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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2020.1
- 수록면
- 148 - 181 (34page)
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초록· 키워드
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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.
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등록된 정보가 없습니다.
참고문헌 (85)
참고문헌 신청
Hype cycle / Gartner
Krizhevsky A / 2012 / ImageNet classification with deep convolutional neural networks / Advances in neural information processing systems :1097~1105
Fukushima K / 1980 / Neocognitron : a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position / Biol Cybern 36:193~202
Hubel DH / 1959 / Receptive fields of single neurones in the cat’s striate cortex / J Physiol 148:574~591
LeCun Y / 1998 / Gradient-based learning applied to document recognition / Proc IEEE 86:2278~2323
Krizhevsky A / 2012 / ImageNet classification with deep convolutional neural networks / Advances in neural information processing systems :1097~1105
Fukushima K / 1980 / Neocognitron : a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position / Biol Cybern 36:193~202
Hubel DH / 1959 / Receptive fields of single neurones in the cat’s striate cortex / J Physiol 148:574~591
LeCun Y / 1998 / Gradient-based learning applied to document recognition / Proc IEEE 86:2278~2323
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