Application of Deep Learning Techniques for Automated Diagnosis of Non-Syndromic Craniosynostosis Using Skull

Abstract Non-syndromic craniosynostosis (NSCS) is a disease, in which a single cranial bone suture is prematurely fused. The early intervention of the disease is associated with a favorable outcome at a later age, so appropriate screening of NSCS is essential for its clinical management. The present study aims to develop a classification and detection system of NSCS using skull X-ray images and a convolutional neural network (CNN) deep learning framework. A total of 56 NSCS cases (scaphocephaly [n = 17], trigonocephaly [n = 28], anterior plagiocephaly [n = 8], and posterior plagiocephaly [n = 3]) and 25 healthy control infants were included in the study. All the cases underwent skull X-rays and computed tomography scan for diagnosis in our institution. The lateral views obtained from the patients were retrospectively examined using a CNN framework. Our CNN model classified the 4 NSCS types and control with high accuracy (100%). All the cases were correctly classified. The proposed CNN model may offer a safe and high-sensitivity screening of NSCS and facilitate early diagnosis of the disease and better neurocognitive outcome for patients.