• 대한두개안면성형외과학회지
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• 제23권1호
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• pp.4-5
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• 2022

• 대한두개안면성형외과학회지
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• 제23권2호
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• pp.51-52
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• 2022

• 대한두개안면성형외과학회지
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• 제20권1호
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• pp.1-2
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• 2019

• Archives of Plastic Surgery
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• 제42권5호
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• pp.559-566
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• 2015

Background Citation analysis is a recognized scientometric method of classifying cited articles according to the frequency of which they have been referenced. The total number of citations an article receives is considered to reflect it's significance among it's peers. Methods Until now, a bibliometric analysis has never been performed in the specialty of craniofacial anomalies and craniofacial surgery. This citation analysis generates an extensive list of the 50 most influential papers in this developing field. Journals specializing in craniofacial surgery, maxillofacial surgery, plastic surgery, neurosurgery, genetics and pediatrics were searched to demonstrate which articles have cultivated the specialty within the past 55 years. Results The results show an intriguing compilation of papers which outline the fundamental knowledge of craniofacial anomalies and the developments of surgical techniques to manage these patients. Conclusions This citation analysis provides a summation of the current most popular trends in craniofacial literature. These esteemed papers aid to direct our decision making today within this specialty.

• 대한두개안면성형외과학회지
• /
• 제23권1호
• /
• pp.1-3
• /
• 2022

• 대한두개안면성형외과학회지
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• 제23권2호
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• pp.49-50
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• 2022

• 대한두개안면성형외과학회지
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• 제20권5호
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• pp.281-283
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• 2019

• 대한두개안면성형외과학회지
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• 제23권3호
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• pp.93-94
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• 2022

• 대한두개안면성형외과학회지
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• 제21권2호
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• pp.77-79
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• 2020

• 대한두개안면성형외과학회지
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• 제22권5호
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• pp.223-231
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• 2021

The field of artificial intelligence (AI) is rapidly advancing, and AI models are increasingly applied in the medical field, especially in medical imaging, pathology, natural language processing, and biosignal analysis. On the basis of these advances, telemedicine, which allows people to receive medical services outside of hospitals or clinics, is also developing in many countries. The mechanisms of deep learning used in medical AI include convolutional neural networks, residual neural networks, and generative adversarial networks. Herein, we investigate the possibility of using these AI methods in the field of craniofacial surgery, with potential applications including craniofacial trauma, congenital anomalies, and cosmetic surgery.