Implantology.  2020 Sep;24(3):148-181. 10.32542/implantology.202015.

Application of Deep Learning in Dentistry and Implantology

  • 1Clinical Assistant Professor, Department of Periodontology, Dankook University College of Dentistry, Cheonan, Korea
  • 2Associate Dean, Department of Maxillo-Stomatology, Vietnam National University School of Medicine and Pharmacy, Hanoi, Vietnam
  • 3Associate Professor, Department of Periodontology, Dankook University College of Dentistry, Cheonan, Korea


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.


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