For: | Gokdeniz ST, Kamburoğlu K. Artificial intelligence in dentomaxillofacial radiology. World J Radiol 2022; 14(3): 55-59 [PMID: 35432776 DOI: 10.4329/wjr.v14.i3.55] |
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URL: | https://www.wjgnet.com/1949-8470/full/v14/i3/55.htm |
Number | Citing Articles |
1 |
Kıvanç Kamburoğlu. Trends in dentomaxillofacial radiology. World Journal of Radiology 2025; 17(1): 97255 doi: 10.4329/wjr.v17.i1.97255
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2 |
Farida Abesi, Atena Sadat Jamali, Mohammad Zamani. Accuracy of artificial intelligence in the detection and segmentation of oral and maxillofacial structures using cone-beam computed tomography images: a systematic review and meta-analysis. Polish Journal of Radiology 2023; 88: 256 doi: 10.5114/pjr.2023.127624
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3 |
Nitin Rane, Saurabh Choudhary, Jayesh Rane. Towards Autonomous Healthcare: Integrating Artificial Intelligence (AI) for Personalized Medicine and Disease Prediction. SSRN Electronic Journal 2023; doi: 10.2139/ssrn.4637894
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4 |
Shunv Ying, Feng Huang, Wei Liu, Fuming He. Deep learning in the overall process of implant prosthodontics: A state‐of‐the‐art review. Clinical Implant Dentistry and Related Research 2024; 26(5): 835 doi: 10.1111/cid.13307
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5 |
Rocco Franco, Giuseppe Minervini. Digitalization, Technologies, New Approaches, and Telemedicine in Dentistry and Craniofacial/Temporomandibular Disorders. Applied Sciences 2024; 14(13): 5871 doi: 10.3390/app14135871
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6 |
Yoonji Lee, Jeong-Hye Pyeon, Sung-Hoon Han, Na Jin Kim, Won-Jong Park, Jun-Beom Park. A Comparative Study of Deep Learning and Manual Methods for Identifying Anatomical Landmarks through Cephalometry and Cone-Beam Computed Tomography: A Systematic Review and Meta-Analysis. Applied Sciences 2024; 14(16): 7342 doi: 10.3390/app14167342
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