DenTiUS Plaque, a Web-Based Application for the Quantification of Bacterial Plaque: Development and Usability Study

Background: In the dentistry field, the analysis of dental plaque is vital because it is the main etiological factor in the 2 most prevalent oral diseases: caries and periodontitis. In most of the papers published in the dental literature, the quantification of dental plaque is carried out using traditional, non-automated, and time-consuming indices. Therefore, the development of an automated plaque quantification tool would be of great value to clinicians and researchers. Objective: This study aimed to develop a web-based tool called DenTiUS and various clinical indices to evaluate dental plaque levels using image analysis techniques. Methods: The tool was executed as a web-based application to facilitate its use by researchers. Expert users are free to define experiments, including images from either a single patient (to observe an individual plaque growth pattern) or several patients (to perform a group characterization) at a particular moment or over time. A novel approach for detecting visible plaque has been developed as well as a new concept known as nonvisible plaque. This new term implies the classification of the remaining dental area into 3 subregions according to the risk of accumulating plaque in the near future. New metrics have also been created to describe visible and nonvisible plaque levels. Results: The system generates results tables of the quantitative analysis with absolute averages obtained in each image (indices about visible plaque) and relative measurements (indices about visible and nonvisible plaque) relating to the reference moment. The clinical indices that can be calculated are the following: plaque index of an area per intensity (API index, a value between 0 and 100), area growth index (growth rate of plaque per unit of time in hours; percentage area/hour), and area time index (the time in days needed to achieve a plaque area of 100% concerning the initial area at the same moment). Images and graphics can be obtained for a moment from a patient in addition to a full report presenting all the processing data. Dentistry experts evaluated the DenTiUS Plaque software through a usability test, with the best-scoring questions those related to the workflow efficiency, value of the online help, attractiveness of the user interface, and overall satisfaction. Conclusions: The DenTiUS Plaque software allows automatic, reliable, and repeatable quantification of dental plaque levels, providing information about area, intensity, and growth pattern. Dentistry experts recognized that this software is suitable for quantification of dental plaque levels. Consequently, its application in the analysis of plaque evolution patterns associated with different oral conditions, as well as to evaluate the effectiveness of various oral hygiene measures, can represent an improvement in the clinical setting and the methodological quality of research studiesThis work received financial support from Johnson & Johnson company (Grant 2016‐CE219), Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2019-2022 ED431G-2019/04, 2017-2020 Potential Growth Group ED431B 2017/029, 2017-2020 Competitive Reference Group ED431C 2017/69, and N. Vila-Blanco support ED481A-2017), and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University SystemS

XAS: Automatic yet eXplainable Age and Sex determination by combining imprecise per-tooth predictions

Chronological age and biological sex estimation are two key tasks in a variety of procedures, including human identification and migration control. Issues such as these have led to the development of both semiautomatic and automatic prediction models, but the former are expensive in terms of time and human resources, while the latter lack the interpretability required to be applicable in real-life scenarios. This paper therefore proposes a new, fully automatic methodology for the estimation of age and sex. This first applies a tooth detection by means of a modified CNN with the objective of extracting the oriented bounding boxes of each tooth. Then, it feeds the image features inside the tooth boxes into a second CNN module designed to produce per-tooth age and sex probability distributions. The method then adopts an uncertainty-aware policy to aggregate these estimated distributions. Our approach yielded a lower mean absolute error than any other previously described, at 0.97 years. The accuracy of the sex classification was 91.82%, confirming the suitability of the teeth for this purpose. The proposed model also allows analyses of age and sex estimations on every tooth, enabling experts to identify the most relevant for each task or population cohort or to detect potential developmental problems. In conclusion, the performance of the method in both age and sex predictions is excellent and has a high degree of interpretability, making it suitable for use in a wide range of application scenariosS

Deep Neural Networks for Chronological Age Estimation From OPG Images

Chronological age estimation is crucial labour in many clinical procedures, where the teeth have proven to be one of the best estimators. Although some methods to estimate the age from tooth measurements in orthopantomogram (OPG) images have been developed, they rely on time-consuming manual processes whose results are affected by the observer subjectivity. Furthermore, all those approaches have been tested only on OPG image sets of good radiological quality without any conditioning dental characteristic. In this work, two fully automatic methods to estimate the chronological age of a subject from the OPG image are proposed. The first (DANet) consists of a sequential Convolutional Neural Network (CNN) path to predict the age, while the second (DASNet) adds a second CNN path to predict the sex and uses sex-specific features with the aim of improving the age prediction performance. Both methods were tested on a set of 2289 OPG images of subjects from 4.5 to 89.2 years old, where both bad radiological quality images and images showing conditioning dental characteristics were not discarded. The results showed that the DASNet outperforms the DANet in every aspect, reducing the median Error (E) and the median Absolute Error (AE) by about 4 months in the entire database. When evaluating the DASNet in the reduced datasets, the AE values decrease as the real age of the subjects decreases, until reaching a median of about 8 months in the subjects younger than 15. The DASNet method was also compared to the state-of-the-art manual age estimation methods, showing significantly less over- or under-estimation problems. Consequently, we conclude that the DASNet can be used to automatically predict the chronological age of a subject accurately, especially in young subjects with developing dentitionsThis work was supported in part by the Consellería de Cultura, Educación e Ordenación Universitaria under Grant ED431G/08, in part by the Potential Growth Group ED431B 2017/029, in part by the Competitive Reference Group ED431C 2017/69, in part by the N Vila-Blanco Support ED481A-2017, and in part by the European Regional Development Fund (ERDF)S

Improving zebrafish embryo xenotransplantation conditions by increasing incubation temperature and establishing a proliferation index with ZFtool

Background Zebrafish (Danio rerio) is a model organism that has emerged as a tool for cancer research, cancer being the second most common cause of death after cardiovascular disease for humans in the developed world. Zebrafish is a useful model for xenotransplantation of human cancer cells and toxicity studies of different chemotherapeutic compounds in vivo. Compared to the murine model, the zebrafish model is faster, can be screened using high-throughput methods and has a lower maintenance cost, making it possible and affordable to create personalized therapies. While several methods for cell proliferation determination based on image acquisition and quantification have been developed, some drawbacks still remain. In the xenotransplantation technique, quantification of cellular proliferation in vivo is critical to standardize the process for future preclinical applications of the model. Methods This study improved the conditions of the xenotransplantation technique – quantification of cellular proliferation in vivo was performed through image processing with our ZFtool software and optimization of temperature in order to standardize the process for a future preclinical applications. ZFtool was developed to establish a base threshold that eliminates embryo auto-fluorescence and measures the area of marked cells (GFP) and the intensity of those cells to define a ‘proliferation index’. Results The analysis of tumor cell proliferation at different temperatures (34 °C and 36 °C) in comparison to in vitro cell proliferation provides of a better proliferation rate, achieved as expected at 36°, a maintenance temperature not demonstrated up to now. The mortality of the embryos remained between 5% and 15%. 5- Fluorouracil was tested for 2 days, dissolved in the incubation medium, in order to quantify the reduction of the tumor mass injected. In almost all of the embryos incubated at 36 °C and incubated with 5-Fluorouracil, there was a significant tumor cell reduction compared with the control group. This was not the case at 34 °C. Conclusions Our results demonstrate that the proliferation of the injected cells is better at 36 °C and that this temperature is the most suitable for testing chemotherapeutic drugs like the 5-FluorouracilThis research was funded by the Fondo de Investigación Sanitaria (Instituto Carlos III) - FIS project (PI13/01388). The funding body had no role in the design of the study and collection, analysis, and interpretation of data and in writing of this manuscriptS

Fully Automatic Teeth Segmentation in Adult OPG Images

In this work, the problem of segmenting teeth in panoramic dental images is addressed. The Random Forest Regression Voting Constrained Local Models (RFRV-CLM) are used to perform the segmentation in two steps. Firstly, a set of mandible and teeth keypoints are located, and then that points are used to initialise each individual tooth model. A method to detect missing teeth based on the quality of fit is presented. The system is evaluated using 346 manually annotated images containing adult-stage teeth. Encouraging results on detecting missing teeth are achieved. The system is able to locate the outline of the teeth to a median point-to-curve error of 0.2 mm

Automated description of the mandible shape by deep learning

Purpose: The shape of the mandible has been analyzed in a variety of fields, whether to diagnose conditions like osteoporosis or osteomyelitis, in forensics, to estimate biological information such as age, gender, and race or in orthognathic surgery. Although the methods employed produce encouraging results, most rely on the dry bone analyses or complex imaging techniques that, ultimately, hamper sample collection and, as a consequence, the development of large-scale studies. Thus, we proposed an objective, repeatable, and fully automatic approach to provide a quantitative description of the mandible in orthopantomographies (OPGs). Methods: We proposed the use of a deep convolutional neural network (CNN) to localize a set of landmarks of the mandible contour automatically from OPGs. Furthermore, we detailed four different descriptors for the mandible shape to be used for a variety of purposes. This includes a set of linear distances and angles calculated from eight anatomical landmarks of the mandible, the centroid size, the shape variations from the mean shape, and a group of shape parameters extracted with a point distribution model. Results: The fully automatic digitization of the mandible contour was very accurate, with a mean point to the curve error of 0.21 mm and a standard deviation comparable to that of a trained expert. The combination of the CNN and the four shape descriptors was validated in the well-known problems of forensic sex and age estimation, obtaining 87.8% of accuracy and a mean absolute error of 1.57 years, respectivelyOpen Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This work has received financial support from Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2019-2022 ED431G-2019/04 and Group with Growth Potential ED431B 2020-2022 GPC2020/27) and the European Regional Development Fund (ERDF), which acknowledges the CiTIUS-Research Center in Intelligent Technologies of the University of Santiago de Compostela as a Research Center of the Galician University SystemS