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      Roles of Dental Care in Disaster Medicine in Japan

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          Abstract

          Purpose of Review

          Natural disasters occur frequently in Japan. A disaster medical system was rapidly developed in Japan following the Great Hanshin-Awaji Earthquake in 1995. Dentistry has become increasingly important in disaster medicine. This review summarizes the roles of dental professionals in disaster medicine, highlights relevant issues, and identifies new directions for research to improve disaster relief activities based on our previous experiences as dental professionals supporting the victims of major disasters.

          Recent Findings

          Many preventable deaths after a disaster are caused by aspiration pneumonia, which occurs against a background of factors that are compounded by a harsh living environment. An important aim of dental care in disaster medicine is to prevent these disaster-related deaths in vulnerable persons such as the elderly. This can be achieved through interventions to maintain oral hygiene, preserve and enhance oral function (i.e., chewing and swallowing), and improve the diet, since these interventions help to prevent the development of malnutrition and frailty in vulnerable people. Dental identification of disaster victims could be improved through the use of intraoral three-dimensional scanners and artificial intelligence to automate the acquisition of dental findings and through the construction of a national database of digitized dental records. Advances in personal identification methods will be needed given the prediction that a catastrophic earthquake will occur on the Nankai Trough during the next 30 years and claim more victims than the 2011 Great East Japan Earthquake.

          Summary

          Disaster-related deaths due to aspiration pneumonia can be prevented by providing appropriate dental care to those in need. The process of identifying victims could be made more efficient through the use of intraoral three-dimensional scanning, artificial intelligence, and a digital database of dental records. Establishing and strengthening relationships between professionals in different regions will help to optimize the multidisciplinary response to future large-scale disasters.

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          Most cited references41

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          Dermatologist-level classification of skin cancer with deep neural networks

          Skin cancer, the most common human malignancy, is primarily diagnosed visually, beginning with an initial clinical screening and followed potentially by dermoscopic analysis, a biopsy and histopathological examination. Automated classification of skin lesions using images is a challenging task owing to the fine-grained variability in the appearance of skin lesions. Deep convolutional neural networks (CNNs) show potential for general and highly variable tasks across many fine-grained object categories. Here we demonstrate classification of skin lesions using a single CNN, trained end-to-end from images directly, using only pixels and disease labels as inputs. We train a CNN using a dataset of 129,450 clinical images—two orders of magnitude larger than previous datasets—consisting of 2,032 different diseases. We test its performance against 21 board-certified dermatologists on biopsy-proven clinical images with two critical binary classification use cases: keratinocyte carcinomas versus benign seborrheic keratoses; and malignant melanomas versus benign nevi. The first case represents the identification of the most common cancers, the second represents the identification of the deadliest skin cancer. The CNN achieves performance on par with all tested experts across both tasks, demonstrating an artificial intelligence capable of classifying skin cancer with a level of competence comparable to dermatologists. Outfitted with deep neural networks, mobile devices can potentially extend the reach of dermatologists outside of the clinic. It is projected that 6.3 billion smartphone subscriptions will exist by the year 2021 (ref. 13) and can therefore potentially provide low-cost universal access to vital diagnostic care.
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            Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

            Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.
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              Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm

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                Author and article information

                Contributors
                yamazoe@dent.kyushu-u.ac.jp
                Journal
                Curr Oral Health Rep
                Curr Oral Health Rep
                Current Oral Health Reports
                Springer International Publishing (Cham )
                2196-3002
                25 June 2022
                : 1-8
                Affiliations
                [1 ]GRID grid.411248.a, ISNI 0000 0004 0404 8415, Section of Geriatric Dentistry and Perioperative Medicine in Dentistry, , Kyushu University Hospital, ; Fukuoka, 812-8582 Japan
                [2 ]GRID grid.177174.3, ISNI 0000 0001 2242 4849, Section of Oral Healthcare and Dentistry Cooperation, Division of Maxillofacial Diagnostic and Surgical Science, Faculty of Dental Science, , Kyushu University, ; Fukuoka, 812-8582 Japan
                [3 ]GRID grid.177174.3, ISNI 0000 0001 2242 4849, Center for Advanced Medical Innovation, , Kyushu University, ; Fukuoka, 812-8582 Japan
                [4 ]GRID grid.411152.2, ISNI 0000 0004 0407 1295, Disaster Medical Education and Research Center, , Kumamoto University Hospital, ; Kumamoto, 860-8556 Japan
                Author information
                http://orcid.org/0000-0002-3688-0615
                Article
                314
                10.1007/s40496-022-00314-z
                9244076
                35789816
                87e6b865-5a8b-44de-9dc5-e5cdd21a6ff2
                © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                : 26 May 2022
                Categories
                Oral Disease and Nutrition (F Nishimura, Section Editor)

                natural disaster,dentistry,aspiration pneumonia,dental identification,multidisciplinary team,dietary support

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