Study of edge detection task in dental panoramic radiographs

A Successful Non-surgical Management of a Type II Dens Invaginatus with Antimicrobial Photodynamic Therapy: A Case Report

Endodontic therapy of dens invaginatus (DI) anomalies is challenging due to difficult access, inadequate cleaning and shaping, and incomplete disinfection of its complicated root canal system. The present case report describes the treatment of tooth #10 with tooth discomfort, intermittent pus discharge, and localized swelling. Sinus tract, mobility, and probing grade 1 were observed. The tooth was tender on palpation and percussion with negative responses to pulp sensibility tests. Radiographic assessments revealed an atypical structure of pulpal anatomy, probably dens invaginatus, associated with a large periapical lesion and severe root curvature. Cone-beam computed tomography confirmed the presence of DI type II. Finally, the diagnosis of pulp necrosis with chronic apical abscess of tooth #10 was made. Combining antimicrobial photodynamic therapy as an adjunctive treatment with different irrigation techniques were effective in nonsurgical endodontic management of the complicated DI type II in a maxillary lateral incisor with a large periradicular lesion and severe root curvature. Six-month and one-year recall radiographic images revealed asymptomatic tooth and progressive osseous healing.

Uses of Different Machine Learning Algorithms for Diagnosis of Dental Caries

Background

Dental caries is one of the major oral health problems and is increasing rapidly among people of every age (children, men, and women). Deep learning, a field of Artificial Intelligence (AI), is a growing field nowadays and is commonly used in dentistry. AI is a reliable platform to make dental care better, smoother, and time-saving for professionals. AI helps the dentistry professionals to fulfil demands of patients and to ensure quality treatment and better oral health care. AI can also help in predicting failures of clinical cases and gives reliable solutions. In this way, it helps in reducing morbidity ratio and increasing quality treatment of dental problem in population.

Objectives

The main objective of this study is to conduct a systematic review of studies concerning the association between dental caries and machine learning. The objective of this study is to design according to the PICO criteria.

Materials and Methods

A systematic search for randomized trials was conducted under the guidelines of PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). In this study, e-search was conducted from four databases including PubMed, IEEE Xplore, Science Direct, and Google Scholar, and it involved studies from year 2008 to 2022.

Result

This study fetched a total of 133 articles, from which twelve are selected for this systematic review. We analyzed different types of machine learning algorithms from which deep learning is widely used with dental caries images dataset. Neural Network Backpropagation algorithm, one of the deep learning algorithms, gives a maximum accuracy of 99%.

Conclusion

In this systematic review, we concluded how deep learning has been applied to the images of teeth to diagnose the detection of dental caries with its three types (proximal, occlusal, and root caries). Considering our findings, further well-designed studies are needed to demonstrate the diagnosis of further types of dental caries that are based on progression (chronic, acute, and arrested), which tells us about the severity of caries, virginity of lesion, and extent of caries. Apart from dental caries, AI in the future will emerge as supreme technology to detect other diseases of oral region combinedly and comprehensively because AI will easily analyze big datasets that contain multiple records.

Incremental deep learning for reflectivity data recognition in stomatology

The recognition of stomatological disorders and the classification of dental caries are important areas of biomedicine that can hugely benefit from machine learning tools for the construction of relevant mathematical models. This paper explores the possibility of using reflectivity data to distinguish between healthy tissues and caries by deep learning and multilayer convolutional neural networks. The experimental data set includes more than 700 observations recorded in the stomatology laboratory. For rigor, the results obtained from the deep learning systems are compared with those evaluated for selected sets of features estimated for each observation and classified by a decision tree, support vector machine (SVM), k-nearest neighbor, Bayesian methods, and two-layer neural networks. The classification accuracy obtained for the deep learning systems was 98.1% and 94.4% for data in the signal and spectral domains, respectively, in comparison with an accuracy of 97.2% and 87.2% evaluated by the SVM method. The proposed method conclusively demonstrates how the artificial intelligence and deep learning methodology can contribute to improved diagnosis of dental problem in stomatology.

Comparison of 2D OPG image versus orthopantomogram from 3D CBCT from the forensic point of view

Forensic dental identification has employed traditionally 2D digital radiological imaging techniques. More recently, 3D cone beam computer tomography (CBCT) data, widely applied in clinical dentistry, have been gradually used. The purpose of this study was to compare the precision and quality of 2D digital orthopantomogram (OPG) and 2D OPG images generated from cone beam computed tomography (CBCT). The study sample consisted of 50 patients with archived conventional 2D OPG and 3D CBCT images. Patients signed an informed consent form to take part in our study. Measurements of the mandible, teeth and dental restorations were taken by two observers on calibrated 2D OPG and 3D CBCT-to-OPG images using measurement functionalities of DOPLHIN software. Acquired dimensions were compared side by side and images of fillings were superimposed. For better visual comparison and more efficient image registration, the methods of spline interpolation were used. The pairs of absolute measurements obtained from conventional OPG and CBCT-to-OPG-converted images were highly correlated (p < 0.05). However, larger, and horizontally measured distances were revealed to be more affected than shorter vertically taken measurements. In relative terms, CBCT-generated width/length indices of the canines and the first molars ranged from 84% to 99.8% of those acquired from traditional OPGs. In addition, corresponding points on the teeth and fillings were compared side by side and in superimposition. The average coincidence of images was 6.1%. The results revealed that for selected metric variables 2D OPGs and 3D CBCT-generated OPGs were complementary and could be used for forensic comparisons.

Detection of the mandibular canal in orthopantomography using a Gabor-filtered anisotropic generalized Hough transform

In this paper, we explore the possibility of applying the anisotropic generalized Hough transform (AGHT) enhanced with a Gabor based time-frequency filtering (GTF) for the determination of the mandibular canal in digital dental panoramic radiographs. The proposed method is based on template matching using the fact that the shape of the mandibular canal is usually the same, followed by a filtering of the accumulator space in the Gabor domain for a precise detection of the position. The proposed procedure consists of a detailed description of the shape of the canal in its canonical form and on preserving Gabor filtering information for sorting the hierarchy of location candidates after applying anisotropically the extraction algorithm. The experimental results show that the proposed procedure is robust to recognition under occlusion and under the presence of additional structures e.g. teeth, projection errors.

Evaluation of dental morphometrics during the orthodontic treatment

BACKGROUND

Diagnostic orthodontic and prosthetic procedures commence with an initial examination, during which a number of individual findings on occlusion or malocclusion are clarified. Nowadays we try to replace standard plaster casts by scanned objects and digital models.

METHOD

Geometrically calibrated images aid in the comparison of several different steps of the treatment and show the variation of selected features belonging to individual biomedical objects. The methods used are based on geometric morphometrics, making a new approach to the evaluation of the variability of features. The study presents two different methods of measurement and shows their accuracy and reliability.

RESULTS

The experimental part of the present paper is devoted to the analysis of the dental arch objects of 24 patients before and after the treatment using the distances between the canines and premolars as the features important for diagnostic purposes. Our work proved the advantage of measuring digitalized orthodontic models over manual measuring of plaster casts, with statistically significant results and accuracy sufficient for dental practice.

CONCLUSION

A new method of computer imaging and measurements of a dental stone cast provides information with the precision required for orthodontic treatment. The results obtained point to the reduction in the variance of the distances between the premolars and canines during the treatment, with a regression coefficient RC=0.7 and confidence intervals close enough for dental practice. The ratio of these distances pointed to the nearly constant value of this measure close to 0.84 for the given set of 24 individuals.