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Classification of Approximal Caries in Bitewing Radiographs Using Convolutional Neural Networks

  Sensors 2021 , 21 (15), 5192; (registering DOI) Received: 11 June 2021 / Revised: 21 July 2021 / Accepted: 22 July 2021 / Published: 31 July 2021 (This article belongs to the Special Issue Artificial Intelligence & Robotics in Dental Medicine ) Download PDF Citation Export Abstract Dental caries is an extremely common problem in dentistry that affects a significant part of the population. Approximal caries are especially difficult to identify because their position makes clinical analysis difficult. Radiographic evaluation—more specifically, bitewing images—are mostly used in such cases. However, incorrect interpretations may interfere with the diagnostic process. To aid dentists in caries evaluation, computational methods and tools can be used. In this work, we propose a new method that combines image processing techniques and convolutional neural networks to identify approximal dental caries in bitewing radiogr

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