107 research outputs found

    Object Detection for Caries or Pit and Fissure Sealing Requirement in Children's First Permanent Molars

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    Dental caries is one of the most common oral diseases that, if left untreated, can lead to a variety of oral problems. It mainly occurs inside the pits and fissures on the occlusal/buccal/palatal surfaces of molars and children are a high-risk group for pit and fissure caries in permanent molars. Pit and fissure sealing is one of the most effective methods that is widely used in prevention of pit and fissure caries. However, current detection of pits and fissures or caries depends primarily on the experienced dentists, which ordinary parents do not have, and children may miss the remedial treatment without timely detection. To address this issue, we present a method to autodetect caries and pit and fissure sealing requirements using oral photos taken by smartphones. We use the YOLOv5 and YOLOX models and adopt a tiling strategy to reduce information loss during image pre-processing. The best result for YOLOXs model with tiling strategy is 72.3 mAP.5, while the best result without tiling strategy is 71.2. YOLOv5s6 model with/without tiling attains 70.9/67.9 mAP.5, respectively. We deploy the pre-trained network to mobile devices as a WeChat applet, allowing in-home detection by parents or children guardian

    Artificial Intelligence in Dentistry

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    Artificial intelligence (AI) is the branch of computer science dedicated to building systems to perform tasks that normally require human intelligence. AI tries to solve problems and learn similar to humans. The field of AI has experienced phenomenal development and growth over the past two decades; with the latest developments in digitized data collection, machine learning, and computing infrastructure, AI applications are expanding rapidly, especially in areas that are thought to be reserved for experts in their fields. Artificial intelligence has started to take place rapidly in dental clinical applications. The use of artificial intelligence in dentistry has enormous potential to improve patient care and drive major advances in healthcare. AI in dentistry is being researched for various purposes, such as identifying anatomical and pathological structures, diagnosing diseases and predicting treatment results, and selecting materials to be used. Advances in AI offer healthcare benefits, such as reducing postoperative complications, improving quality of life, and reducing the number of unnecessary procedures. It can also play a great helping role for dentists in increasing the accuracy of diagnosis. This chapter aims to explain the current applications and future predictions of artificial intelligence in dentistry, which is one of the most current topics of recent times

    Digital Workflows and Material Sciences in Dental Medicine

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    The trend of digitalization is an omnipresent phenomenon nowadays – in social life and in the dental community. Advancement in digital technology has fostered research into new dental materials for the use of these workflows, particularly in the field of prosthodontics and oral implantology.CAD/CAM-technology has been the game changer for the production of tooth-borne and implant-supported (monolithic) reconstructions: from optical scanning, to on-screen designing, and rapid prototyping using milling or 3D-printing. In this context, the continuous development and speedy progress in digital workflows and dental materials ensure new opportunities in dentistry.The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on digital workflows to determine the uptake of technological innovations in dental materials science. In addition, emphasis is placed on identifying future research needs to manage the continuous increase in digitalization in combination with dental materials and to accomplish their clinical translation.This Special Issue welcomes all types of studies and reviews considering the perspectives of the various stakeholders with regard to digital dentistry and dental materials

    Issues in Contemporary Orthodontics

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    Issues in Contemporary Orthodontics is a contribution to the ongoing debate in orthodontics, a discipline of continuous evolution, drawing from new technology and collective experience, to better meet the needs of students, residents, and practitioners of orthodontics. The book provides a comprehensive view of the major issues in orthodontics that have featured in recent debates. Abroad variety of topics is covered, including the impact of malocclusion, risk management and treatment, and innovation in orthodontics

    Automatisierte Erkennung und Kategorisierung der Molaren-Inzisiven-Hypomineralisation mit Hilfe künstlicher Intelligenz auf Fotografien von Zähnen

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    A deep learning-based convolutional neural network (CNN) could improve dental diagnostic accuracy by automated detection and categorization of molar-incisor-hypomineralisation (MIH) on intra-oral photographs. For the purpose of this study on artificial intelligence (AI), an image set consisting of 3.241 intraoral images was split into training (N = 2.596) and test set (N = 649). The overall dataset was classified into the following categories: teeth with no signs of hypomineralisation and no dental intervention (N = 767), teeth with no signs of hypominerlisation and a MIH-related “atypical” restoration (N = 76), teeth with no signs of hypomineralisation and presence of pit and fissure sealant (N = 742), teeth with hypomineralisation and intervention (N = 815), teeth with hypominerlisation and an atypical restoration (N = 158), teeth with hypomineralisation and fissure sealing (N = 181), teeth with enamel disintegration and no intervention (n = 290), teeth with enamel disintegration and an atypical restoration (N = 169) and teeth with enamel disintegration and presence of sealing material (N = 43). After the cyclic training of the convolutional neural network most dental photographs could be automatically classified with an acceptable diagnostic accuracy. Here, an overall diagnostic accuracy of 95.2% was achieved. AUC values ranged from 0.873 (enamel breakdown with a sealant) to 0.994 (atypical restoration with no MIH). It can be concluded that AI powered MIH detection and diagnostics showed promising results in the diagnostic study. However, there is a substantial need for further improvements

    A hybrid mask RCNN-based tool to localize dental cavities from real-time mixed photographic images

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    Nearly 3.5 billion humans have oral health issues, including dental caries, which requires dentist-patient exposure in oral examinations. The automated approaches identify and locate carious regions from dental images by localizing and processing either colored photographs or X-ray images taken via specialized dental photography cameras. The dentists’ interpretation of carious regions is difficult since the detected regions are masked using solid coloring and limited to a particular dental image type. The software-based automated tools to localize caries from dental images taken via ordinary cameras requires further investigation. This research provided a mixed dataset of dental photographic (colored or X-ray) images, instantiated a deep learning approach to enhance the existing dental image carious regions’ localization procedure, and implemented a full-fledged tool to present carious regions via simple dental images automatically. The instantiation mainly exploits the mixed dataset of dental images (colored photographs or X-rays) collected from multiple sources and pre-trained hybrid Mask RCNN to localize dental carious regions. The evaluations performed by the dentists showed that the correctness of annotated datasets is up to 96%, and the accuracy of the proposed system is between 78% and 92%. Moreover, the system achieved the overall satisfaction level of dentists above 80%

    SUCCESS OF ARTIFICIAL INTELLIGENCE SYSTEM IN DETERMINING ALVEOLAR BONE LOSS FROM DENTAL PANORAMIC RADIOGRAPHY IMAGES

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    Objectives: The aim of this study was to detect alveolar bone loss from dental panoramic radiographic images using artificial intelligence systems. Material and Methods: A total of 2276 panoramic radiographic images were used in this study. While 1137 of them belong to cases with bone destruction, 1139 were periodontally healthy. The dataset is divided into three parts as training (n=1856) , validation (n=210) and testing set (n= 210). All images in the data set were resized to 1472x718 pixels before training. A random sequence was created using the open-source python programming language and OpenCV, NumPy, Pandas, and Matplotlib libraries effectively. A pre-trained Google Net Inception v3 CNN network was used for preprocessing and data sets were trained using transfer learning. Diagnostic performance was evaluated with the confusion matrix using sensivitiy, specificity, precision, accuracy and F1 score. Results: Of the 105 cases with bone loss, 99 were detected by the AI system. Sensitivity was 0.94, specificity 0.88, precision 0.89, accuracy 0.91 and F1 score 0.91. Conclusion: The convolutional neural network model is successful in determining periodontal bone losses. It can be used as a system to facilitate the work of physicians in diagnosis and treatment planning in the future

    Designing Clinical Data Presentation Using Cognitive Task Analysis Methods

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    Despite the many decades of research on effective use of clinical systems in medicine, the adoption of health information technology to improve patient care continues to be slow especially in ambulatory settings. This applies to dentistry as well, a primary care discipline with approximately 137,000 practicing dentists in the United States. One critical reason is the poor usability of clinical systems, which makes it difficult for providers to navigate through the system and obtain an integrated view of patient data during patient care. Cognitive science methods have shown significant promise to meaningfully inform and formulate the design, development and assessment of clinical information systems. Most of these methods were applied to evaluate the design of systems after they have been developed. Very few studies, on the other hand, have used cognitive engineering methods to inform the design process for a system itself. It is this gap in knowledge – how cognitive engineering methods can be optimally applied to inform the system design process – that this research seeks to address through this project proposal. This project examined the cognitive processes and information management strategies used by dentists during a typical patient exam and used the results to inform the design of an electronic dental record interface. The resulting 'proof of concept' was evaluated to determine the effectiveness and efficiency of such a cognitively engineered and application flow design. The results of this study contribute to designing clinical systems that provide clinicians with better cognitive support during patient care. Such a system will contribute to enhancing the quality and safety of patient care, and potentially to reducing healthcare costs

    Revolutionizing Dental Caries Diagnosis through Artificial Intelligence

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    The diagnosis and management of dental caries, a prevalent global oral health issue, have traditionally depended on clinical examination and the interpretation of radiographic images. However, with the rapid advancements in technology, the landscape of dental diagnostics is transforming. This chapter delves into the revolutionary impact of artificial intelligence (AI) on detecting and managing dental caries. Dental professionals can now achieve enhanced diagnostic accuracy by harnessing the power of machine learning algorithms and image recognition technologies, even identifying early-stage caries that conventional methods might overlook. The integration of AI into dentistry not only promises improved patient outcomes by facilitating timely interventions and streamlining clinical workflows, potentially redefining the future of oral healthcare. While the prospects are promising, it is imperative to concurrently address the challenges and ethical considerations accompanying AI-driven diagnostics to ensure that the technology augments, rather than supplants, the expertise of dental professionals. The chapter serves as a comprehensive overview of the current state of AI in dental caries diagnosis, its potential benefits, and the road ahead
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