A novel semi-supervised fuzzy clustering method based on interactive fuzzy satisficing for dental x-ray image segmentation

Research and application of artificial intelligence in dentistry from lower-middle income countries - a scoping review

Artificial intelligence (AI) has been integrated into dentistry for improvement of current dental practice. While many studies have explored the utilization of AI in various fields, the potential of AI in dentistry, particularly in low-middle income countries (LMICs) remains understudied. This scoping review aimed to study the existing literature on the applications of artificial intelligence in dentistry in low-middle income countries. A comprehensive search strategy was applied utilizing three major databases: PubMed, Scopus, and EBSCO Dentistry & Oral Sciences Source. The search strategy included keywords related to AI, Dentistry, and LMICs. The initial search yielded a total of 1587, out of which 25 articles were included in this review. Our findings demonstrated that limited studies have been carried out in LMICs in terms of AI and dentistry. Most of the studies were related to Orthodontics. In addition gaps in literature were noted such as cost utility and patient experience were not mentioned in the included studies.

Point Tracking Technology of Sports Image Sequence Marks Based on Fuzzy Clustering Algorithm

Fuzzy clustering algorithms have received widespread attention in various fields. Point tracking technology has significant application importance in sports image data analysis. In order to solve the problem of limited tracking performance caused by the fuzzy and rough division of moving image edges, this paper proposes a point tracking technology based on a fuzzy clustering algorithm, which is used for the point tracking of moving image sequence signs. This article analyzes the development status of sports image sequence analysis and processing technology and introduces some basic theories about fuzzy clustering algorithms. On the basis of the fuzzy clustering algorithm, the positioning and tracking of the marker points of the moving image sequence are studied. A series of experiments have proved that the fuzzy clustering algorithm can improve the recognition rate of the landmark points of the moving image. For the detection and tracking of moving targets, the fuzzy clustering algorithm can reach the limit faster under the same number of iterations, and the image noise can be reduced to 60% of the original by 5 iterations. This has excellent development value in application.

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.

Applications of artificial intelligence in dentistry: A comprehensive review

OBJECTIVE

To perform a comprehensive review of the use of artificial intelligence (AI) and machine learning (ML) in dentistry, providing the community with a broad insight on the different advances that these technologies and tools have produced, paying special attention to the area of esthetic dentistry and color research.

MATERIALS AND METHODS

The comprehensive review was conducted in MEDLINE/PubMed, Web of Science, and Scopus databases, for papers published in English language in the last 20 years.

RESULTS

Out of 3871 eligible papers, 120 were included for final appraisal. Study methodologies included deep learning (DL; n = 76), fuzzy logic (FL; n = 12), and other ML techniques (n = 32), which were mainly applied to disease identification, image segmentation, image correction, and biomimetic color analysis and modeling.

CONCLUSIONS

The insight provided by the present work has reported outstanding results in the design of high-performance decision support systems for the aforementioned areas. The future of digital dentistry goes through the design of integrated approaches providing personalized treatments to patients. In addition, esthetic dentistry can benefit from those advances by developing models allowing a complete characterization of tooth color, enhancing the accuracy of dental restorations.

CLINICAL SIGNIFICANCE

The use of AI and ML has an increasing impact on the dental profession and is complementing the development of digital technologies and tools, with a wide application in treatment planning and esthetic dentistry procedures.

Descriptive analysis of dental X-ray images using various practical methods: A review

In dentistry, practitioners interpret various dental X-ray imaging modalities to identify tooth-related problems, abnormalities, or teeth structure changes. Another aspect of dental imaging is that it can be helpful in the field of biometrics. Human dental image analysis is a challenging and time-consuming process due to the unspecified and uneven structures of various teeth, and hence the manual investigation of dental abnormalities is at par excellence. However, automation in the domain of dental image segmentation and examination is essentially the need of the hour in order to ensure error-free diagnosis and better treatment planning. In this article, we have provided a comprehensive survey of dental image segmentation and analysis by investigating more than 130 research works conducted through various dental imaging modalities, such as various modes of X-ray, CT (Computed Tomography), CBCT (Cone Beam Computed Tomography), etc. Overall state-of-the-art research works have been classified into three major categories, i.e., image processing, machine learning, and deep learning approaches, and their respective advantages and limitations are identified and discussed. The survey presents extensive details of the state-of-the-art methods, including image modalities, pre-processing applied for image enhancement, performance measures, and datasets utilized.

A New Integrated Approach Based on the Iterative Super-Resolution Algorithm and Expectation Maximization for Face Hallucination

This paper proposed and verified a new integrated approach based on the iterative super-resolution algorithm and expectation-maximization for face hallucination, which is a process of converting a low-resolution face image to a high-resolution image. The current sparse representation for super resolving generic image patches is not suitable for global face images due to its lower accuracy and time-consumption. To solve this, in the new method, training global face sparse representation was used to reconstruct images with misalignment variations after the local geometric co-occurrence matrix. In the testing phase, we proposed a hybrid method, which is a combination of the sparse global representation and the local linear regression using the Expectation Maximization (EM) algorithm. Therefore, this work recovered the high-resolution image of a corresponding low-resolution image. Experimental validation suggested improvement of the overall accuracy of the proposed method with fast identification of high-resolution face images without misalignment.

Correction of Cupping Artifacts in Axial Cone-Beam Computed Tomography Images by Using Image Processing Algorithms

Objectives:

One of the most important problems of cone-beam computed tomography (CBCT) imaging technique is the presence of dense objects, such as implants, amalgam fillings, and metal veneers, which result in beam-hardening artifacts. With an increase in the application of CBCT images and considering the problems in relation to cupping artifacts, some algorithms have been presented to reduce these artifacts. The aim was to present an algorithm to eliminate cupping artifacts from axial and other reconstructed CBCT images.

Materials and Methods:

We used CBCT images of NewTom VG imaging system (Verona, Italy, at Dentistry Faculty, Medical Sciences University, Tabriz, Iran) in which every image has a resolution of 366 × 320 in DICOM format. 50 images of patients with cupping artifacts were selected. Using Sobel edge detector and nonlinear gamma correction coefficient, the difference was calculated between the density of axial images in the main image and the image resulting from nonlinear gamma correction at the exact location of the radiopaque dental materials detected by Sobel. The points at which this density difference was out of a definite limit were treated as image artifacts and were eliminated from the main image by the inpainting method.

Results:

The resultant axial images, for producing reconstructed cross-sectional, panoramic images without cupping artifacts, were imported into NTT viewer V5.6 and utilized.

Conclusions:

With comparison, acquired images observed that the offering algorithm is practical and effective for reducing the cupping artifacts and preserving the quality of the reconstructed images. This algorithm does not need any additional equipment.

Multi-Attribute Multi-Perception Decision-Making Based on Generalized T-Spherical Fuzzy Weighted Aggregation Operators on Neutrosophic Sets

The framework of the T-spherical fuzzy set is a recent development in fuzzy set theory that can describe imprecise events using four types of membership grades with no restrictions. The purpose of this manuscript is to point out the limitations of the existing intuitionistic fuzzy Einstein averaging and geometric operators and to develop some improved Einstein aggregation operators. To do so, first some new operational laws were developed for T-spherical fuzzy sets and their properties were investigated. Based on these new operations, two types of Einstein aggregation operators are proposed namely the Einstein interactive averaging aggregation operators and the Einstein interactive geometric aggregation operators. The properties of the newly developed aggregation operators were then investigated and verified. The T-spherical fuzzy aggregation operators were then applied to a multi-attribute decision making (MADM) problem related to the degree of pollution of five major cities in China. Actual datasets sourced from the UCI Machine Learning Repository were used for this purpose. A detailed study was done to determine the most and least polluted city for different perceptions for different situations. Several compliance tests were then outlined to test and verify the accuracy of the results obtained via our proposed decision-making algorithm. It was proved that the results obtained via our proposed decision-making algorithm was fully compliant with all the tests that were outlined, thereby confirming the accuracy of the results obtained via our proposed method.

A note on the marker-based watershed method for X-ray image segmentation

BACKGROUND AND OBJECTIVE

A relatively new marker-based watershed method was proposed with international readership circulation. In this letter, we put forward a note on the use of max and min symbols in their study.

METHODS

We deduced the mathematical equations in their study.

RESULTS

We found the ``min'' operation should be changed to ``max'' in original Eqs. (1) and (5).

CONCLUSIONS

In spite of the two minor problems, the paper contains many important academic points.