information in the visible and near-infrared wavelength ranges. The dataset used in this paper contains 116 hyperspectral images from 18 patients taken from different viewing angles. Image annotation (ground truth) includes 38 classes in six different sub-groups assessed by dental experts. Mask region-based convolutional neural network (Mask R-CNN) is used as a deep learning model, for instance segmentation of areas. Preliminary results show high potential and accuracy for classification and segmentation of different classes.

In optical imaging, optical filters can be used to enhance the visibility of featuresof-interest and thus aid in visualization. Optical filter design based on hyperspectral imaging employs various statistical methods to find an optimal design. Some methods, like principal component analysis, produce vectors that can be interpreted as filters that have a partially negative transmission spectrum. These filters, however, are not directly implementable optically. Earlier implementations of partially negative filters have concentrated on spectral reconstruction. Here we show a novel method for implementing partially negative optical filters for contrast-enhancement purposes in imaging applications. We describe the method and its requirements, and show its feasibility with color chart and dental imaging examples. The results are promising: visual comparison of computational color chart render and optical measurement show matching images, and visual inspection of dental images show increased contrast.