Automatic measurement of the Cobb angle for adolescent idiopathic scoliosis using convolutional neural network

Abstract This study proposes a convolutional neural network method for automatic vertebrae detection and Cobb angle (CA) measurement on X-ray images for scoliosis. 1021 full-length X-ray images of the whole spine of patients with adolescent idiopathic scoliosis (AIS) were used for training and segmentation. The proposed AI algorithm's results were compared with those of the manual method by six doctors using the intraclass correlation coefficient (ICC). The ICCs recorded by six doctors and AI were excellent or good, with a value of 0.973 for the major curve in the standing position. The mean error between AI and doctors was not affected by the angle size, with AI tending to measure 1.7°–2.2° smaller than that measured by the doctors. The proposed method showed a high correlation with the doctors’ measurements, regardless of the CA size, doctors’ experience, and patient posture. The proposed method showed excellent reliability, indicating that it is a promising automated method for measuring CA in patients with AIS