Deep learning–based automated detection and geometric severity assessment of open gingival embrasures in intraoral photographs: A multicentre study
- DOI
- 10.1016/j.jdent.2026.107055
- Published
- 2027-01
- Container
- Journal of Dentistry
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.jdent.2026.107055,
title = {Deep learning–based automated detection and geometric severity assessment of open gingival embrasures in intraoral photographs: A multicentre study},
author = {Ruijie Zhang and Lang Lei and Xianglong Han and Lijun Tan and Guangzhao Guan and Zhiyi Huang and Li Mei},
year = {2027},
journal = {Journal of Dentistry},
doi = {10.1016/j.jdent.2026.107055},
url = {https://doi.org/10.1016/j.jdent.2026.107055}
}RIS
TY - JOUR TI - Deep learning–based automated detection and geometric severity assessment of open gingival embrasures in intraoral photographs: A multicentre study AU - Ruijie Zhang AU - Lang Lei AU - Xianglong Han AU - Lijun Tan AU - Guangzhao Guan AU - Zhiyi Huang AU - Li Mei PY - 2027 JO - Journal of Dentistry DO - 10.1016/j.jdent.2026.107055 UR - https://doi.org/10.1016/j.jdent.2026.107055 ER -
APA
Zhang, R., Lei, L., Han, X., Tan, L., Guan, G., Huang, Z., & Mei, L. (2027). Deep learning–based automated detection and geometric severity assessment of open gingival embrasures in intraoral photographs: A multicentre study. Journal of Dentistry. https://doi.org/10.1016/j.jdent.2026.107055
Source records
- crossref · retrieved 2026-09-25T09:13:24.289Z