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For: Mohammad-rahimi H, Motamedian SR, Rohban MH, Krois J, Uribe S, Nia EM, Rokhshad R, Nadimi M, Schwendicke F. Deep Learning for Caries Detection: A Systematic Review. Journal of Dentistry 2022. [DOI: 10.1016/j.jdent.2022.104115] [Cited by in Crossref: 4] [Cited by in F6Publishing: 5] [Article Influence: 4.0] [Reference Citation Analysis]
Number Citing Articles
1 Mohammad-Rahimi H, Rokhshad R, Bencharit S, Krois J, Schwendicke F. Deep learning: A primer for dentists and dental researchers. J Dent 2023;:104430. [PMID: 36682721 DOI: 10.1016/j.jdent.2023.104430] [Reference Citation Analysis]
2 Oztekin F, Katar O, Sadak F, Yildirim M, Cakar H, Aydogan M, Ozpolat Z, Talo Yildirim T, Yildirim O, Faust O, Acharya UR. An Explainable Deep Learning Model to Prediction Dental Caries Using Panoramic Radiograph Images. Diagnostics (Basel) 2023;13. [PMID: 36673036 DOI: 10.3390/diagnostics13020226] [Reference Citation Analysis]
3 Eggmann F, Ayub JM, Conejo J, Blatz MB. Deep margin elevation-Present status and future directions. J Esthet Restor Dent 2023. [PMID: 36602272 DOI: 10.1111/jerd.13008] [Reference Citation Analysis]
4 Sadr S, Mohammad-Rahimi H, Motamedian SR, Zahedrozegar S, Motie P, Vinayahalingam S, Dianat O, Nosrat A. Deep Learning for Detection of Periapical Radiolucent Lesions: A Systematic Review and Meta-analysis of Diagnostic Test Accuracy. J Endod 2022:S0099-2399(22)00845-7. [PMID: 36563779 DOI: 10.1016/j.joen.2022.12.007] [Reference Citation Analysis]
5 Albalawi F, Alamoud KA. Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review. Applied Sciences 2022;12:11864. [DOI: 10.3390/app122211864] [Reference Citation Analysis]
6 Hsu K, Yuh DY, Lin SC, Lyu PS, Pan GX, Zhuang YC, Chang CC, Peng HH, Lee TY, Juan CH, Juan CE, Liu YJ, Juan CJ. Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography. Sci Rep 2022;12:19809. [PMID: 36396696 DOI: 10.1038/s41598-022-23901-7] [Reference Citation Analysis]
7 Barayan MA, Qawas AA, Alghamdi AS, Alkhallagi TS, Al-dabbagh RA, Aldabbagh GA, Linjawi AI. Effectiveness of Machine Learning in Assessing the Diagnostic Quality of Bitewing Radiographs. Applied Sciences 2022;12:9588. [DOI: 10.3390/app12199588] [Reference Citation Analysis]
8 Bonfanti-Gris M, Garcia-Cañas A, Alonso-Calvo R, Salido Rodriguez-Manzaneque MP, Pradies Ramiro G. Evaluation of an Artificial Intelligence web-based software to detect and classify dental structures and treatments in panoramic radiographs. J Dent 2022;126:104301. [PMID: 36150430 DOI: 10.1016/j.jdent.2022.104301] [Reference Citation Analysis]
9 Al Mokdad S, Al Houria A, Abu Talib M, Moufti M, Bouridane A, Nasir Q. Analytical Overview on Transfer Learning in Processing Dental X-rays. 2022 The 5th International Conference on Control and Computer Vision 2022. [DOI: 10.1145/3561613.3561635] [Reference Citation Analysis]
10 Mohammad-Rahimi H, Motamedian SR, Pirayesh Z, Haiat A, Zahedrozegar S, Mahmoudinia E, Rohban MH, Krois J, Lee JH, Schwendicke F. Deep learning in periodontology and oral implantology: A scoping review. J Periodontal Res 2022. [PMID: 35856183 DOI: 10.1111/jre.13037] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
11 Thurzo A, Urbanová W, Novák B, Czako L, Siebert T, Stano P, Mareková S, Fountoulaki G, Kosnáčová H, Varga I. Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis. Healthcare (Basel) 2022;10:1269. [PMID: 35885796 DOI: 10.3390/healthcare10071269] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 5.0] [Reference Citation Analysis]