Conclusion : Identification of dental implants using deep learning|palmerah.implantgigi.com

Conclusion

Though there are several issues that still need to be addressed, implant systems can be identified from panoramic radiographic images using deep learning-based object detection. To increase the learning performance and apply this system in clinical practice, a higher quality and larger number of implant images and images of other implants will be needed in subsequent studies.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available because the panoramic radiographs used in this study can be used only in the hospital.

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Acknowledgements

The authors would like to thank the staff of the Department of Prosthodontics, Gerodontology and Oral Rehabilitation, Osaka University Graduate School of Dentistry.

Funding

The authors declare that they have no funding for this study.

Author information

Affiliations

Department of Prosthodontics, Gerodontology and Oral Rehabilitation, Osaka University Graduate School of Dentistry, 1-8 Yamadaoka, Suita, Osaka, 565-0871, Japan

Toshihito Takahashi, Tomoya Gonda, Tomoaki Mameno, Masahiro Wada & Kazunori Ikebe
Division of Medical Information, Osaka University Dental Hospital, 1-8 Yamadaoka, Suita, Osaka, 565-0871, Japan

Kazunori Nozaki

Contributions

TT, KN, and TM analyzed the data and build and modify the programing algorithm. TT, TG, TM, and MW collected the data and made the research plan for this study. TT, KN, MW, and KI were major contributors in writing the manuscript. The author(s) read and approved the final manuscript.