%0 Journal Article
%T Semi-automatic method for pre-surgery scoliosis classification on X-ray images using Bending Asymmetry Index.
%A Yang D
%A Lee TTY
%A Lai KKL
%A Lam TP
%A Castelein RM
%A Cheng JCY
%A Zheng YP
%J Int J Comput Assist Radiol Surg
%V 0
%N 0
%D Sep 2022 9
%M 36085434
%F 3.421
%R 10.1007/s11548-022-02740-x
%X <B>OBJECTIVE: </B>Bending Asymmetry Index (BAI) has been proposed to characterize the types of scoliotic curve in three-dimensional ultrasound imaging. Scolioscan has demonstrated its validity and reliability in scoliosis assessment with manual assessment-based X-ray imaging. The objective of this study is to investigate the ultrasound-derived BAI method to X-ray imaging of scoliosis, with supplementary information provided for the pre-surgery planning.<BR><B>METHODS: </B>About 30 pre-surgery scoliosis subjects (9 males and 21 females; Cobb: 50.9 ± 19.7°, range 18°-115°) were investigated retrospectively. Each subject underwent three-posture X-ray scanning supine on a plain mattress on the same day. BAI is an indicator to distinguish structural or non-structural curves through the spine flexibility information obtained from lateral bending spinal profiles. BAI was calculated semi-automatically with manual annotation of vertebral centroids and pelvis level inclination adjustment. BAI classification was validated with the scoliotic curve type and traditional Lenke classification using side-bending Cobb angle measurement (S-Cobb).<BR><B>RESULTS: </B>82 curves from 30 pre-surgery scoliosis patients were included. The correlation coefficient was R2 = 0.730 (p < 0.05) between BAI and S-Cobb. In terms of scoliotic curve type classification, all curves were correctly classified; out of 30 subjects, 1 case was confirmed as misclassified when applying to Lenke classification earlier, thus has been adjusted.<BR><B>CONCLUSIONS: </B>BAI method has demonstrated its inter-modality versatility in X-ray imaging application. The curve type classification and the pre-surgery Lenke classification both indicated promising performances upon the exploratory dataset. A fully-automated of BAI measurement is surely an interesting direction to continue our endeavor. Deep learning on the vertebral-level segmentation should be involved in further study.