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For: Ito S, Mine Y, Yoshimi Y, Takeda S, Tanaka A, Onishi A, Peng TY, Nakamoto T, Nagasaki T, Kakimoto N, Murayama T, Tanimoto K. Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning. Sci Rep 2022;12:221. [PMID: 34997167 DOI: 10.1038/s41598-021-04354-w] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 2.0] [Reference Citation Analysis]
Number Citing Articles
1 Bonaldi L, Pretto A, Pirri C, Uccheddu F, Fontanella CG, Stecco C. Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies. Bioengineering (Basel) 2023;10. [PMID: 36829631 DOI: 10.3390/bioengineering10020137] [Reference Citation Analysis]
2 Li M, Punithakumar K, Major PW, Le LH, Nguyen KT, Pacheco-Pereira C, Kaipatur NR, Nebbe B, Jaremko JL, Almeida FT. Temporomandibular joint segmentation in MRI images using deep learning. J Dent 2022;127:104345. [PMID: 36368120 DOI: 10.1016/j.jdent.2022.104345] [Reference Citation Analysis]
3 Okazaki S, Mine Y, Iwamoto Y, Urabe S, Mitsuhata C, Nomura R, Kakimoto N, Murayama T. Analysis of the feasibility of using deep learning for multiclass classification of dental anomalies on panoramic radiographs. Dent Mater J 2022. [PMID: 36002296 DOI: 10.4012/dmj.2022-098] [Reference Citation Analysis]
4 MacDonald D, Reitzik S. "New Normal" Radiology. Int Dent J 2022:S0020-6539(22)00104-6. [PMID: 35667883 DOI: 10.1016/j.identj.2022.05.002] [Reference Citation Analysis]