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Application of Deep Learning in Dentistry and Implantology

대한구강악안면임플란트학회지 2020년 24권 3호 p.148 ~ 181
강대영, Duong Hieu Pham, 박정철,
소속 상세정보
강대영 ( Kang Dae-Young ) - Dankook University College of Dentistry Department of Periodontology
 ( Duong Hieu Pham ) - Vietnam National University School of Medicine and Pharmacy Department of Maxilo-Stomatology
박정철 ( Park Jung-Chul ) - Dankook University College of Dentistry Department of Periodontology

Abstract


Artificial intelligence and deep learning algorithms are infiltrating various fields of medicine and dentistry. The purpose of the current study was to review literatures applying deep learning algorithms to the dentistry and implantology. Electronic literature search through MEDLINE and IEEE Xplore library database was performed at 2019 October by combining free-text terms and entry terms associated with ‘dentistry’ and ‘deep learning’. The searched literature was screened by title/abstract level and full text level. Following data were extracted from the included studies: information of author, publication year, the aim of the study, architecture of deep learning, input data, output data, and performance of the deep learning algorithm in the study. 340 studies were retrieved from the databases and 62 studies were included in the study. Deep learning algorithms were applied to tooth localization and numbering, detection of dental caries/periodontal disease/periapical disease/oral cancerous lesion, localization of cephalometric landmarks, image quality enhancement, prediction and compensation of deformation error in additive manufacturing of prosthesis. Convolutional neural network was used for periapical radiograph, panoramic radiograph, or computed tomography in most of included studies. Deep learning algorithms are expected to help clinicians diagnose and make decisions by extracting dental data, detecting diseases and abnormal lesions, and improving image quality

키워드

Deep learning; Dentistry; Dental implants; Machine learning; Neural networks; Radiography, Dental

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