X-Ray based manual Cobb angle measurement is inherently time-consuming and associated with high inter- and intra-observer variability. The existing automated scoliosis measurement methods suffer from insufficient accuracy. We propose a two-step segmentation-based deep architecture to automate Cobb angle measurement for scoliosis assessment. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Wu Yujie, Namdar Khashayar, Chen Chaojun, Hosseinpour Shahob, Shroff Manohar, Doria Andrea, Khalvati Farzad

Keywords: Scoliosis, Deep Learning, Cobb Angle

Scoliosis is a deformity of the spine that affects 2-3% of the population. Currently, the reference standard for assessing scoliosis is the manual assignment of Cobb angles at the site of the curvature center using X-ray images. This process is time consuming and unreliable as it is affected by inter- and intra-observer variability. To overcome these inaccuracies, machine learning (ML) methods can be used to automate the Cobb angle measurement process. Read More

Meeting name: SPR 2023 Annual Meeting & Postgraduate Course , 2023

Authors: Chen Chaojun, Namdar Khashayar, Wu Yujie, Hosseinpour Shahob, Shroff Manohar, Doria Andrea, Khalvati Farzad

Keywords: Scoliosis, Cobb Angle, Deep Learning

Scoliosis is a complex spine deformity with direct functional and cosmetic impacts on the individual. The reference standard for assessing scoliosis severity is the Cobb angle which is measured on radiographs by human specialists, carrying interobserver variability and inaccuracy of measurements. These limitations may result in lack of timely referral for management at a time the scoliotic deformity progression can be saved from surgery. We aimed to create a machine learning (ML) model for automatic calculation of Cobb angles on 3-foot standing spine radiographs of children and adolescents with clinical suspicion of scoliosis (AIS) across two clinical scenarios (idiopathic, group 1 and congenital scoliosis, group 2). Read More

Meeting name: SPR 2024 Annual Meeting & Postgraduate Course , 2024

Authors: Stott Samantha, Wu Yujie, Hosseinpour Shahob, Chen Chaojun, Namdar Khashayar, Amirabadi Afsaneh, Shroff Manohar, Khalvati Farzad, Doria Andrea

Keywords: Radiograph, Children