Adolescent idiopathic scoliosis (AIS) causes serious health problems when left untreated after onset. In Japan,moire images obtained from moire screening systems have been widely used for early stage detection of AIS. However, the problems of this system are the need for manual diagnosis after screening and the result classifying only two classes, normal or abnormal, which cannot provide diagnostic information essential for treating AIS. Therefore, we propose a screening system that can estimate spinal positions from a moire image using a convolutional neural network (CNN) and then automatically screening the spinal deformity from the estimated spine. For this, training dataset is generated by merging a moire image and spine positions on a radiograph. The estimated spine by CNN is evaluated for scoliosis by the proposed measuring method, which calculates the Cobb angle, a standard for scoliosis diagnosis. Results show that the proposed system has low error when compared with the published results of similar systems and the observer error of manual diagnosis. The proposed system is not only able to screen the spine as an alternative to radiography using only the moire image but also provides detailed spinal information for treatment.
Ran CHOI†, Kota WATANABE‡, Hiroaki JINGUJI‡†, Nobuyuki FUJITA‡, Yoji OGURA‡, Satoru DEMURA‡‡, Toshiaki KOTANI⁎, Kanichiro WADA⁑, Masashi MIYAZAKI⁂, Hideki SHIGEMATSU⁑⁑, Yoshimitsu AOKI† (Member)
†Keio Univ. School of Science and Technology, ‡Keio Univ. School of medicine, ‡†Tokyo Health Service Association, ‡‡Kanazawa Univ, Graduate School of Medical Science, ⁎Seirei Sakura Citizen Hospital, ⁑ Hirosaki Univ. Graduate School of Medicine, ⁂Oita Univ. School of Medicine, ⁑⁑ Nara Medical University