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      Personalized dental medicine, artificial intelligence, and their relevance for dentomaxillofacial imaging

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          Abstract

          Personalized medicine refers to the tailoring of diagnostics and therapeutics to individuals based on one’s biological, social, and behavioral characteristics. While personalized dental medicine is still far from being a reality, advanced artificial intelligence (AI) technologies with improved data analytic approaches are expected to integrate diverse data from the individual, setting, and system levels, which may facilitate a deeper understanding of the interaction of these multilevel data and therefore bring us closer to more personalized, predictive, preventive, and participatory dentistry, also known as P4 dentistry. In the field of dentomaxillofacial imaging, a wide range of AI applications, including several commercially available software options, have been proposed to assist dentists in the diagnosis and treatment planning of various dentomaxillofacial diseases, with performance similar or even superior to that of specialists. Notably, the impact of these dental AI applications on treatment decision, clinical and patient-reported outcomes, and cost-effectiveness has so far been assessed sparsely. Such information should be further investigated in future studies to provide patients, providers, and healthcare organizers a clearer picture of the true usefulness of AI in daily dental practice.

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          Highly accurate protein structure prediction with AlphaFold

          Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort 1 – 4 , the structures of around 100,000 unique proteins have been determined 5 , but this represents a small fraction of the billions of known protein sequences 6 , 7 . Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’ 8 —has been an important open research problem for more than 50 years 9 . Despite recent progress 10 – 14 , existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14) 15 , demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.
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            Approximation capabilities of multilayer feedforward networks

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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
                Journal
                Dentomaxillofac Radiol
                Dentomaxillofac Radiol
                dmfr
                Dentomaxillofacial Radiology
                The British Institute of Radiology.
                0250-832X
                1476-542X
                01 January 2023
                12 December 2022
                : 52
                : 1
                : 20220335
                Affiliations
                [1 ] org-divisionDivision of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong , Hong Kong SAR, China
                [2 ] org-divisionDivision of Oral and Maxillofacial Radiology, Applied Oral Sciences and Community Dental Care, Faculty of Dentistry, The University of Hong Kong , Hong Kong SAR, China
                [3 ] org-divisionDepartment of Oral Health & Medicine, University Center for Dental Medicine Basel UZB, University of Basel , Basel, Switzerland
                [4 ] org-divisionDepartment of Oral Diagnostics, Digital Health and Health Services Research, Charité–Universitätsmedizin Berlin , Berlin, Germany
                Author notes
                Address correspondence to: Falk Schwendicke. E-mail: falk.schwendicke@ 123456charite.de
                Address correspondence to: Kuo Feng Hung. E-mail: hungkfg@ 123456hku.hk
                Author information
                https://orcid.org/0000-0002-3971-3484
                Article
                DMFR-D-22-00335
                10.1259/dmfr.20220335
                9793453
                36472627
                9020258a-bffa-4c85-9f22-32268f8b2aa6
                © 2023 The Authors. Published by the British Institute of Radiology

                This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 Unported License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited.

                History
                : 15 October 2022
                : 08 November 2022
                : 11 November 2022
                Page count
                Figures: 1, Tables: 5, Equations: 0, References: 121, Pages: 22, Words: 14112
                Categories
                The Cutting Edge - Review Article
                dmfr, DMFR

                personalized medicine,artificial intelligence,deep learning,diagnostic imaging,dentistry

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