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Cited by in F6Publishing
For: Jiang L, Chen D, Cao Z, Wu F, Zhu H, Zhu F. A two-stage deep learning architecture for radiographic staging of periodontal bone loss. BMC Oral Health 2022;22:106. [PMID: 35365122 DOI: 10.1186/s12903-022-02119-z] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
Number Citing Articles
1 Widyaningrum R, Candradewi I, Aji NRAS, Aulianisa R. Comparison of Multi-Label U-Net and Mask R-CNN for panoramic radiograph segmentation to detect periodontitis. Imaging Sci Dent 2022;52:383-91. [PMID: 36605859 DOI: 10.5624/isd.20220105] [Reference Citation Analysis]
2 J D, Tk L. Digital Decision Making In Dentistry: Analysis And Prediction of Periodontitis Using Machine Learning Approach. ijngc 2022. [DOI: 10.47164/ijngc.v13i3.614] [Reference Citation Analysis]
3 Kearney VP, Yansane AM, Brandon RG, Vaderhobli R, Lin GH, Hekmatian H, Deng W, Joshi N, Bhandari H, Sadat AS, White JM. A generative adversarial inpainting network to enhance prediction of periodontal clinical attachment level. J Dent 2022;:104211. [PMID: 35760207 DOI: 10.1016/j.jdent.2022.104211] [Reference Citation Analysis]
4 Joudi NAE, Othmani MB, Bourzgui F, Mahboub O, Lazaar M. Review of the role of Artificial Intelligence in dentistry: Current applications and trends. Procedia Computer Science 2022;210:173-180. [DOI: 10.1016/j.procs.2022.10.134] [Reference Citation Analysis]