Deep neural networks for dental implant system classification

Shintaro Sukegawa, Kazumasa Yoshii, Takeshi Hara, Katsusuke Yamashita, Keisuke Nakano, Norio Yamamoto, Hitoshi Nagatsuka, Yoshihiko Furuki

Research output: Contribution to journalArticlepeer-review

51 Citations (Scopus)


In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images.

Original languageEnglish
Article number984
Pages (from-to)1-13
Number of pages13
Issue number7
Publication statusPublished - Jul 2020


  • Artificial intelligence
  • Classification
  • Convolutional neural networks
  • Deep learning
  • Dental implant

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology


Dive into the research topics of 'Deep neural networks for dental implant system classification'. Together they form a unique fingerprint.

Cite this