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将深度学习与 3D 体视摄影相结合进行颅缝早闭诊断。

Combining deep learning with 3D stereophotogrammetry for craniosynostosis diagnosis.

机构信息

Department of Neurosurgery, Radboudumc, Nijmegen, The Netherlands.

Radboudumc 3D Lab, Radboudumc, Nijmegen, The Netherlands.

出版信息

Sci Rep. 2020 Sep 18;10(1):15346. doi: 10.1038/s41598-020-72143-y.

Abstract

Craniosynostosis is a condition in which cranial sutures fuse prematurely, causing problems in normal brain and skull growth in infants. To limit the extent of cosmetic and functional problems, swift diagnosis is needed. The goal of this study is to investigate if a deep learning algorithm is capable of correctly classifying the head shape of infants as either healthy controls, or as one of the following three craniosynostosis subtypes; scaphocephaly, trigonocephaly or anterior plagiocephaly. In order to acquire cranial shape data, 3D stereophotographs were made during routine pre-operative appointments of scaphocephaly (n = 76), trigonocephaly (n = 40) and anterior plagiocephaly (n = 27) patients. 3D Stereophotographs of healthy infants (n = 53) were made between the age of 3-6 months. The cranial shape data was sampled and a deep learning network was used to classify the cranial shape data as either: healthy control, scaphocephaly patient, trigonocephaly patient or anterior plagiocephaly patient. For the training and testing of the deep learning network, a stratified tenfold cross validation was used. During testing 195 out of 196 3D stereophotographs (99.5%) were correctly classified. This study shows that trained deep learning algorithms, based on 3D stereophotographs, can discriminate between craniosynostosis subtypes and healthy controls with high accuracy.

摘要

颅缝早闭是一种颅缝过早融合的疾病,导致婴儿正常大脑和颅骨生长出现问题。为了限制美容和功能问题的程度,需要迅速做出诊断。本研究旨在探讨深度学习算法是否能够正确地将婴儿的头型分类为健康对照组,或以下三种颅缝早闭亚型之一:舟状头畸形、斜头畸形或额偏头畸形。为了获取颅骨形状数据,在舟状头畸形(n=76)、斜头畸形(n=40)和额偏头畸形(n=27)患者的常规术前预约期间进行了 3D 立体摄影。在 3-6 个月大时,对健康婴儿(n=53)进行了 3D 立体摄影。对颅骨形状数据进行采样,并使用深度学习网络将颅骨形状数据分类为:健康对照组、舟状头畸形患者、斜头畸形患者或额偏头畸形患者。为了训练和测试深度学习网络,使用了分层十折交叉验证。在测试中,196 张 3D 立体照片中的 195 张(99.5%)被正确分类。本研究表明,基于 3D 立体照片的训练有素的深度学习算法可以非常准确地区分颅缝早闭亚型和健康对照组。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a612/7501225/b6348e83fd35/41598_2020_72143_Fig1_HTML.jpg

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