TY - JOUR
T1 - Application of Deep Learning Techniques for Automated Diagnosis of Non-Syndromic Craniosynostosis Using Skull
AU - Mizutani, Katsuhiro
AU - Miwa, Tomoru
AU - Sakamoto, Yoshiaki
AU - Toda, Masahiro
N1 - Funding Information:
This work was supported in part by grants from the Japan Society for the Promotion of Science (JSPS) (19K09536 to T.M.).
Publisher Copyright:
© 2022 Lippincott Williams and Wilkins. All rights reserved.
PY - 2022/9/1
Y1 - 2022/9/1
N2 - 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.
AB - 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.
KW - Convolutional neural network
KW - non-syndromic craniosynostosis
KW - skull X-ray
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U2 - 10.1097/SCS.0000000000008620
DO - 10.1097/SCS.0000000000008620
M3 - Article
C2 - 35261366
AN - SCOPUS:85137160939
SN - 1049-2275
VL - 33
SP - 1843
EP - 1846
JO - Journal of Craniofacial Surgery
JF - Journal of Craniofacial Surgery
IS - 6
ER -