Automatic computer-aided caries detection from dental x-ray images using intelligent level set

Dental diseases have high risk of affection across the globe and mostly in adult population. The analysis of dental X-ray images has some difficulties in comparison to other medical images, which makes segmentation a more challenging process. One of the most important and yet largely unsolved issues in the level set method framework is the efficiency of signed force, speed function and initial contour (IC) generation. In this paper, a new segmentation method based on level set (LS) is proposed in two phases; IC generation using morphological information of image and intelligent level set segmentation utilizing motion filtering and back propagation neural network. The segmentation results are efficient and accurate as compared to other studies. The new approach to isolate each segmented teeth image is proposed by employing integral projection technique and feature map designed for each tooth to extract the local information and therefore to detect caries area. The achieved overall performance of the proposed segmentation method was evaluated at 120 periapical dental radiograph (X-ray), with images at 90% and the detection accuracy of 98%

Applications of artificial intelligence in dentistry: A comprehensive review

This work was funded by the Spanish Ministry of Sciences, Innovation and Universities under Projects RTI2018-101674-B-I00 and PGC2018-101904-A-100, University of Granada project A.TEP. 280.UGR18, I+D+I Junta de Andalucia 2020 project P20-00200, and Fapergs/Capes do Brasil grant 19/25510000928-3. Funding for open-access charge: Universidad de Granada/CBUAObjective: 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.Spanish Ministry of Sciences, Innovation and Universities RTI2018-101674-B-I00 PGC2018-101904-A-100University of Granada project A.TEP. 280.UGR18Junta de Andalucia P20-00200Fapergs/Capes do Brasil grant 19/25510000928-3Universidad de Granada/CBU

Automated Teeth Extraction and Dental Caries Detection in Panoramic X-ray

Dental caries is one of the most chronic diseases that involves the majority of people at least once during their lifetime. This expensive disease accounts for 5-10% of the healthcare budget in developing countries. Caries lesions appear as the result of dental biofi lm metabolic activity, caused by bacteria (most prominently Streptococcus mutans) feeding on uncleaned sugars and starches in oral cavity. Also known as tooth decay, they are primarily diagnosed by general dentists solely based on clinical assessments. Since in many cases dental problems cannot be detected with simple observations, dental x-ray imaging is introduced as a standard tool for domain experts, i.e. dentists and radiologists, to distinguish dental diseases, such as proximal caries. Among different dental radiography methods, Panoramic or Orthopantomogram (OPG) images are commonly performed as the initial step toward assessment. OPG images are captured with a small dose of radiation and can depict the entire patient dentition in a single image. Dental caries can sometimes be hard to identify by general dentists relying only on their visual inspection using dental radiography. Tooth decays can easily be misinterpreted as shadows due to various reasons, such as low image quality. Besides, OPG images have poor quality and structures are not presented with strong edges due to low contrast, uneven exposure, etc. Thus, disease detection is a very challenging task using Panoramic radiography. With the recent development of Artificial Intelligence (AI) in dentistry, and with the introduction of Convolutional Neural Network (CNN) for image classification, developing medical decision support systems is becoming a topic of interest in both academia and industry. Providing more accurate decision support systems using CNNs to assist dentists can enhance their diagnosis performance, resulting in providing improved dental care assistance for patients. In the following thesis, the first automated teeth extraction system for Panoramic images, using evolutionary algorithms, is proposed. In contrast to other intraoral radiography methods, Panoramic is captured with x-ray film outside the patient mouth. Therefore, Panoramic x-rays contain regions outside of the jaw, which make teeth segmentation extremely difficult. Considering that we solely need an image of each tooth separately to build a caries detection model, segmentation of teeth from the OPG image is essential. Due to the absence of significant pixel intensity difference between different regions in OPG radiography, teeth segmentation becomes very hard to implement. Consequently, an automated system is introduced to get an OPG as input and gives images of single teeth as the output. Since only a few research studies are utilizing similar task for Panoramic radiography, there is room for improvement. A genetic algorithm is applied along with different image processing methods to perform teeth extraction by jaw extraction, jaw separation, and teeth-gap valley detection, respectively. The proposed system is compared to the state-of-the-art in teeth extraction on other image types. After teeth are segmented from each image, a model based on various untrained and pretrained CNN-based architectures is proposed to detect dental caries for each tooth. Autoencoder-based model along with famous CNN architectures are used for feature extraction, followed by capsule networks to perform classification. The dataset of Panoramic x-rays is prepared by the authors, with help from an expert radiologist to provide labels. The proposed model has demonstrated an acceptable detection rate of 86.05%, and an increase in caries detection speed. Considering the challenges of performing such task on low quality OPG images, this work is a step towards developing a fully automated efficient caries detection model to assist domain experts

Automated Teeth Extraction and Dental Caries Detection in Panoramic X-ray

Dental caries is one of the most chronic diseases that involves the majority of people at least once during their lifetime. This expensive disease accounts for 5-10% of the healthcare budget in developing countries. Caries lesions appear as the result of dental biofi lm metabolic activity, caused by bacteria (most prominently Streptococcus mutans) feeding on uncleaned sugars and starches in oral cavity. Also known as tooth decay, they are primarily diagnosed by general dentists solely based on clinical assessments. Since in many cases dental problems cannot be detected with simple observations, dental x-ray imaging is introduced as a standard tool for domain experts, i.e. dentists and radiologists, to distinguish dental diseases, such as proximal caries. Among different dental radiography methods, Panoramic or Orthopantomogram (OPG) images are commonly performed as the initial step toward assessment. OPG images are captured with a small dose of radiation and can depict the entire patient dentition in a single image. Dental caries can sometimes be hard to identify by general dentists relying only on their visual inspection using dental radiography. Tooth decays can easily be misinterpreted as shadows due to various reasons, such as low image quality. Besides, OPG images have poor quality and structures are not presented with strong edges due to low contrast, uneven exposure, etc. Thus, disease detection is a very challenging task using Panoramic radiography. With the recent development of Artificial Intelligence (AI) in dentistry, and with the introduction of Convolutional Neural Network (CNN) for image classification, developing medical decision support systems is becoming a topic of interest in both academia and industry. Providing more accurate decision support systems using CNNs to assist dentists can enhance their diagnosis performance, resulting in providing improved dental care assistance for patients. In the following thesis, the first automated teeth extraction system for Panoramic images, using evolutionary algorithms, is proposed. In contrast to other intraoral radiography methods, Panoramic is captured with x-ray film outside the patient mouth. Therefore, Panoramic x-rays contain regions outside of the jaw, which make teeth segmentation extremely difficult. Considering that we solely need an image of each tooth separately to build a caries detection model, segmentation of teeth from the OPG image is essential. Due to the absence of significant pixel intensity difference between different regions in OPG radiography, teeth segmentation becomes very hard to implement. Consequently, an automated system is introduced to get an OPG as input and gives images of single teeth as the output. Since only a few research studies are utilizing similar task for Panoramic radiography, there is room for improvement. A genetic algorithm is applied along with different image processing methods to perform teeth extraction by jaw extraction, jaw separation, and teeth-gap valley detection, respectively. The proposed system is compared to the state-of-the-art in teeth extraction on other image types. After teeth are segmented from each image, a model based on various untrained and pretrained CNN-based architectures is proposed to detect dental caries for each tooth. Autoencoder-based model along with famous CNN architectures are used for feature extraction, followed by capsule networks to perform classification. The dataset of Panoramic x-rays is prepared by the authors, with help from an expert radiologist to provide labels. The proposed model has demonstrated an acceptable detection rate of 86.05%, and an increase in caries detection speed. Considering the challenges of performing such task on low quality OPG images, this work is a step towards developing a fully automated efficient caries detection model to assist domain experts

Odontology & artificial intelligence

Neste trabalho avaliam-se os três fatores que fizeram da inteligência artificial uma tecnologia essencial hoje em dia, nomeadamente para a odontologia: o desempenho do computador, Big Data e avanços algorítmicos. Esta revisão da literatura avaliou todos os artigos publicados na PubMed até Abril de 2019 sobre inteligência artificial e odontologia. Ajudado com inteligência artificial, este artigo analisou 1511 artigos. Uma árvore de decisão (If/Then) foi executada para selecionar os artigos mais relevantes (217), e um algoritmo de cluster k-means para resumir e identificar oportunidades de inovação. O autor discute os artigos mais interessantes revistos e compara o que foi feito em inovação durante o International Dentistry Show, 2019 em Colónia. Concluiu, assim, de forma crítica que há uma lacuna entre tecnologia e aplicação clínica desta, sendo que a inteligência artificial fornecida pela indústria de hoje pode ser considerada um atraso para o clínico de amanhã, indicando-se um possível rumo para a aplicação clínica da inteligência artificial.There are three factors that have made artificial intelligence (AI) an essential technology today: the computer performance, Big Data and algorithmic advances. This study reviews the literature on AI and Odontology based on articles retrieved from PubMed. With the help of AI, this article analyses a large number of articles (a total of 1511). A decision tree (If/Then) was run to select the 217 most relevant articles-. Ak-means cluster algorithm was then used to summarize and identify innovation opportunities. The author discusses the most interesting articles on AI research and compares them to the innovation presented during the International Dentistry Show 2019 in Cologne. Three technologies available now are evaluated and three suggested options are been developed. The author concludes that AI provided by the industry today is a hold-up for the praticioner of tomorrow. The author gives his opinion on how to use AI for the profit of patients

Automatic dental caries detection in bitewing radiographs.

Doctoral Degree. University of KwaZulu-Natal, Durban.Dental Caries is one of the most prevalent chronic disease around the globe. Distinguishing carious lesions has been a challenging task. Conventional computer aided diagnosis and detection methods in the past have heavily relied on visual inspection of teeth. These are only effective on large and clearly visible caries on affected teeth. Conventional methods have been limited in performance due to the complex visual characteristics of dental caries images, which consists of hidden or inaccessible lesions. Early detection of dental caries is an important determinant for treatment and benefits much from the introduction of new tools such as dental radiography. A method for the segmentation of teeth in bitewing X-rays is presented in this thesis, as well as a method for the detection of dental caries on X-ray images using a supervised model. The diagnostic method proposed uses an assessment protocol that is evaluated according to a set of identifiers obtained from a learning model. The proposed technique automatically detects hidden and inaccessible dental caries lesions in bitewing radio graphs. The approach employed data augmentation to increase the number of images in the data set in order to have a total of 11,114 dental images. Image pre-processing on the data set was through the use of Gaussian blur filters. Image segmentation was handled through thresholding, erosion and dilation morphology, while image boundary detection was achieved through active contours method. Furthermore, the deep learning based network through the sequential model in Keras extracts features from the images through blob detection. Finally, a convexity threshold value of 0.9 is introduced to aid in the classification of caries as either present or not present. The relative efficacy of the supervised model in diagnosing dental caries when compared to current systems is indicated by the results detailed in this thesis. The proposed model achieved a 97% correct diagnostic which proved quite competitive with existing models.Author's Publications are listed on page 4 of this thesis

Application of artificial intelligence in the dental field : A literature review

Purpose: The purpose of this study was to comprehensively review the literature regarding the application of artificial intelligence (AI) in the dental field, focusing on the evaluation criteria and architecture types. Study selection: Electronic databases (PubMed, Cochrane Library, Scopus) were searched. Full-text articles describing the clinical application of AI for the detection, diagnosis, and treatment of lesions and the AI method/architecture were included. Results: The primary search presented 422 studies from 1996 to 2019, and 58 studies were finally selected. Regarding the year of publication, the oldest study, which was reported in 1996, focused on “oral and maxillofacial surgery.” Machine-learning architectures were employed in the selected studies, while approximately half of them (29/58) employed neural networks. Regarding the evaluation criteria, eight studies compared the results obtained by AI with the diagnoses formulated by dentists, while several studies compared two or more architectures in terms of performance. The following parameters were employed for evaluating the AI performance: accuracy, sensitivity, specificity, mean absolute error, root mean squared error, and area under the receiver operating characteristic curve. Conclusion: Application of AI in the dental field has progressed; however, the criteria for evaluating the efficacy of AI have not been clarified. It is necessary to obtain better quality data for machine learning to achieve the effective diagnosis of lesions and suitable treatment planning