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      Convolutional-neural-network-based radiographs evaluation assisting in early diagnosis of the periodontal bone loss via periapical radiograph

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          Abstract

          Background/Purpose

          The preciseness of detecting periodontal bone loss is examiners dependent, and this leads to low reliability. The need for automated assistance systems on dental radiographic images has been increased. To the best of our knowledge, no studies have quantitatively and automatically staged periodontitis using dental periapical radiographs. The purpose of this study was to evaluate periodontal bone loss and periodontitis stage on dental periapical radiographs using deep convolutional neural networks (CNNs).

          Materials and methods

          336 periapical radiographic images (teeth: 390) between January 2017 and December 2019 were collected and de-identified. All periapical radiographic image datasets were divided into training dataset (n = 82, teeth: 123) and test dataset (n = 336, teeth: 390). For creating an optimal deep CNN algorithm model, the training datasets were directly used for the segmentation and individual tooth detection. To evaluate the diagnostic power, we calculated the degree of alveolar bone loss deviation between our proposed method and ground truth, the Pearson correlation coefficients (PCC), and the diagnostic accuracy of the proposed method in the test datasets.

          Results

          The periodontal bone loss degree deviation between our proposed method and the ground truth drawn by the three periodontists was 6.5 %. In addition, the overall PCC value of our proposed system and the periodontists’ diagnoses was 0.828 ( P < 0.01). The total diagnostic accuracy of our proposed method was 72.8 %. The diagnostic accuracy was highest for stage III (97.0 %).

          Conclusion

          This tool helps with diagnosis and prevents omission, and this may be especially helpful for inexperienced younger doctors and doctors in underdeveloped countries. It could also dramatically reduce the workload of clinicians and timely access to periodontist care for people requiring advanced periodontal treatment.

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          Most cited references35

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          Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

          State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available. Extended tech report
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            Staging and grading of periodontitis: Framework and proposal of a new classification and case definition

            Authors were assigned the task to develop case definitions for periodontitis in the context of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions. The aim of this manuscript is to review evidence and rationale for a revision of the current classification, to provide a framework for case definition that fully implicates state-of-the-art knowledge and can be adapted as new evidence emerges, and to suggest a case definition system that can be implemented in clinical practice, research and epidemiologic surveillance.
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              Periodontitis: Consensus report of workgroup 2 of the 2017 World Workshop on the Classification of Periodontal and Peri-Implant Diseases and Conditions: Classification and case definitions for periodontitis

              A new periodontitis classification scheme has been adopted, in which forms of the disease previously recognized as "chronic" or "aggressive" are now grouped under a single category ("periodontitis") and are further characterized based on a multi-dimensional staging and grading system. Staging is largely dependent upon the severity of disease at presentation as well as on the complexity of disease management, while grading provides supplemental information about biological features of the disease including a history-based analysis of the rate of periodontitis progression; assessment of the risk for further progression; analysis of possible poor outcomes of treatment; and assessment of the risk that the disease or its treatment may negatively affect the general health of the patient. Necrotizing periodontal diseases, whose characteristic clinical phenotype includes typical features (papilla necrosis, bleeding, and pain) and are associated with host immune response impairments, remain a distinct periodontitis category. Endodontic-periodontal lesions, defined by a pathological communication between the pulpal and periodontal tissues at a given tooth, occur in either an acute or a chronic form, and are classified according to signs and symptoms that have direct impact on their prognosis and treatment. Periodontal abscesses are defined as acute lesions characterized by localized accumulation of pus within the gingival wall of the periodontal pocket/sulcus, rapid tissue destruction and are associated with risk for systemic dissemination.
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                Author and article information

                Contributors
                Journal
                J Dent Sci
                J Dent Sci
                Journal of Dental Sciences
                Association for Dental Sciences of the Republic of China
                1991-7902
                2213-8862
                12 October 2023
                January 2024
                12 October 2023
                : 19
                : 1
                : 550-559
                Affiliations
                [a ]Division of Periodontology, Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
                [b ]Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
                [c ]Division of Family Dentistry, Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
                [d ]Department of Dentistry, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan
                [e ]Division of Prosthodontics, Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
                [f ]School of Dentistry, College of Dental Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan
                [g ]Department of Computer Science, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
                Author notes
                []Corresponding author. Division of Prosthodontics, Department of Dentistry, Kaohsiung Medical University Hospital, Kaohsiung Medical University No. 100, Shih-Chuan 1st Road, Kaohsiung 807, Taiwan. dujekang@ 123456gmail.com
                [∗∗ ]Corresponding author. Department of Computer Science, National Yang Ming Chiao Tung University, No. 1001, Daxue Rd. East Dist., Hsinchu City 300093, Taiwan. wangts@ 123456cs.nctu.edu.tw
                Article
                S1991-7902(23)00334-3
                10.1016/j.jds.2023.09.032
                10829720
                38303886
                961fc039-6f06-45bf-94cb-7231cd420e0b
                © 2023 Association for Dental Sciences of the Republic of China. Publishing services by Elsevier B.V.

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 4 September 2023
                : 30 September 2023
                Categories
                Original Article

                artificial intelligence,periodontitis,convolutional neural networks,periodontal bone loss,classification

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