Photographic Cranial Shape Analysis using Deep Learning

Purpose: To determine the feasibility of using deep learning algorithms that can identify and classify types of cranial malformations, i.e., craniosynostosis and deformational plagiocephaly and brachycephaly (DPB), using top view photographs of the infant head. Method: We used 72 3D head volumes of infants with normal (13), DPB (34), and craniosynostosis (25). These volumes contain only information about the head shape and lack texture. From these 3D volumes, top-view 2D renderings were generated from different viewing angles. We generated 37 2D files were generated from each 3D volume, and we applied additional data augmentation to obtain a total of 5,254 2D images. We then used this dataset to investigate the performance of a well-known deep learning architectures for image classification, i.e., LeNet. The data were divided into training and test sets (85% and 15%, respectively with minimum one data of each class in the test set). We performed model evaluation by cross-validation. Results: The overall accuracy of the cranial shape analysis model was 87.5 +/- 5.59%. Cases with craniosynostosis were identified with 0.99 +/- 0.01 accuracy, while subjects with DPB were identified with 0.78 +/- 0.1. The accuracy of the model to identify normal cases without cranial deformation was 0.96 +/- 0.04. Conclusions: Deep learning-based methods can be used for the accurate detection, and classification of different types of conditions with head malformation using 2D photographic data. These algorithms will be packaged as a mobile health solution to make a decision support tool available at the point-of-care.