Use of artificial intelligence to recover mandibular morphology after disease

Abstract Mandibular tumors and radical oral cancer surgery often cause bone dysmorphia and defects. Most patients present with noticeable mandibular deformations, and doctors often have difficulty determining their exact mandibular morphology. In this study, a deep convolutional generative adversari...

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Autores principales:	Ye Liang, JingJing Huan, Jia-Da Li, CanHua Jiang, ChangYun Fang, YongGang Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e98f70c7f9544bc1a487203d19da9700
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spelling	oai:doaj.org-article:e98f70c7f9544bc1a487203d19da97002021-12-02T18:51:27ZUse of artificial intelligence to recover mandibular morphology after disease10.1038/s41598-020-73394-52045-2322https://doaj.org/article/e98f70c7f9544bc1a487203d19da97002020-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-73394-5https://doaj.org/toc/2045-2322Abstract Mandibular tumors and radical oral cancer surgery often cause bone dysmorphia and defects. Most patients present with noticeable mandibular deformations, and doctors often have difficulty determining their exact mandibular morphology. In this study, a deep convolutional generative adversarial network (DCGAN) called CTGAN is proposed to complete 3D mandibular cone beam computed tomography data from CT data. After extensive training, CTGAN was tested on 6 mandibular tumor cases, resulting in 3D virtual mandibular completion. We found that CTGAN can generate mandibles with different levels and rich morphology, including positional and angular changes and local patterns. The completion results are shown as tomographic images combining generated and natural areas. The 3D generated mandibles have the anatomical morphology of the real mandibles and transition smoothly to the portions without disease, showing that CTGAN constructs mandibles with the expected patient characteristics and is suitable for mandibular morphological completion. The presented modeling principles can be applied to other areas for 3D morphological completion from medical images. Clinical trial registration: This study is not a clinical trial. Patient data were only used for testing in a virtual environment. The use of the digital data used in this study was ethically approved.Ye LiangJingJing HuanJia-Da LiCanHua JiangChangYun FangYongGang LiuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ye Liang JingJing Huan Jia-Da Li CanHua Jiang ChangYun Fang YongGang Liu Use of artificial intelligence to recover mandibular morphology after disease
description	Abstract Mandibular tumors and radical oral cancer surgery often cause bone dysmorphia and defects. Most patients present with noticeable mandibular deformations, and doctors often have difficulty determining their exact mandibular morphology. In this study, a deep convolutional generative adversarial network (DCGAN) called CTGAN is proposed to complete 3D mandibular cone beam computed tomography data from CT data. After extensive training, CTGAN was tested on 6 mandibular tumor cases, resulting in 3D virtual mandibular completion. We found that CTGAN can generate mandibles with different levels and rich morphology, including positional and angular changes and local patterns. The completion results are shown as tomographic images combining generated and natural areas. The 3D generated mandibles have the anatomical morphology of the real mandibles and transition smoothly to the portions without disease, showing that CTGAN constructs mandibles with the expected patient characteristics and is suitable for mandibular morphological completion. The presented modeling principles can be applied to other areas for 3D morphological completion from medical images. Clinical trial registration: This study is not a clinical trial. Patient data were only used for testing in a virtual environment. The use of the digital data used in this study was ethically approved.
format	article
author	Ye Liang JingJing Huan Jia-Da Li CanHua Jiang ChangYun Fang YongGang Liu
author_facet	Ye Liang JingJing Huan Jia-Da Li CanHua Jiang ChangYun Fang YongGang Liu
author_sort	Ye Liang
title	Use of artificial intelligence to recover mandibular morphology after disease
title_short	Use of artificial intelligence to recover mandibular morphology after disease
title_full	Use of artificial intelligence to recover mandibular morphology after disease
title_fullStr	Use of artificial intelligence to recover mandibular morphology after disease
title_full_unstemmed	Use of artificial intelligence to recover mandibular morphology after disease
title_sort	use of artificial intelligence to recover mandibular morphology after disease
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/e98f70c7f9544bc1a487203d19da9700
work_keys_str_mv	AT yeliang useofartificialintelligencetorecovermandibularmorphologyafterdisease AT jingjinghuan useofartificialintelligencetorecovermandibularmorphologyafterdisease AT jiadali useofartificialintelligencetorecovermandibularmorphologyafterdisease AT canhuajiang useofartificialintelligencetorecovermandibularmorphologyafterdisease AT changyunfang useofartificialintelligencetorecovermandibularmorphologyafterdisease AT yonggangliu useofartificialintelligencetorecovermandibularmorphologyafterdisease
_version_	1718377420049874944

Use of artificial intelligence to recover mandibular morphology after disease

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