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.

颅缝早闭是一种颅缝过早融合的疾病，会导致婴儿正常大脑和颅骨生长出现问题。为了限制外观和功能问题的程度，需要快速诊断。本研究的目的是调查深度学习算法是否能够正确地将婴儿的头部形状分类为健康对照或以下三种颅缝早闭亚型之一；舟头畸形、三角头畸形或前斜头畸形。为了获取颅骨形状数据，在舟头畸形 (n = 76)、三角头畸形 (n = 40) 和前斜头畸形 (n = 27) 患者的常规术前预约期间拍摄了 3D 立体照片。对 3-6 个月大的健康婴儿 (n = 53) 拍摄了 3D 立体照片。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、舟头畸形患者、三角头畸形患者或前斜头畸形患者。对于深度学习网络的训练和测试，使用了分层十倍交叉验证。在测试过程中，196 张 3D 立体照片中有 195 张 (99.5%) 被正确分类。这项研究表明，基于 3D 立体照片的经过训练的深度学习算法可以高精度地区分颅缝早闭亚型和健康对照。