ArticleEuropean spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society2025
Using ultrasound reflection coefficient index and other clinical parameters to predict the risk of progression in adolescents with idiopathic scoliosis (AIS)- a pilot study.
Article in European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo determine the association of the ultrasound reflection coefficient index and other clinical parameters to predict curve progression in children with AIS.
methodsSixty-six females (13.9 ± 1.5 years old) under observation with baseline Cobb angle (24.4 ± 10.4°) consented and participated. Besides the standard clinical procedures, all participants were scanned by an ultrasound (US) imager in a standing position. All participants had been followed, and the average follow-up visit was 7.5 ± 3.1 months. Five parameters were investigated, including the Cobb angle and the Risser sign measured from the radiographs, and the kyphotic angle (KA), the axial vertebral rotation (AVR), and the reflection coefficient (RC) index measured from the ultrasonographs. The curve was considered progressed when the Cobb angle measured on two consecutive radiographs was increased by more than 5°.
resultsAmong the 5 parameters, the KA, RC index, and Risser sign were found to be significantly associated with progression (p < 0.05). Children who had KA ≤ 38° and RC ≤ 0.06 showed higher chances of progression (62%) versus children with KA > 38° and RC > 0.06.
conclusionThis pilot study showed that curve progression is associated with the Risser sign, KA, and RC index. Other parameters and a more extensive clinical study should be combined to develop a higher-accuracy prediction model.
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